# The PHANGS-AstroSat Atlas of Nearby Star Forming Galaxies

Hamid Hassani Dept. of Physics, University of Alberta, 4-183 CCIS, Edmonton,
Alberta, T6G 2E1, Canada  Erik Rosolowsky Dept. of Physics, University of
Alberta, 4-183 CCIS, Edmonton, Alberta, T6G 2E1, Canada  Eric W. Koch Center
for Astrophysics $\mid$ Harvard & Smithsonian, 60 Garden St., 02138 Cambridge,
MA, USA  Dept. of Physics, University of Alberta, 4-183 CCIS, Edmonton,
Alberta, T6G 2E1, Canada  Joseph Postma Dept. of Physics and Astronomy,
University of Calgary, Calgary, Alberta, Canada  Joseph Nofech Dept. of
Physics, University of Alberta, 4-183 CCIS, Edmonton, Alberta, T6G 2E1, Canada
Harrisen Corbould Dept. of Physics, University of Alberta, 4-183 CCIS,
Edmonton, Alberta, T6G 2E1, Canada  David Thilker Center for Astrophysical
Sciences, The Johns Hopkins University, Baltimore, MD USA  Adam K. Leroy
Department of Astronomy, The Ohio State University, Columbus, Ohio 43210, USA
Eva Schinnerer Max Planck Institut für Astronomie, Königstuhl 17, D-69117
Heidelberg, Germany  Francesco Belfiore INAF — Osservatorio Astrofisico di
Arcetri, Largo E. Fermi 5, I-50125, Florence, Italy  Frank Bigiel Argelander-
Institut für Astronomie, Universität Bonn, Auf dem Hügel 71, 53121 Bonn,
Germany  Médéric Boquien Instituto de Alta Investigación, Universidad de
Tarapacá, Casilla 7D, Arica, Chile  Mélanie Chevance Zentrum für Astronomie
der Universität Heidelberg, Institut für Theoretische Astrophysik, Albert-
Ueberle-Str. 2, 69120 Heidelberg  Cosmic Origins Of Life (COOL) Research DAO,
coolresearch.io  Daniel A. Dale Department of Physics & Astronomy, University
of Wyoming, Laramie, WY 82071 USA  Oleg V. Egorov Astronomisches Rechen-
Institut, Zentrum für Astronomie der Universität Heidelberg, Mönchhofstr.
12-14, D-69120 Heidelberg, Germany  Eric Emsellem European Southern
Observatory, Karl-Schwarzschild-Straße 2, 85748 Garching, Germany  Univ Lyon,
Univ Lyon1, ENS de Lyon, CNRS, Centre de Recherche Astrophysique de Lyon
UMR5574, F-69230 Saint-Genis-Laval France  Simon C. O. Glover Universität
Heidelberg, Zentrum für Astronomie, Institut für Theoretische Astrophysik,
Albert-Ueberle-Str. 2, 69120, Heidelberg, Germany  Kathryn Grasha ARC DECRA
Fellow  Research School of Astronomy and Astrophysics, Australian National
University, Canberra, ACT 2611, Australia  ARC Centre of Excellence for All
Sky Astrophysics in 3 Dimensions (ASTRO 3D), Australia  Brent Groves
International Centre for Radio Astronomy Research, University of Western
Australia, 7 Fairway, Crawley, 6009 WA, Australia  Kiana Henny Department of
Physics & Astronomy, University of Wyoming, Laramie, WY 82071 USA  Jaeyeon Kim
Kavli Institute for Particle Astrophysics & Cosmology (KIPAC), Stanford
University, CA 94305, USA  Ralf S. Klessen Universität Heidelberg, Zentrum für
Astronomie, Institut für Theoretische Astrophysik, Albert-Ueberle-Str. 2,
69120, Heidelberg, Germany.  Universität Heidelberg, Interdisziplinäres
Zentrum für Wissenschaftliches Rechnen, Im Neuenheimer Feld 205, D-69120
Heidelberg, Germany.  Kathryn Kreckel Astronomisches Rechen-Institut, Zentrum
für Astronomie der Universität Heidelberg, Mönchhofstraße 12-14, 69120
Heidelberg, Germany  J. M. Diederik Kruijssen Technical University of Munich,
School of Engineering and Design, Department of Aerospace and Geodesy, Chair
of Remote Sensing Technology, Arcisstr. 21, 80333 Munich, Germany  Cosmic
Origins Of Life (COOL) Research DAO, coolresearch.io  Janice C. Lee Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
Steward Observatory, University of Arizona, Tucson, AZ 85712, USA  Laura A.
Lopez Department of Astronomy, The Ohio State University, 140 W. 18th Ave.,
Columbus, OH 43210, USA  Center for Cosmology and AstroParticle Physics, The
Ohio State University, 191 W. Woodruff Ave., Columbus, OH 43210, USA  Justus
Neumann Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117 Heidelberg,
Germany  Hsi-An Pan Department of Physics, Tamkang University, No.151,
Yingzhuan Road, Tamsui District, New Taipei City 251301, Taiwan  Karin M.
Sandstrom Department of Astronomy & Astrophysics, University of California,
San Diego, 9500 Gilman Drive, La Jolla, CA 92093  Sumit K. Sarbadhicary
Department of Physics, The Ohio State University, Columbus, Ohio 43210, USA
Center for Cosmology & Astro-Particle Physics, The Ohio State University,
Columbus, Ohio 43210, USA  Jiayi Sun Department of Physics and Astronomy,
McMaster University, 1280 Main Street West, Hamilton, ON L8S 4M1, Canada
Canadian Institute for Theoretical Astrophysics (CITA), University of Toronto,
60 St George Street, Toronto, ON M5S 3H8, Canada  Thomas G. Williams Sub-
department of Astrophysics, Department of Physics, University of Oxford, Keble
Road, Oxford OX1 3RH, UK

(Accepted December 7, 2023)

###### Abstract

We present the Physics at High Angular resolution in Nearby GalaxieS
(PHANGS)-AstroSat atlas, which contains ultraviolet imaging of 31 nearby star-
forming galaxies captured by the Ultraviolet Imaging Telescope (UVIT) on the
AstroSat satellite. The atlas provides a homogeneous data set of far- and
near-ultraviolet maps of galaxies within a distance of 22 Mpc and a median
angular resolution of 1.4 arcseconds (corresponding to a physical scale
between 25 and 160 pc). After subtracting a uniform ultraviolet background and
accounting for Milky Way extinction, we compare our estimated flux densities
to GALEX observations, finding good agreement. We find candidate extended UV
disks around the galaxies NGC 6744 and IC 5332. We present the first
statistical measurements of the clumping of the UV emission and compare it to
the clumping of molecular gas traced with ALMA. We find that bars and spiral
arms exhibit the highest degree of clumping, and the molecular gas is even
more clumped than the FUV emission in galaxies. We investigate the variation
of the ratio of observed FUV to H$\alpha$ in different galactic environments
and kpc-sized apertures. We report that $\sim 65$% varation of the
$\log_{10}$(FUV/H$\alpha$) can be described through a combination of dust
attenuation with star formation history parameters. The PHANGS-AstroSat atlas
enhances the multi-wavelength coverage of our sample, offering a detailed
perspective on star formation. When integrated with PHANGS data sets from
ALMA, VLT-MUSE, HST and JWST, it develops our comprehensive understanding of
attenuation curves and dust attenuation in star-forming galaxies.

editorials, notices — miscellaneous — catalogs — surveys

††journal: ApJS††software: python, Astropy (Astropy Collaboration et al.,
2013, 2018, 2022), numpy (Harris et al., 2020), CCDLAB (Postma & Leahy, 2017),
spectral-cube (Ginsburg et al., 2019), and matplotlib (Hunter, 2007), ChatGPT.

##  1 Introduction

The high-mass stellar population of a galaxy plays a central role in galactic
evolution. High-mass stars, which are short-lived and must have formed
recently, change the colors of galaxies and provide the mechanical and
radiative feedback that determine the future evolution of the system (Krumholz
et al., 2014). Beyond the Local Group, our study of these young, massive stars
relies on their photospheric emission, which is directly traced in the near-
and far-ultraviolet and indirectly observed using recombination line emission
in the optical (e.g., H$\alpha$) and reprocessing from dust (Kennicutt &
Evans, 2012).

Studying ultraviolet (UV) emission from nearby extragalactic targets traces
star formation over a longer time scale (100-200 Myr) in comparison to other
typical tracers, such as recombination lines (5-20 Myr; Kennicutt & Evans,
2012). To understand the UV spectral behaviour of young stellar populations
located beyond the optical disk, both large fields-of-view (FoV) and multi-
band UV imaging are essential. The latter feature is a key to constraining the
attenuation properties along with the UV color (Decleir et al., 2019).
Moreover, because of its sensitivity to small numbers of high-mass stars, UV
emission provides a good tracer of star formation beyond optical disks
(Thilker et al., 2007), and more generally in galactic environments
characterized by low density (Lee et al., 2011) such as dwarf galaxies.
Studying short-lived massive stars in low-density outskirts of galaxies
constrains how gas may accrete into galaxies and which mechanism is
responsible for the regulation of star formation in these low-column density
environments (Gil de Paz et al., 2007a).

In the past decades, several telescopes such as the Galaxy Evolution Explorer
(GALEX) and the Neil Gehrels Swift Observatory’s Ultraviolet Optical Telescope
(UVOT) have mapped UV bands in nearby galaxies (Lee et al., 2011; Rampazzo et
al., 2017; Leroy et al., 2019). GALEX provided imaging in the near-UV (NUV;
2500 Å) and far-UV (FUV; 1500 Å) with a field of view of $\sim$1.28∘ and
angular resolution of 4 to 6″, respectively (Martin et al., 2005). On the
other hand, Swift-UVOT has three UV bands with a smaller FoV (17′) and an
angular resolution of $\sim$2.5″(Roming et al., 2005). Many insights about
nearby galaxy populations have been derived from GALEX, combined with
multiwaveband results, which have provided a rich understanding of galaxy
evolution and the impacts of star formation (e.g., Salim et al., 2007; Gil de
Paz et al., 2007b; Leroy et al., 2008; Lee et al., 2009).

The Ultraviolet Imaging Telescope (UVIT) on the AstroSat mission offers an
improvement in resolution for imaging energetic UV photons with multiple UV
bands. It extends the legacy of GALEX/SWIFT by effectively resolving the
structures of star-forming regions, thanks to its $<1.8\arcsec$ resolution
(Kumar et al., 2012; Singh et al., 2014). UVIT observes in 9 bands, covering
the range from 1480 Å to 2790 Å, with a 28′ FoV (Rahna et al., 2017). UVIT
data provide a new view of the resolved properties of faint stellar
populations beyond the optical disk of galaxies and offer detailed additional
information on the UV colour and properties of the underlying stellar
population.

The UV filter set of the AstroSat UVIT instrument consists of one narrow-band
filter (N279N), three wide-band filters (F148W, F154W, and N242W), and five
medium-band filters (F169M, F172M, N219M, N245M, and N263M), where filter
names follow the Hubble Space Telescope convention that the three digit number
represents the central wavelength in nm. These filters capture photospheric
emission from stellar populations with transmission curves in Figure 1 (Leahy
et al., 2022a, b). The data from AstroSat UVIT can be utilized to discern and
model both the age and mass of young stellar populations, ranging from
approximately $5-200$ Myr and with masses greater than $10^{3-4}M_{\odot}$
(e.g.; Ujjwal et al., 2022). Many of these populations are located beyond
$R_{25}$, i.e. the radius at which the surface brightness falls below 25
magnitudes per square arcsecond in the B-band.

Sensitive, wide-field UV observations are even more powerful when combined
with probes of the different phases of the ISM and stellar populations. The
star formation process, as traced by the NUV and FUV, offers valuable insight
when aligned with other tracers of star formation phases. CO observations,
which trace molecular gas, identify dust-shrouded star-forming regions, areas
where UV photons can quickly destroy clumps. Observations of ionized gas,
captured through hydrogen recombination lines, showcase the re-emission
stemming from the Extreme UV (EUV) of the highest mass stars.

Integrating the UV observations from nearby galaxies with a spatial resolution
below 200 pc can enhance the comprehensive understanding advanced by projects
like the Physics at High Angular resolution in Nearby GalaxieS (PHANGS)
collaboration. The PHANGS-ALMA survey (Leroy et al., 2021a) yielded $\sim
1\arcsec$ resolution observations of CO(2-1) emission from molecular clouds in
90 nearby galaxies. Similarly, PHANGS-MUSE used VLT/MUSE observations to map
out optical emission line from 19 of the 90 ALMA targets, also at $\lesssim
1\arcsec$ resolution. The PHANGS-AstroSat provides an essential complement to
the UV imaging obtained by the PHANGS-HST survey (Lee et al., 2022a) which
only probes to $\sim$2500Å, but achieves an angular resolution of 0$\farcs$08
over the smaller fields-of-view of the WFC3 camera. In this work, we compare
the new UV data to these other tracers of the star formation process to
understand how galaxies evolve at these arcsecond scales.

One of the main drivers of galaxy evolution is the relationship between gas
and star formation called the Kennicutt-Schmidt (KS) relation (Kennicutt,
1998), which presents a tight correlation between the surface densities of gas
and star formation on large scales ($>1~{}\mathrm{kpc}$) in galaxies. However,
several studies indicate that this relation operates on kpc scales and it may
break down at smaller scales (e.g. Bigiel et al., 2008; Kruijssen et al.,
2019). While these results typically rely on recombination line tracers of
star formation, AstroSat imaging can play a key role in estimating the link
between star formation and the amount of molecular gas over longer timescales.
UV observation can make progress even in the outskirts of galaxies, where the
fraction of molecular relative to neutral gas is low. However, we should note
that simulations such as those by Khoperskov & Vasiliev (2017) have shown that
the relation between UV-based star formation rates and the total amount of gas
at 50-pc resolution is unclear, and observations suggest that the KS relation
prescription cannot be extrapolated to the low-density environments of outer
galaxy disks (Dessauges-Zavadsky et al., 2014). These findings underscore the
need for high-resolution investigations of UV-based star formation processes
beyond the optical disks of galaxies.

The relationship between UV and H$\alpha$ luminosities provides valuable
information on dust content, the age of stellar populations, and the
metallicity of galaxies. Previous studies (e.g., Lee et al., 2009, 2011) have
attempted to uncover the details of the UV-H$\alpha$ relation with a focus on
understanding recipes for the integrated star formation rate in galaxies.
However, the dependence of this relation on the actual amount of molecular gas
and dust reddening remains unclear. Furthermore, there is a lack of detailed
information on how the ratio changes across different environments within
galaxies, such as bars, centres, and spiral arms at small scales. The ionized
gas traced by H$\alpha$ and observed through MUSE/VLT observations with the
PHANGS-MUSE survey offers a new perspective on recent star formation up to 10
million years ago as well as dust reddening (Emsellem et al., 2022). When
combined with the new UVIT observations, we will be able to directly relate
changing UV-to-H$\alpha$ to the local environment in these galaxies.

In this paper, we present a homogeneous data set of UV images of 31 nearby
galaxies observed by AstroSat in the wavelength range of 1480 Å to 2790 Å. Our
main focus is to reduce and assess the quality of the data set and compare it
with previous low-resolution observations. Additionally, we study the
variation of the UV and molecular gas clumpiness in different morphological
regions of galaxies. By comparing the FUV to H$\alpha$ flux in different
galaxies, we aim to provide predictive models for the ratio in different
environments. This work is expected to contribute to the interpretation of
different tracers of star formation at high resolution ($<$200 pc) and provide
a quantitative framework for the relation between UV and H$\alpha$ luminosity.

In Section 2.1, we present our sample selection strategies and then the
observations. We focus on the reduction and quality assessment of our data in
Section 3. Then, we present two new XUV disk candidates in Section 4 and
provide details about the clumping of UV and CO emission at high resolution
($\lesssim 1.5^{\prime\prime}$) in Section 5. Finally, we compare the
luminosity of FUV to H$\alpha$ emission to show how these two star formation
tracers vary in different morphological parts of galaxies and how their ratio
depends on dust attenuation, star formation history parameters, and the
equivalent width (EW) of the H$\alpha$ emission (Section 6).

##  2 Data

###  2.1 Sample Selection

To maximize the opportunity for multiwavelength science, we selected galaxies
that have been observed as part of the PHANGS survey for observation using
AstroSat. The primary PHANGS sample consists of 90 nearby galaxies observed
with the Atacama Large Millimeter/submillimeter Array (ALMA, Leroy et al.,
2021b) in molecular line emission. These targets are selected to be nearby
($D<22~{}\mathrm{Mpc}$) so a high efficiency observation with ALMA yields high
quality maps of the molecular interstellar medium with $\sim 1^{\prime\prime}$
resolution. The proximity of the targets means that the $1^{\prime\prime}$
resolution translates into small physical scales, providing a robust census of
star-forming galaxies at $\sim 100$ pc linear resolution, which matches the
typical scale heights of galaxies (Sun et al., 2020) and the size of giant
molecular clouds (Rosolowsky et al., 2021). The PHANGS targets are relatively
massive with ($10^{9.75}<M_{\star}/M_{\odot}<10^{11.0}$,) and mostly lie on
the star forming main sequence, with
SFR/$M_{\star}>10^{-11}~{}\mathrm{yr}^{-1}$. The sample was selected with a
constraint to avoid highly inclined systems, ensuring that $i<75^{\circ}$.
Table 3 presents the 31 galaxies in the PHANGS-AstroSat sample and their
properties, adopted from Leroy et al. (2021b).

###  2.2 Observations

The AstroSat satellite mission provides observations from four instruments:
the Large Area X-ray Proportional Counter (LAXPC), the Cadmium Zinc Telluride
Imager (CZTI), the Soft X-ray Telescope (SXT), and the Ultraviolet Imaging
Telescope (UVIT; Kumar et al., 2012), which is the primary instrument used in
this study. The main goal of UVIT is imaging in far-ultraviolet (FUV;
1300$-$1800 Å) and near-ultraviolet (NUV; 2000$-$3000 Å) with a field of view
of $\sim$28$\arcmin$. We observed 11 of the 31 selected galaxies under
programs T03-032, T03-033, A05-022, A07-027, A08-003, and A10-021. The PHANGS-
AstroSat observations adopted a uniform strategy for the primary sample,
requesting 3.6 ks of time on each target in the F148W filter. However,
instrumental fluctuations and orbital accessibility limit the actual time
acquired to $1-3$ ks. We supplement these observations with additional
archival data for 20 additional PHANGS targets, identified from the AstroSat
Archive111https://astrobrowse.issdc.gov.in/astro_archive/archive/Home.jsp. As
the supplementary data come from various observing programs, they use
different observational strategies and exposures. Archival observations
feature a variety of adopted filters and exposure times, some of which may be
longer than those proposed for our observations. For instance, NGC 4476 has an
exposure time that is 10 times longer than most of the sample. During UVIT
observations, AstroSat consistently records data from other instruments within
its focal plane. While we do not present these data in this work, they are
accessible directly from the archive.

The combination of archival and dedicated observations lead to heterogeneous
data across the 31 galaxies. Table 1 summarizes the available AstroSat
observations in this atlas, including the observation IDs, dates of
observations, and the observed filter set as illustrated in Figure 1. This
table includes allocated projects led by our team, as well as data sets
retrieved from the archive. Table 2 summarizes the exposure times for the
different galaxies across the different filter sets. We describe the data
processing for UVIT in Section 3.

Table 1: PHANGS-AstroSat Galaxies

Galaxy | Observation ID | PI∗ | Observation Date | Filter(s)
---|---|---|---|---
IC5332 | A07_027T02_9000003258 | rosolowsky | 26-Oct-2019 | F148W
| A07_027T02_9000003640 | rosolowsky | 4-May-2020 | F148W
NGC0253 | G0685_010T01_9000001672 | jmurthy | 08-Nov-2017 | F169M,N245M
| G0685_031T01_9000001702 | askpati | 19-Nov-2017 | F169M,N219M,N263M
NGC0300 | G05_235T01_9000000590 | hutchingsj | 11-Aug-2016 | F148W,F154W,F169M,F172M,N219M,N245M,N263M
NGC0628 | A04_209T01_9000002378 | carobert | 20-Sep-2018 | F154W,F172M
| G06_151T01_9000000836 | askpati | 29-Nov-2016 | F148W,F154W,F169M,F172M,N242W,N219M,N245M,N263M,N279N
NGC1097 | A10_021T01_9000004044 | rosolowsky | 02-Dec-2020 | F148W
NGC1300 | A07_027T05_9000003506 | rosolowsky | 16-Feb-2020 | F148W
NGC1317 | A04_164T01_9000001770 | nilkanth | 14-Dec-2017 | F148W,F154W,F169M,F172M,N219M
NGC1365 | A02_006T01_9000000776 | gulabd | 08-Nov-2016 | F169M,N279N
| A02_006T01_9000000802 | gulabd | 17-Nov-2016 | F148W,F169M,N279N
| A02_006T01_9000000934 | gulabd | 28-Dec-2016 | F169M,N279N
| G07_057T02_9000001504 | stalin | 31-Aug-2017 | F148W,F172M,N219M,N263M
NGC1385 | A07_027T06_9000003508 | rosolowsky | 16-Feb-2020 | F148W
NGC1433 | G07_066T01_9000001510 | swarna | 01-Sep-2017 | F154W,F169M,N219M,N245M,N263M,N279N
NGC1512 | G06_135T01_9000000908 | swarna | 21-Dec-2016 | F154W,N245M,N263M
| G07_068T01_9000001502 | kanak | 30-Aug-2017 | F154W,N242W
NGC1546 | A07_010T11_9000003240 | rrampazzo | 17-Oct-2019 | F148W
NGC1566 | G06_087T01_9000000926 | stalin | 26-Dec-2016 | F148W,F172M,N219M,N263M
| T02_085T01_9000002296 | gulabd | 11-Aug-2018 | F154W
| T03_020T01_9000002444 | gulabd | 22-Oct-2018 | F154W
NGC2090 | A05_155T02_9000003200 | mousumi | 24-Sep-2019 | F148W
NGC2835 | T03_032T01_9000002564 | rosolowsky | 14-Dec-2018 | F148W
NGC2903 | G08_031T03_9000001972 | askpati | 12-Mar-2018 | F148W,F169M,N219M,N263M
NGC3351 | T03_034T01_9000002500 | rosolowsky | 10-Nov-2018 | F148W
NGC3621 | G08_083T03_9000002022 | stalin | 07-Apr-2018 | F148W,F172M
NGC3627 | T03_033T01_9000002568 | rosolowsky | 15-Dec-2018 | F148W
NGC4254 | A08_003T04_9000003634 | hutchingsj | 02-May-2020 | F148W
NGC4298 | A10_021T04_9000004116 | rosolowsky | 17-Jan-2021 | F148W
NGC4321 | A08_003T05_9000003426 | hutchingsj | 11-Jan-2020 | F154W
NGC4476 | G06_051T01_9000000972 | pcote_nrc | 22-Jan-2017 | F154W,N242W
NGC4535 | A10_021T06_9000004338 | rosolowsky | 27-April-2021 | F148W
NGC4571 | G06_016T01_9000001052 | kanak | 25-Feb-2017 | F154W,N263M
NGC4579 | A08_003T09_9000003644 | hutchingsj | 05-May-2020 | F154W
NGC4654 | A07_027T12_9000003664 | rosolowsky | 13-May-2020 | F148W
| A08_003T08_9000003638 | hutchingsj | 03-May-2020 | F154W
NGC5128 | G08_023T01_9000001978 | sreekumar | 15-Mar-2018 | F148W,N219M,N245M,N279N
NGC6744 | A05_022T09_9000003058 | rosolowsky | 24-July-2019 | F148W
| A10_021T10_9000004210 | rosolowsky | 26-Feb-2021 | F148W
NGC7496 | A07_027T16_9000003222 | rosolowsky | 05-Oct-2019 | F148W
| A07_027T16_9000003642 | rosolowsky | 05-May-2020 | F148W
| A07_027T16_9000003666 | rosolowsky | 14-May-2020 | F148W
NGC7793 | G06_024T01_9000000780 | annapurni | 10-Nov-2016 | F148W,N242W

Note. — ∗ The PIs listed here are directly read from the AstroSat Archive
Search system. The ‘Filter(s)’ column shows all available filters as presented
on the AstroSat Archive Search; we do not necessarily utilize calibrated data
for every single listed filter for each galaxy.

Table 2: PHANGS-AstroSat Exposures

Galaxy | F148W | F154W | F172M | F169M | N242W | N245M | N263M | N219M | N279N
---|---|---|---|---|---|---|---|---|---
| (ks) | (ks) | (ks) | (ks) | (ks) | (ks) | (ks) | (ks) | (ks)
IC5332 | 2.28∗ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC0253 |  $\cdots$ |  $\cdots$ |  $\cdots$ | 12.29 |  $\cdots$ | 12.40 | 3.02 | 4.57 |  $\cdots$
NGC0300 | 12.84 | 1.65 | 0.95 | 6.92 |  $\cdots$ | 2.74 | 2.52 | 0.32 |  $\cdots$
NGC0628 | 1.82 | 4.88 | 19.12 | 2.42 | 0.43 | 1.32 | 2.08 | 1.36 | 4.65
NGC1097 | 1.75∗ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC1300 | 3.44∗ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC1317 | 0.83 | 1.01 | 0.87 | 1.41 |  $\cdots$ |  $\cdots$ |  $\cdots$ | 4.32 |  $\cdots$
NGC1365 | 2.10 |  $\cdots$ | 6.59 | 46.03 |  $\cdots$ |  $\cdots$ | 2.07 | 6.53 | 47.39
NGC1385 | 1.79∗ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC1433 |  $\cdots$ | 3.14 |  $\cdots$ | 3.17 |  $\cdots$ | 1.67 | 0.91 | 3.20 | 1.25
NGC1512 |  $\cdots$ | 5.80 |  $\cdots$ |  $\cdots$ | 3.70 | 1.02 | 1.20 |  $\cdots$ |  $\cdots$
NGC1546 | 6.65 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC1566 | 2.94 | 3.38 | 1.34 |  $\cdots$ |  $\cdots$ |  $\cdots$ | 2.96 | 1.37 |  $\cdots$
NGC2090 | 6.43 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC2835 | 3.41∗ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC2903 | 3.50 |  $\cdots$ |  $\cdots$ | 4.32 |  $\cdots$ |  $\cdots$ | 3.35 | 4.61 |  $\cdots$
NGC3351 | 2.64∗ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC3621 | 2.10 |  $\cdots$ | 6.37 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC3627 | 3.17∗ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC4254 |  $\cdots$ | 7.60 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC4298 | 2.92∗ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC4321 |  $\cdots$ | 7.63 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC4476 |  $\cdots$ | 35.88 |  $\cdots$ |  $\cdots$ | 35.21 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC4535 | 1.21∗ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC4571 |  $\cdots$ | 9.58 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ | 9.65 |  $\cdots$ |  $\cdots$
NGC4579 |  $\cdots$ | 7.59 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC4654 | 3.52∗ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC5128 | 23.31 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ | 4.75 |  $\cdots$ | 10.53 | 8.00
NGC6744 | 3.45∗ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC7496 | 6.16∗ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
NGC7793 | 7.53 |  $\cdots$ |  $\cdots$ |  $\cdots$ | 8.10 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$

Note. — ∗ Observed as a part of the PHANGS-AstroSat request.

Figure 1: Filters used in the AstroSat UVIT observations, corrected for in-
orbit calibrations. Blue lines show the effective area of FUV bands, whereas
the pink lines are the NUV bands. Table 3: Adopted Galaxy Properties

Galaxy | SFR | Stellar Mass | Distance | Incl. | PA
---|---|---|---|---|---
| (M⊙ yr-1 ) | ($10^{10}M_{\odot}$) | (Mpc) | (∘) | (∘)
IC5332 | 0.47 | 0.41 | 9.01 | 26.9 | 74.4
NGC0253 | 4.34 | 5.0 | 3.7 | 75.0 | 52.48
NGC0300 | 0.18 | 0.15 | 2.09 | 39.8 | 114.3
NGC0628 | 2.19 | 1.75 | 9.84 | 8.9 | 20.7
NGC1097∗ | 5.75 | 4.74 | 13.58 | 48.6 | 122.4
NGC1300 | 4.14 | 1.17 | 18.99 | 31.8 | 278.0
NGC1317 | 4.17 | 0.48 | 19.11 | 23.2 | 221.5
NGC1365∗ | 9.78 | 16.9 | 19.57 | 55.4 | 201.1
NGC1385 | 0.95 | 2.09 | 17.22 | 44.0 | 181.3
NGC1433 | 7.34 | 1.13 | 18.63 | 28.6 | 199.7
NGC1512 | 5.16 | 1.28 | 18.83 | 42.5 | 261.9
NGC1546 | 2.24 | 0.83 | 17.69 | 70.3 | 147.8
NGC1566∗ | 6.09 | 4.54 | 17.69 | 29.5 | 214.7
NGC2090 | 1.09 | 0.41 | 11.75 | 64.5 | 192.46
NGC2835 | 1.0 | 1.24 | 12.22 | 41.3 | 1.0
NGC2903 | 4.3 | 3.08 | 10.0 | 66.8 | 203.7
NGC3351 | 2.3 | 1.32 | 9.96 | 45.1 | 193.2
NGC3621∗ | 1.14 | 0.99 | 7.06 | 65.8 | 343.8
NGC3627∗ | 6.81 | 3.84 | 11.32 | 57.3 | 173.1
NGC4254 | 2.66 | 3.07 | 13.1 | 34.4 | 68.1
NGC4298 | 1.05 | 0.46 | 14.92 | 59.2 | 313.9
NGC4321 | 5.56 | 3.56 | 15.21 | 38.5 | 156.2
NGC4476 | 0.65 | 0.04 | 17.54 | 60.14 | 27.38
NGC4535 | 3.4 | 2.16 | 15.77 | 44.7 | 179.7
NGC4571 | 1.23 | 0.29 | 14.9 | 32.7 | 217.5
NGC4579∗ | 13.99 | 2.17 | 21.0 | 40.22 | 91.3
NGC4654 | 3.69 | 3.79 | 21.98 | 55.6 | 123.2
NGC5128 | 9.38 | 1.23 | 3.69 | 45.33 | 32.17
NGC6744 | 5.29 | 2.41 | 9.39 | 52.7 | 14.0
NGC7496∗ | 0.99 | 2.26 | 18.72 | 35.9 | 193.7
NGC7793 | 0.23 | 0.27 | 3.62 | 50.0 | 290.0

Note. — ∗ host an AGN based on Véron-Cetty & Véron (2010). Properties are
taken from Leroy et al. (2021b).

###  2.3 Ancillary Data

Beside AstroSat FUV and NUV observations, we also use several data sets from
the literature and from the PHANGS-ALMA and PHANGS-MUSE surveys, which we
summarize below.

####  2.3.1 PHANGS-ALMA

We used CO (2-1) emission line maps from the PHANGS–ALMA project (Leroy et
al., 2021b). The emission lines were observed at the rest frequency of
$\nu=230.538$ GHz (Band 6), where the combination of receiver sensitivity and
atmospheric transparency makes ALMA most efficient for $1^{\prime\prime}$
scale mapping of molecular gas. The ALMA data set provides a unique,
$\sim$1″(100 pc at 20 Mpc) view of nearby main-sequence galaxies at GMCs
scale. The median of 1$\sigma$ noise in the cube of all targets is estimated
to be 6.2 mJy beam-1 at native resolution (corresponding to a brightness
temperature of $\sim 0.17$ K). The velocity resolution of 2.5 km s-1 is chosen
to improve the signal-to-noise ratio and quality of the deconvolution process.
These observations were conducted using a bandwidth of 937.5 MHz with a
channel width of $\Delta\nu=244$ kHz. In contrast to UV observations from
AstroSat, ALMA maps only cover the infrared-bright portion of the inner disk
($R_{\mathrm{gal}}=5-6\,$kpc) of our targets. Final data products are released
using two different signal-selection masks, both indicating the likely
location of real CO emission inside each cube. In this study, we used high
completeness “broad” mask which is better for the detection of diffuse
emission from the galaxy. The broad mask is built by convolving the cube into
several lower resolution cubes and then a mask was created. Along with zero-
moment maps that we used in this study, the uncertainty maps are also provided
based on a three-dimensional noise model (Leroy et al., 2021b, , their section
7.2).

####  2.3.2 PHANGS-MUSE

We also utilized products from PHANGS-MUSE (program IDs: 1100.B-0651/PI: E.
Schinnerer; 095.C-0473/PI: G. Blanc; and 094.C-0623/PI: K. Kreckel). This
sample encompasses 19 nearby star-forming spiral galaxies, as described by
Emsellem et al. (2022). Out of these, 15 targets are also included in the
PHANGS-AstroSat observations. The entire program took a total telescope time
of $\sim$ 172 hours and each target has been observed with 3 to 15 pointings
to cover the disk of the galaxy. Observations were conducted using MUSE wide-
field mode (WFM; FoV = 1′), using the nominal (non-extended) wavelength from
4800 to 9300 Å. The spectral resolution varies with wavelength but is
typically around 2.75 Å (Full Width at Half Maximum, FWHM). The median Point
Spread Function of the sample has a 0.69″ FWHM at 6483.5 Å. We use the
emission line maps from Emsellem et al. (2022), which are created through the
MUSE Data Analysis Pipeline (DAP) that fits each emission line along with the
properties of the underlying stellar continuum. In this work, we use the
H$\alpha$ and H$\beta$ emission lines as well as an estimate of the light-
weighted stellar population ages determined from fits to the stellar spectra
(Emsellem et al., 2022).

####  2.3.3 Literature data sets

Finally, we use the infrared and UV maps of our targets provided by Leroy et
al. (2019), who supply an atlas of WISE (3.4$\mu$m to 22$\mu$m) and GALEX UV
maps of $\sim$ 15000 nearby galaxies (D $\leq$ 50 Mpc). These GALEX maps are
background-subtracted for UV background emission and corrected for foreground
Galactic extinction. In our quality assessment analysis, we employ GALEX maps
which are convolved to an angular resolution of 7$\farcs$5, in accordance with
the (Leroy et al., 2019) dataset.

Figure 2: AstroSat UVIT F148W (FUV) map (blue) and N263M (NUV) (yellow) of NGC
1566 using an asinh brightness scale. The dynamical range of the N263M image
is same as for the F148W map but normalized to the F148W/N263M central
wavelength ratio for consistent cross-filter comparison. We show circles with
radii of $R_{25}$ (dashed line) and 1.5 $R_{25}$ (solid line). The
classification of spiral arms environments (green) is shown on the map. We
show the PSF FWHM in the bottom left corner of the map as a red dot and a
physical scale of 1kpc in bottom right corner. In addition, we plotted the
1-kpc size hexagonal apertures that we use to measure several quantities in
sections 5 and 6. We overlaid the MUSE/VLT and ALMA footprints in orange and
red colors respectively.

##  3 AstroSat Data Processing

The Level-1 data products were generated using v2.1 and v2.2 of the AstroSat
pipeline depending from Ravishankar et al. (2021) on the data set, and the
Level-2 data were processed with the CCDlab software package (Postma & Leahy,
2017). The pipeline accounts for various instrumental effects such as
spacecraft drifts, jitter, and thermal effects. A bright white dwarf (HZ4) was
adopted as a photometric calibration source. A detailed set of criteria for
selecting this source is discussed by Tandon et al. (2017). The intensity of
images are converted from observed counts/s (CPS) to flux density per 0.42″
size pixel ($F_{\lambda}$) using the updated in-orbit calibrations, following:

$F_{\lambda}(\mathrm{ergs}\,\mathrm{s}^{-1}\,\mathrm{cm}^{-2}\,\mbox{\AA}^{-1})=\mathrm{CPS}\times\mathrm{UC},$
(1)

where UC is the unit conversion factor derived from the zero-point (ZP)
magnitude in the AB system as (Tandon et al., 2020, their table 3):

$\mathrm{UC}=10^{-0.4\frac{(\mathrm{ZP}+2.407)}{\lambda_{\mathrm{mean}}^{2}}}.$
(2)

The $\lambda_{\mathrm{mean}}$ corresponds to the mean wavelengths of the UVIT
filters in Å. The uncertainties in $F_{\lambda}$ for different filters are due
to the Poisson noise of the photon-counter detector as well as to
uncertainties in the UC factors (Subramaniam et al., 2016). In this study, we
only propagate the uncertainty in CPS as the UC errors are taken to be small.
These uncertainties yield $5\%$ uncertainty in the flux density for each
filter (Singh et al., 2021). We align each image along the cardinal directions
by reprojecting with the reproject python
package222https://reproject.readthedocs.io/en/stable/. Because the Point
Spread Function (PSF) is minimally-sampled, we found that the adaptive
resampling method was sufficient to maintain flux conservation in the
reprojected image. Other interpolation and flux conserving methods produced a
Moiré artifact pattern in the reprojected image, which was also noted by Clark
et al. (2018), where they increased the pixel size in the reprojected image to
avoid this artifact. Using the adaptive resampling method in reproject, we can
maintain the same pixel scales as the original images. The final images have a
pixel size of $0.42\arcsec$. As an example, we show a large panel of NGC 1566
FUV (F148W) map with environmental masks adopted from Querejeta et al. (2021)
in different colors and 1-kpc size hexagonal apertures in Fig 2. We show the
rest of PHANGS-AstroSat targets at FUV band in Figures 3 and 4 with contours
from CO(2-1) molecular gas. In this study, we refer to FUV data specifically
as data from the F148W band, which is the most available filter. Whenever the
F148W band data is not available, we use other bands. Most galaxies have
observations in the F148W filter, except NGC 1433, NGC 1512, NGC 4321, NGC
4571, NGC 4476, NGC 4579, and NGC 4254 which are in the F154W filter. In the
case of NGC 0253, the only FUV band available is F169M.

Figure 3: Maps of PHANGS-AstroSat galaxies in the FUV band (blue) and any
available NUV band (yellow) on an asinh brightness scale. The dynamical range
of NUV is same as FUV map but normalized to FUV/NUV central wavelength ratio
for consistent cross-filter comparison. Red contours are from ALMA CO(2-1)
data from 1 to 10 K km s-1. Both FUV and CO data are at a common 1.8$\arcsec$
resolution. Galaxies vary in angular and physical size. A radius of $R_{25}$
is shown as a dashed-line circle. The white line on the top left of maps have
a length of 1 kpc. We overlay the MUSE/VLT footprints with an orange color.
Figure 4: Fig. 3 continued. In the case of NGC 0300, NGC 5128, NGC 0253, and
NGC 7793 we use lower resolution ALMA CO data (TP and 7-m; $\sim 8\arcsec$)
with contours between 0.75 to 10, 5 to 100, 5 to 100, and 1 to 10 K km s-1
respectively.

###  3.1 Background subtraction

Figure 5: Radial profiles of FUV (blue) and CO (orange) emissions for IC5332
(left) and NGC 1566 (right). The FUV radial profiles are presented before
background subtraction and correction for Milky Way extinction. The black
vertical line indicates the position of $R_{25}$ in kpc, while the gray line
represents the maximum length of the bar, as reported by Herrera-Endoqui et
al. (2015), which we have excluded from the radial profiles. The solid lines
show the exponential fits to the radial profiles as described in the text.

FUV imaging shows a near-constant flux of emission from “blank” sky, which we
refer to as the background even though much of this emission may be from
Galactic and solar system sources (Kulkarni, 2022). Taking advantage of the
large field of view (FoV) of AstroSat observations, we have a radial profile
of each object that extends beyond $\sim$30 kpc galactocentric distance for
most of the targets. We estimate the background by assuming the galaxy
emission profile is an exponential disk (e.g., Regan et al., 2001) and include
a constant background term:

$S_{\lambda}=S_{0}\,e^{(-R_{\mathrm{gal}}/\gamma)}+B_{\lambda},$ (3)

where $S_{\lambda}$ is the emission at different bands, $S_{0}$ is the surface
brightness at galactocentric radius $R_{\mathrm{gal}}=0$, $\gamma$ is the
scale length, and $B_{\lambda}$ is the background value. We estimate
background emission ($B_{\lambda}$) for all UV bands of AstroSat. The
calibrated units of FUV and NUV maps are in erg s-1 cm-2 Å-1 pix-1, but we
present maps scaled to units of MJy sr-1 to facilitate comparison to
multiwavelength data. For comparison with the UV emission, we apply the same
exponential model to the CO emission, fixing $B=0$, where we use the CO “broad
mask” integrated intensity maps from Leroy et al. (2021b). This fit to the
molecular line emission derives its scale-length to compare it with the FUV
scale-length. We return to the comparison of the FUV-CO scale-lengths in more
detail in Section 4 but here the primary use is to set the background level
for the UV emission.

Table 4: Radial profile properties of galaxies.

Galaxy | $E(B-V)$ | $B_{\mathrm{FUV}}$ | ${\gamma}_{\mathrm{FUV}}$ | ${\gamma}_{\mathrm{CO}}$
---|---|---|---|---
| (mag) | ($10^{-18}$ erg/s/cm2/Å) | (kpc) | (kpc)
IC5332 | 0.02 | 0.07 | $4.3\pm 0.2$ | $1.1\pm 0.1$
NGC0300* | 0.01 | 0.08 |  $\cdots$ | $1.4\pm 0.5$
NGC0628 | 0.07 | 0.07 | $5.2\pm 0.2$ | $1.9\pm 0.2$
NGC1097 | 0.03 | 0.06 | $6.5\pm 0.7$ | $1.9\pm 0.3$
NGC1300 | 0.03 | 0.08 | $4.6\pm 0.3$ | $2.0\pm 0.6$
NGC1317 | 0.02 | 0.08 | $0.8\pm 0.1$ | $0.6\pm 0.1$
NGC1365 | 0.02 | 0.05 | $8.3\pm 0.4$ |  $\cdots$
NGC1385 | 0.02 | 0.09 | $1.5\pm 0.1$ | $1.2\pm 0.1$
NGC1546 | 0.01 | 0.08 | $1.8\pm 0.1$ |  $\cdots$
NGC1566 | 0.01 | 0.09 | $4.7\pm 0.2$ | $2.2\pm 0.2$
NGC2090 | 0.04 | 0.15 | $5.6\pm 0.4$ | $1.9\pm 0.6$
NGC2835 | 0.1 | 0.12 | $5.0\pm 0.5$ | $2.1\pm 0.3$
NGC2903 | 0.03 | 0.07 | $4.0\pm 0.2$ | $1.6\pm 0.1$
NGC3351 | 0.03 | 0.08 | $2.7\pm 0.1$ | $1.5\pm 0.3$
NGC3621* | 0.08 | 0.12 |  $\cdots$ | $1.8\pm 0.2$
NGC3627 | 0.04 | 0.07 | $1.8\pm 0.1$ | $1.9\pm 0.2$
NGC4298 | 0.04 | 0.07 | $1.9\pm 0.1$ | $1.3\pm 0.2$
NGC4535 | 0.02 | 0.06 | $4.0\pm 0.2$ | $2.7\pm 0.5$
NGC4654 | 0.03 | 0.07 | $3.6\pm 0.1$ | $2.5\pm 0.1$
NGC5128* | 0.1 | 0.18 |  $\cdots$ | $0.8\pm 0.0$
NGC6744* | 0.04 | 0.13 |  $\cdots$ | $3.6\pm 0.4$
NGC7793 | 0.02 | 0.05 | $1.4\pm 0.1$ | $1.2\pm 0.1$
NGC7496 | 0.01 | 0.07 | $1.7\pm 0.1$ | $0.9\pm 0.1$

Note. — Second column: foreground color excess from Schlegel et al. (1998).
Third column: background emission at F148W. Fourth column: scale length from
F148W band. Fifth column: scale length from CO.

* The background ($B_{\mathrm{FUV}}$) is obtained from “mean-level” method for these targets, described in Section 3.1 and no scale length is determined.

To minimize the impact of the FUV bright central bump commonly found in
galaxies, we exclude the bar region from the radial profile by using a radius
of $1.3\times R_{\mathrm{Bar}}$, where $R_{\mathrm{Bar}}$ is adopted from
Herrera-Endoqui et al. (2015) (see also Querejeta et al., 2021). For NGC 4535
and NGC 6744, we adopt bar sizes of 5.1 and 4 kpc, respectively. We determine
the radial profiles by averaging the flux in annuli with widths of 500 pc from
the center of the galaxy up to a maximum galactocentric distance of $<50$ kpc.
This maximum distance is chosen through visual inspection of different
galaxies to exclude any bright nearby sources. Figure 5 presents the radial
profiles of AstroSat observations in the FUV band for IC 5332 and NGC 1566.
The remaining radial profiles are presented in Appendix A. In the case of NGC
0253, we use the only available filter in the FUV band: F169M.

For most of our targets, AstroSat’s large ($28^{\prime}$) field of view (FoV)
includes enough sky area at larger galactocentric distances that our simple
exponential model (Eq.3) is sufficient for determining the background level of
images robustly. However, some targets have relatively smaller amounts of
empty emission-free sky due to the small coverage of observations or several
bright sources in the FoV. In these cases, regression over the exponential
model fails for a few targets. For example, NGC 0300 is sufficiently nearby
that we only have a radial profile out to a small galactocentric distance of
$R_{\mathrm{gal}}<12$ kpc. Furthermore, the exponential model is not
appropriate for the highly-inclined NGC 0253, where we see many bright gas
clumps at $R_{\mathrm{gal}}<10$ kpc. In the cases of NGC 3621 and NGC 5128, we
decided not to use the regression to determine the background level due to a
number of nearby bright sources. Finally, in the interacting system of NGC
1512-1510, a simple exponential model could not describe the complicated
radial profile of this merging system, hence the background value could not be
estimated robustly from radial profiles.

In these cases, we instead used a “mean-level” estimate of the backgrounds.
The background value for each of these targets is estimated by taking the mean
value of an annulus around each object. The annulus radius is provided by
visual inspection, ensuring the exclusion of nearby sources, mostly
$\mathrm{R}>3\,\mathrm{R}_{25}$. However, these annuli values might still
suffer from bright stars or reaching the edge of the FoV. We reject bright
sources using Chauvenet’s criterion in the annulus data distribution (i.e.,
iteratively rejecting data $>3\sigma$ where $\sigma$ is the standard deviation
and then recalculating $\sigma$). The standard deviation is inserted into the
Chauvenet criterion using the median absolute deviation (MAD) as an estimator.
The MAD standard deviation is provided by the astropy.stats
module333https://docs.astropy.org/en/stable/stats/index.html. Although the MAD
standard deviation is robust to outliers, it could be zero or much smaller
than the normal standard deviation due to the faint nature of background
emission. In that case, we used ordinary standard deviation to avoid failure
of the Chauvenet criterion. After rejecting data using this criterion, we
estimate the background as the mean in the annulus. Regarding the discussion
of the new XUV disk galaxy NGC 6744, which has spiral arms extending beyond 2
$R_{25}$ (as detailed in Section 4), we have decided to avoid using the radial
profile or “mean-level” background. The radial profile of this galaxy is not
exponential and the estimation of the background is not reliable due to the
presence of numerous bright stellar clusters and H II regions. Therefore, we
have carefully chosen a few 1.5′ apertures that are free from bright sources,
and estimated the mean emission from those locations. The estimated background
is 20 and 34 percent higher than “mean-level” and radial profile respectively.

Table 4 lists the foreground extinction, background values, and scale lengths
of our sample for both FUV band and CO emission.

Figure 6: Pixel value distribution of AstroSat F148W to GALEX FUV maps at a
common beam of 7$\farcs$5 for different galaxies. The $y$-axis is plotted in a
log scale. Both maps are background subtracted and corrected for Milky Way
foreground extinction using the same approach. The green dashed line is the
ratio of unity and the gray horizontal lines show a confidence band of 15 per
cent. The slope of regression between pixel values of GALEX and AstroSat maps
(described in Sec. 3.3.2) are shown as orange points on box plots. Figure 7:
Same as Fig. 6, but for AstroSat N219M to GALEX NUV maps.

###  3.2 Foreground extinction

As Milky Way foreground dust extincts UV emission of each target, we first
corrected both AstroSat FUV and NUV fluxes for foreground extinction following
the Leroy et al. (2019) procedure. A uniform foreground dust distribution is
expected due to the small angular size of each target. The ratio of total to
selective extinction $R_{\mathrm{FUV}}$ and $R_{\mathrm{NUV}}$ is given by
Peek & Schiminovich (2013) as a function of color excess $E(B-V)$. The
extinction is estimated using $A_{\lambda}=R_{\lambda}\,E(B-V)$ at the FUV and
NUV wavelengths. Based on Schlegel et al. (1998) dust maps and its $E(B-V)$
value for each galaxy, $A_{\mathrm{FUV}}$ and ANUV is estimated for each
target in the sample, resulting in a median foreground of
A${}_{\mathrm{FUV}}=0.21$ mag but the correction shows $A_{\mathrm{FUV}}>1$
mag for NGC 5128 and NGC 2835.

###  3.3 Quality Assurance

We characterize the quality of the AstroSat imaging through three separate
checks: we assess the resolution by fitting point sources in the data, we
validate the alignment against higher resolution HST data, and we compare the
flux scales to archival GALEX maps from Leroy et al. (2019). In all cases, the
AstroSat data meet acceptable quality standards.

####  3.3.1 Resolution and Alignment

We characterize the point spread function (PSF) of the data reduction
empirically using point sources in the final reduced products. The PSF of the
final data is primarily set by the quality of the tracking solution and
alignment of subimages, which can vary based on the brightness of sources in
the field and number of contributing observations to a final image.

To measure the PSF, we identify point sources in the image, stack the images,
and fit a Moffat PSF model (Moffat, 1969) to the resulting image stack. We
adopt the Moffat PSF model since it appears to characterize the PSF of bright
sources well, including a compact core with extended wings that result from
deviations in star tracking. We develop a customized solution to PSF
characterization because of the relatively low number of photon counts in an
individual source and the relatively small number of stars that are bright in
the UV. First, we smooth the image with a Gaussian kernel with a standard
deviation of 2 pixels. We then identify point sources using the DAO Star
Finder algorithm (Stetson, 1987) as implemented in the photutils package
(Bradley et al., 2020) in python. Of these candidate sources, we cross match
to objects identified in Gaia Data Release 3 (Gaia Collaboration et al.,
2023), retaining only those sources within $2^{\prime\prime}$ of a Gaia DR3
source with a parallax/error value of 10 or more. Such sources are likely to
be stars in our own Galaxy. These selections yielded typically 20-40 sources
per field. We stack the extracted sources on the maximum brightness pixel to
form a single representative star image and fit a two-dimensional Moffat
profile to the resulting stack. The median resolution of the FUV bands is
$1.4^{\prime\prime}$ and the median resolution of the NUV bands is
$1.2^{\prime\prime}$. The better resolution for the NUV bands comes from the
improved tracking of the higher signal-to-noise values in the NUV images
(Tandon et al., 2017). The derived PSF parameters are recorded in header of
each image in the data release.

We use the astrometric solution implemented in CCDLAB, following the approach
outlined in Postma & Leahy (2020). This method identifies UV-bright Gaia
sources in the field and fits an astrometric solution. We evaluate the quality
of the solution by comparing NUV data (where available) to the F275W filter
from the Hubble Space Telescope imaging (Lee et al., 2022a) through cross
correlation. In all cases, we find better than $0.4^{\prime\prime}$ accuracy
in the astrometric alignment.

Figure 8: Ratio of AstroSat (F148W) to GALEX (FUV) in NGC 1365 at a common
beam of 7$\farcs$5\. The color bar at right shows the ratio values in a linear
scale. We expect a ratio $<1$ (yellow to blue colors) as the central
wavelength of the F148W filter is shorter than the GALEX band. As we discuss
in Sec. 3.3.2, the slope of regression between AstroSat and GALEX $\beta=0.92$
with a offset of 0.001 MJy/sr.

####  3.3.2 Comparison with GALEX

To validate our reduction, we performed a comparison with GALEX observations.
Only a portion of AstroSat data is comparable with GALEX bands, notably F148W
and F154W in AstroSat can be compared to the GALEX FUV band, and N219M and
N242W from AstroSat can be compared to the NUV band. Even these comparisons
should be made with caution due to differences between the widths of filters
and their central wavelengths.

The units of AstroSat maps are converted to MJy/sr and then we convolve the
AstroSat images to a common resolution of 7.5″ and reproject the AstroSat
images to match the GALEX observations from Leroy et al. (2019). Pixel values
below 5$\sigma$ RMS are masked using the astropy.stats.sigma-clipped-stats
module on GALEX maps. Figures 6 and 7 show the pixel value distributions of
the AstroSat to GALEX ratio maps, indicating 25th- and 75th-percentiles of
distribution as the top and bottom of the boxes as well as the median value
(center line). The lines extending from the box indicate the minimum and
maximum of the the distribution. Using sklearn.linear_model
module444https://scikit-learn.org/stable/modules/generated/sklearn.linear-
model.LinearRegression.html Ordinary Least Squares (OLS) regression, we also
fit a line to average of every 30 pixel values, using
$Y(\mathrm{Astrosat})=\beta\,X(\mathrm{GALEX})+C$ over data in linear space to
obtain a slope to charaterize the linear relation between data values and
quantify any possible constant offsets between the data. After masking values
below three $\sigma$ for both AstroSat and GALEX maps at a common resolution,
we found good agreement between GALEX FUV and AstroSat F148W bands with a
median of $\beta=0.92$ and with an offset of $C=0.001$ MJy/sr which is lower
than the noise value by a factor of two in most targets. We expect a
systematically lower fraction due to differences in the passband and central
wavelength of F148W compared to the GALEX FUV filter
($\lambda_{\mathrm{central}}$=1528Å). In the NUV, we found a median of
$\beta=0.83$ and $C=0.002$ MJy/sr among targets at N219M band, which is
reasonable given the difference with the GALEX NUV filter bandwidth and the
central wavelength. We plot $\beta$ values over ratio of pixel value
distribution for each galaxy in Fig. 6, 7 at FUV and NUV bands, respectively.
Fig. 8 shows the ratio map of AstroSat F148W to GALEX FUV in NGC 1365 on a
linear scale where we see a good agreement at the 10 percent level across the
galaxy.

Figure 9: New candidates for XUV-disks galaxies. From top to bottom panels:
NGC 6744 and IC 5332 Left: Maps of the AstroSat (F148W) in blue and 2MASS (Ks)
in green at 2MASS resolution. The FUV map is in a asinh brightness scale.
Right: Digitized Sky Survey (DSS) 2 red map overlaid with white contours of
AstroSat F148W at levels of 26.2 and 27.9 $M_{\mathrm{AB}}$ for NGC 6744, 27.2
and 28 $M_{\mathrm{AB}}$ for IC 5332. The FUV maps are not corrected for
internal extinction. Blue ellipses are $R_{25}$ with 0.5 interval for NGC 6744
and 0.25 for IC 5332.

##  4 XUV disks

In the following sections, we illustrate the scientific potential of arcsecond
resolution and relatively wide field-of-view of the FUV imaging in the context
of the PHANGS multiwavelength data set. Unlike the PHANGS observations
conducted by ALMA, MUSE, JWST, and HST, which are predominantly confined to
the inner disks of galaxies, AstroSat’s observations extend significantly
beyond the optical disks, offering a more expansive view of the low-density
structure of galaxies (Leroy et al., 2021a; Emsellem et al., 2022; Lee et al.,
2022a, b). Here, we use the AstroSat data to identify extended ultraviolet
(XUV) disks beyond the optical disk of galaxies. Thilker et al. (2007)
searched for the XUV disk galaxies in the local universe (D$<40$ Mpc) using
GALEX observations, finding UV-bright complexes beyond the optical radius in
30% of spiral galaxies (Thilker et al., 2007). The detection of XUV emission
is particularly intriguing as it implies the presence of recent massive star
formation. Remarkably, this formation is taking place in poorly understood
environments, characterized by low ISM pressure and low metallicity. These
unconventional conditions foster extreme scenarios, which offer valuable
opportunities to challenge and refine our current theories of star formation.
Subsequent observations in the Local Volume and at a distance beyond 100 Mpc
have further confirmed the ubiquity of XUV disk galaxies (Lemonias et al.,
2011), reinforcing their importance in understanding galaxy evolution.

###  4.1 Background

There is some debate about what mechanisms power the XUV emission in galaxies,
which could arise from different types of stars. Spectroscopic observations in
the M83 and NGC4625 indicate that the emission of XUV disks ($>1.5R_{25}$) is
linked to the population of young stars associated with low-mass stars (Gil de
Paz et al., 2007a). Gil de Paz et al. (2007a) highlighted that the emission
lines in the XUV disks of these galaxies are formed by single stars with
masses between 20-40 M⊙. Furthermore, their findings indicate that the role of
planetary nebulae and blue horizontal branch (HB) stars in shaping XUV disks
should be negligible. The highly excited gas of planetary nebulae and its high
electron temperatures cannot explain the observed spectroscopic emission lines
in XUV disks. On the other hand, the blue color of NUV-optical emission, as
well as the structured shape of XUV disks (e.g., spiral arms), are pieces of
evidence that do not support the role of HB stars as candidates for powering
XUV disks (Gil de Paz et al., 2007a). Previous findings also confirm that FUV
complexes with masses of $10^{3}-10^{4}\>{\rm M_{\odot}}$ are being ionized by
single stars (Gil de Paz et al., 2005). On the other hand, UV emission could
also originate from white dwarfs in XUV disks (Sahu et al., 2019).

Different scenarios are proposed for triggering the star formation process in
XUV disks, such as interactions, perturbations, and gas accretion (Bush et
al., 2008, 2010; Das et al., 2021). In addition, spiral density waves could
propagate from inner to outer disk and result in gravitationally unstable
regions which eventually form stars (Lemonias et al., 2011). As one possible
indication of the formation mechanisms, radio observations also find extended
H I disks associated with XUV emission in M 83 and NGC 4625 (Tilanus & Allen,
1993; Bigiel et al., 2010a; Bush & Wilcots, 2004). In the case of the late
type galaxy NGC 0628, a faint trail of UV emission extends into the HI outer
disk, which resembles a spiral-like structure (Das et al., 2020). This
significant amount of HI is maybe due to a low-efficiency process of turning
neutral gas into stars (Gil de Paz et al., 2007a). The consumption timescale
of H I in XUV disks is estimated to range from a few Gyr up to the Hubble time
(Gil de Paz et al., 2005). Galaxies hosting XUV disks might indicate the
presence of molecular clouds as well as extended H I well beyond their stellar
disks. The detection of faint CO emission in the outer disk is essential to
understand the star formation efficiency (SFE) in this environment. For
instance, Dessauges-Zavadsky et al. (2014) detected CO(1-0) in M 63 with a low
SFE, compared to inner disk regions using the IRAM 30 m telescope. Koda et al.
(2022) used deep ALMA observations to identify small molecular clouds
associated with the XUV emission. The mechanisms behind the formation of XUV
disks in galaxies are still unclear and the number of well-studied targets
remains small. In this study, our objective is to use new UVIT observations to
identify new potential candidates using the XUV disk classification scheme
proposed by Thilker et al. (2007).

Figure 10: FUV F148W scale lengths versus CO scale lengths obtained from
radial profiles of galaxies, color coded with $R_{25}$. We show two new XUV
disks candidates with diamond markers.

###  4.2 New candidates

Following the prescriptions of Thilker et al. (2007) and Lemonias et al.
(2011), we used a foreground-extinction-corrected, background-subtracted UV
surface brightness ($\mu_{\mathrm{FUV}}$=27.25 AB magnitudes arcsec-2) as a
threshold level to identify FUV clumps in XUV disks. This UV surface
brightness corresponds to $\Sigma_{\mathrm{SFR}}$= $3\times 10^{-4}$ M⊙ yr-1
kpc-2, which is based on a Salpeter IMF and calibrations from Kennicutt
(1998). This type of XUV disk requires more than one structured UV complex
(e.g. spiral segments) and is known as a Type 1 XUV-disk or “M 83 like”
object. We do not consider diffuse UV emission as a criterion for XUV disks,
as it could be related to other recent SF processes or originated from hot
core-helium-burning stars (Hoopes et al., 2005). On the other hand, Type 2 XUV
disks are often dominated by a large, blue, low-surface-brightness outer area
with a higher specific star formation rate and less structured emission (see
Section 3 in Thilker et al., 2007).

Using the radial profiles presented in Section 3.1, we measured the scale
lengths of FUV emission and CO molecular gas. Figure 10 compares the scale
lengths of these two different data sets. On average, the scale length of FUV
($\gamma_{\mathrm{FUV}}\simeq 3500$ pc) is about twice that of CO molecular
gas. These results indicate that the molecular gas is concentrated in the
stellar disk, while structured clumps of young massive stars, with a longer
lifetime, can exist far beyond the optical disks. Significantly, as
highlighted by Bigiel et al. (2010b), it has been observed that the scale
length of H I is typically twice that of the FUV emission. Consequently, this
leads to an extended depletion time for neutral gas, suggesting that XUV disk
galaxies are frequently enveloped by extensive H I gas reservoirs. It is
important to emphasize that the PHANGS-ALMA observations are primarily focused
on the optical disk regions of galaxies, where the majority of massive CO
clouds (with masses exceeding 104 M⊙) are typically found. This emphasis is
underscored by prior detections of CO in the XUV disks, where such clumps are
often faint and isolated. Consequently, incorporating observations of diffuse
clouds, which would requires deeper observations, is unlikely to significantly
impact the determination of CO scale lengths.

When comparing scale lengths, our focus was on identifying galaxies with large
FUV scale lengths, particularly in comparison to CO emission. Among the
galaxies in our sample, NGC 6744 possesses the largest scale length, with
$\gamma_{\mathrm{FUV}}=9$ kpc and $\gamma_{\mathrm{CO}}=3.8$ kpc. We note that
the radial profile of this galaxy is not exponential, which has resulted in
poor regression results, and hence the uncertainty is likely underestimated.
We also observe that the obtained parameters of this galaxy, such as FUV scale
length, amplitude, and background, are close to being outside of the
reasonable range that we confined our initial regression parameters to. This
is another indication that our regression does not adequately address the
radial profile properties of this galaxy. Therefore, we did not use the
background measured from the radial profile for this galaxy, as also noted in
Table 4. This high value FUV scale length is not surprising as $R_{25}$ is
also more than 20 kpc in this galaxy. We investigate all targets with large
$\gamma_{\mathrm{FUV}}$ relative to $\gamma_{\mathrm{CO}}$ and identify NGC
6744 and IC 5332 as also having FUV bright segments structures beyond their
optical disk, considering these as new candidates for XUV disks. We note that
these targets are not covered in the previous studies of XUV-disk galaxies by
Thilker et al. (2007). We show new XUV disk candidate galaxies in Fig. 9 and
discuss the features of these XUV-disk galaxies below.

NGC 6744: This galaxy is an intermediate gas-rich spiral galaxy that hosts
several FUV-bright clumps arranged in a spiral-arm structure at a distance of
approximately 29 kpc from its center. This galaxy is situated near the
Galactic plane, which has led to it being less studied in surveys at other
wavelengths due to the observational complexities of observing at low Galactic
latitude. However, we have identified two clearly visible FUV-bright spiral
structures, both located beyond 1.5 times the optical radius ($1.5R_{25}$),
without any corresponding bright optical counterparts. Interestingly, we do
not see any bright $3.3$$\mu$m Polycyclic Aromatic Hydrocarbons (PAH) emission
from nearby stellar population in this area, as traced by IRAC 3.6 $\mu$m
images (Fisher & Drory, 2010, and references therein). Low resolution H I
observations also confirmed the presence of these two spiral arms, suggesting
streaming motions along the H I arms as a result of interaction with companion
galaxy (Ryder et al., 1999). The companion IB(s)m NGC 6744A galaxy is visible
in the northwest region in the FUV band as well. Furthermore, GMOS/IFU
observations in the center of this galaxy suggested two periods of star
formation which the last one happened one billion years ago, due to a merger
event (da Silva et al., 2018). Having this evidence, we propose that NGC 6744
is a Type 1 XUV-disk and its nature is probably related to the accretion of
gas from its companion in a merger event.

IC 5332: this galaxy is also another intermediate spiral galaxy but with
5$\times$ lower stellar mass (M$=0.41\,\times\,10^{10}$ M⊙) in comparison with
NGC 6744. Although we see several star forming complexes beyond the optical
disk, we do not identify any ordered spiral-like features. Unlike NGC 6744,
there are several irregular clumpy structures at $R>R_{25}1.5$. Thilker et al.
(2007) found that Type 2 XUV disks are more common in late-type/low-mass
spirals, similar to IC 5332. Using 2MASS Ks data from Skrutskie et al. (2006),
we are unable to define a low surface brightness (LSB) zone for these
irregular structures in the outer disk, as the Ks-band luminosity is faint in
this region. Parkes and ATCA observations of IC 5332 also confirm the
existence of an extended H I emission in the outer disk (Pisano et al., 2011).
On the other hand, using the H$\alpha$ emission map, we see a good match with
FUV emission (but not with old stellar population sites traced by IRAC 3.6
$\mu$m) in several clumps located in the southeast and west regions. These
results might suggest an ongoing recent star formation process that results in
diffuse UV emission and not a outer spiral arm feature. This scenario might
cast doubt on IC 5332 being an XUV-disk galaxy. Furthermore, being in the LGG
478 group of galaxies, makes IC 5332 a possible part of a merger event. A
detailed study of star formation history in IC 5332 could shed light on
understanding different episodes of star formation and confirm such a
scenario. The origin of the IC 5332 XUV emission remains unclear, but this
target remains a candidate for an XUV disk.

##  5 Structural Measurements of FUV Emission

The motivation behind studying the clumping factor in CO and FUV emission is
to understand the structure and distribution of molecular gas and newly born
stars in galaxies. Leroy et al. (2013) indicate that models describing the
structure of the ISM require detailed information on the mapping between the
observed average surface density, referred to as the “area-weighted” surface
density, at a resolution of a kpc. This mapping plays a crucial role in
comparing observations with theoretical models and enhancing our understanding
of galaxy structure.

In this section, we explore the brightness distribution and emission
structures of the FUV map as measured using the clumping factor. We compare
the measures for FUV emission with the results for the CO. We contrast the
clumping factor measurements with the results derived from the Gini
coefficient, which can be used to characterize the structure of emission in a
map (e.g., Davis et al., 2022). Both the clumping factor and the Gini
coefficient are scalar measurements of the shape of the probability density
function.

###  5.1 Clumping Factor

The clumping factor is a scalar measurement that traces the width of the
probability density function relative to its mean, which can be interpreted to
measure the smoothness of the emission. We define the clumping factor
following Leroy et al. (2013); Sun et al. (2022a):

$c_{\mathrm{pix,\,\theta\,pc}}=\frac{\left(\sum\limits_{i}I_{\textit{i},\,\theta\,pc}^{2}\right)N_{\mathrm{pix}}}{\left(\sum\limits_{i}I_{\textit{i},\,\theta\,pc}\right)^{2}},$
(4)

where $I_{\textit{i},\,\theta\,pc}$ is the surface brightness of region ($i$)
at resolution of $\theta$ pc. The summation is over all pixels inside the
region and $N_{\mathrm{pix}}$ is the total number of pixels inside that
region. Eq. 4 is the same definition from Leroy et al. (2013), where it is a
ratio between the mass-weighted mean and the area-weighted mean of surface
density. The measurements from Leroy et al. (2013) indicated that molecular
gas is highly clumped ($c\gtrsim 7$), whereas atomic gas has a smooth
distribution ($c\approx 1$). This result could be interpreted as showing that
most of the 21 cm emission originates from a diffuse medium with a high volume
filling factor (Leroy et al., 2013) while most of the molecular emission is
organized into individual clouds. However, the UV clumping factor has not been
examined before. The photospheric emission from young stars, traced by the
FUV, should have slightly different clumping structures compared to molecular
gas since they are much brighter in the outer disk. Additionally, the FUV
emission may originate from a wide range of stellar populations with different
ages, so the clumping factor could lie between that of molecular and atomic
gas. Here, we explore the clumpiness in CO versus FUV emission to show how the
formation of massive stars may derive from the ISM clumpiness. Having 31
galaxies spanning a wide range of mass and metallicity, we can also inspect
the clumpiness in different galactic environments. We restrict our analysis to
the regions where FUV maps overlap with the ALMA CO observations and carry out
the analysis at a common resolution of 175 pc.

Figure 11: The lower and upper distribution of smoothness measurements of
different morphological structures of galaxies at 175 pc resolution and 1 kpc
size hexagonal apertures, clumping factor (left), and Gini coefficient
(right). The horizontal line inside each box plot represents the median value.

We use the environment masks from Querejeta et al. (2021) to measure the
clumping factor in different morphological regions of galaxies. The
environment masks include almost all morphological features of galaxies, most
importantly, spiral arms, interarm regions, and centers. The masks were
constructed using visual inspection of IRAC 3.6 $\mu$m data and structural
decomposition analysis (Herrera-Endoqui et al., 2015; Salo et al., 2015). We
note that the widths of the spiral arms masks are relatively broad (1-2 kpc)
and are estimated from the spatial distribution of CO emission.

We find that the median values of the clumping factor estimated from FUV and
CO emission are similar across most environment masks at a spatial resolution
of 175 pc. This is illustrated in Fig. 11, which shows the range of clumping
factors seen in different galaxies that share common morphological features.

However, the center and spiral arms are the only regions that are more clumped
in FUV relative to the CO. The median of the FUV clumping factor is 1.51,
whereas the clumping factor for CO is 1.35 in the center of galaxies. To
analyze the differences between these two distributions further, we applied
the Kolmogorov-Smirnov (K-S) statistics. This analysis resulted in a KS value
of 0.4 in the center, indicating a modestly significant difference between the
distributions with a p-value of 0.04. In contrast, within the spiral arms, the
median clumping factor is $c=2.7$ for the FUV and $c=2.5$ for CO. However, the
KS value in this region is lower than in the center, at 0.21. The associated
high p-value of 0.92 shows no evidence for a significant difference between
the FUV and CO distributions in the spiral arms. Consequently, this suggests
that the distribution of the clumping factor for FUV and CO in the spiral arms
is more similar compared to the center.

The bars of galaxies have on average the largest clumping factor, particularly
for the CO emission, with a CO clumping factor distribution reaching up to
$\approx 7$. This result is not surprising since the most massive giant
molecular clouds are located in bars (e.g. Rosolowsky et al., 2021) but these
regions can also be devoid of molecular gas (Leroy et al., 2021b). CO emission
in bars has also been shown to have a relatively thin structure (Stuber et
al., 2023).

Using Table 3, we observed a higher cFUV in the centers of galaxies hosting
AGNs. Notably, a similar trend was also seen in a few non-AGN galaxies.

On the other hand, spiral arms are more clumped in FUV emission, implying UV
bright complexes are more concentrated and molecular clouds more uniformly
spread along spiral arms. The clumping factor could also be exaggerated by
high attenuation in the FUV band, broadening the distribution of light. Our
results suggest that in general, clumps of molecular gas are more concentrated
in galaxies, whereas FUV emission is more spread out at 175 pc resolution.

Figure 11 also shows the distribution of the measured clumping factor using 1
kpc size hexagonal apertures, labeled as “all”. Similar to the galaxy-
integrated values, the median CO clumping factor is still higher ($c=1.7\pm
0.1$) than the FUV values ($c=1.3\pm 0.1$). Our results are in good agreement
with Sun et al. (2022a), where they used highly-sensitive ALMA observations of
90 nearby galaxies and reported $c_{\mathrm{CO}}=1.9$ within hexagonal
apertures. Leroy et al. (2013) reported a higher CO clumping factor ($c=7$) at
20-300 pc resolution. One should account for molecule-poor low-metallicity
systems that Leroy et al. (2013) included in their sample, which usually have
very high clumping factors. In contrast, the PHANGS-ALMA sample typically
studies more distant, higher mass systems (i.e., more metal rich and bright in
CO).

###  5.2 Gini coefficient

We also use the Gini coefficient ($G$) as a non-parametric measure of the
distribution of a quantity (Gini, 1912). It has been adapted for quantitative
morphological studies of galaxies at different wavelengths, including GALEX
observations (e.g. Muñoz-Mateos et al., 2009). It measures the inequality in a
distribution and the higher values indicate a concentration of medium
brightness in a small region. The non-normalized values of the clumping factor
might raise a concern where two different galactic environments exhibit
similar values but not the same morphology. Hence, the Gini coefficient is
considered here to assist us in distinguishing between the smoothness of
different environments. Following Davis et al. (2022), we define the parameter
as:

$G_{\theta\,\mathrm{pc}}=\frac{1}{\bar{I}\,n(n-1)}\sum_{i=1}^{n}(2i-n-1)I_{i},$
(5)

where $n$ is a number of pixels, $I_{i}$ surface brightness of each pixel
sorted in ascending brightness order, and $\bar{I}$ is the mean brightness
measured over all the pixels. The summation ($i$) is over all pixels of the
area. The Gini coefficient, with values between 0 and 1, measures the
concentration of clumps in an area. A higher $G$ value indicates a greater
concentration of clumps. The accuracy of the $G$ measurement can be influenced
by factors such as aperture size and the signal-to-noise ratio of the data. If
there are many faint pixels in the region, the results may be impacted
(Lisker, 2008). We present the distribution of the Gini coefficient in Fig.
11. The median of the Gini coefficient measured in 1 kpc hexagonal apertures
shows that our measurements of $G_{\rm CO}=0.43$ are higher than $G_{\rm
FUV}=0.3$. This aligns with clumping factor measurements, indicating that UV
emission is smoother at a kpc resolution.

The Gini coefficient presents a complementary picture of smoothness of the ISM
in galaxies compared to the clumping factor. As illustrated in Fig. 11, the
smoothness of both the CO and FUV emission decreases in parallel from the
inside to the outer parts of galaxies. The centers exhibit a smoother
distribution than spiral arms and disks. We note that the high clumpiness
measured by Gini in disks are due to the high level of noise in ALMA data at
175 pc. In spiral arms, there is no clear correlation between $G$ and $c$,
with $G$ suggesting a more clumpy distribution. This discrepancy may be due to
the sensitivity of $G$ to the effect of faint diffuse emission in spiral arms
or the limited sample size (only 14 galaxies with spiral arms masks at this
resolution). However, both measures find that the clumping of the CO and FUV
emission is similar with the CO being typically slightly more clustered than
the FUV. Such a trend could be expected given the typical “cycling” time of
the molecular ISM ($<30$ Myr Blitz & Shu, 1980; Chevance et al., 2022) is less
than the FUV emission that would be produced from the stars formed from the
molecular clouds (100 Myr). Such clumping should be readily measured in
simulations.

##  6 Comparing FUV and H$\alpha$ Emission

In this section, we compare the FUV to the H$\alpha$ emission in the resolved
structure of galaxies. FUV and H$\alpha$ are tracers of star formation on
different timescales. The UV continuum arises primarily from the photosphere
of O and B stars ($\mathrm{M}>3~{}\mathrm{M}_{\odot}$), sensitive to star
formation over a 100 Myr time scale (Kennicutt & Evans, 2012). In contrast,
the H$\alpha$ emission line originates from the recombination of gas that is
ionized in H II regions by massive stars (Extreme UV emission), which are
typically O and only early-type B stars with masses of
$M>17~{}M_{\odot}\,$(Lee et al., 2009, 2011). The H$\alpha$ emission line
traces recent star formation with a timescale of about 5 Myr (Kennicutt &
Evans, 2012). Hence, the H$\alpha$ emission is the optical proxy for photons
with $E_{\gamma}>13.6~{}\mathrm{eV}$ but the AstroSat FUV data trace photons
with $6<E_{\gamma}/\mathrm{eV}<9$.

Both tracers also suffer the effects of dust attenuation, but the attenuation
toward the shorter wavelengths (FUV) is higher. Many studies (e.g Calzetti et
al., 1994; Kreckel et al., 2013) have observed that nebular emission from
dust-enveloped H II regions undergo roughly double the reddening of the
stellar continuum. As a result, H$\alpha$ attenuation is more comparable to
the UV continuum, once differential reddening between gas and stars is
factored in (Lee et al., 2009).

Utilizing the improved resolution from the AstroSat FUV observations, our goal
is to compare these observations with PHANGS-MUSE H$\alpha$ data (Emsellem et
al., 2022). With 15 targets common to both data sets, we can probe the
variation in the FUV-to-H$\alpha$ ratio across distinct galactic environments
at 175 pc and 1 kpc resolution. This ratio is influenced by a multitude of
factors such as dust attenuation, the age and mass of the stellar populations,
star formation history, Initial Mass Function (IMF), and metallicity (Lee et
al., 2009).

The objective of this section is to examine the local variations of $\log$
FUV/H$\alpha$ (hereafter the “log ratio”) with respect to the resolved
structure of galaxies at a scale of 175 pc. Using the original unit of the
UVIT maps (erg cm -2 $\lambda^{-1}$ s-1), in this section, we express our
results for the log ratio in terms of:

$\displaystyle\log\left(\frac{\mathrm{FUV}}{\mathrm{H}\alpha}\right)$
$\displaystyle=\log_{10}\left(\frac{\lambda_{\mathrm{FUV}}F_{\mathrm{FUV}}/\mathrm{(erg\,s^{-1}\,cm^{-2})}}{F_{\mathrm{H\alpha}}/\mathrm{(erg\,s^{-1}\,cm^{-2})}}\right)$
(6)
$\displaystyle=\log_{10}\left(\frac{L_{\mathrm{FUV}}/\mathrm{(erg\,s^{-1})}}{L_{\mathrm{H\alpha}}/\mathrm{(erg\,s^{-1})}}\right)$
(7)

where the $\lambda_{\mathrm{FUV}}$ is the central wavelength of the FUV filter
used.

The aim is to build upon the previous study by Lee et al. (2009) on an
integrated galaxy scale and understand which physical properties affect the
log ratio on a sub-kpc scale. Our analysis centers on the observed log
FUV/H$\alpha$ ratio with an emphasis on the effect of attenuation. However,
since we do not have NUV observations for all of our targets, we cannot
independently assess the stellar attenuation at UV bands.

Figure 12: Distribution of the log FUV/H$\alpha$ ratio across different
morphological environments using both 175 pc resolution and 1 kpc hexagonal
apertures (each box represents different quantiles of the ratio). The tails of
the distributions reveal variations in fainter quantiles, particularly a lower
fraction in centers and bars. The median of each distribution is marked with a
vertical line in the central quantile.

###  6.1 Environmental dependency

We investigate the variation of the log FUV/H$\alpha$ ratio across different
environments by examining the distribution of the uncorrected log
FUV/H$\alpha$ ratio at resolutions of 175 pc and 1 kpc, as shown in Fig. 12.
The median of log FUV/H$\alpha$ ratio is approximately 2 across these
environments. At the coarser 1 kpc resolution, the log ratio exhibits a median
of 2.1 within hexagonal apertures for all targets. These observations align
well with predictions from stellar population synthesis codes. In these
models, the H$\alpha$ flux is determined under the assumption of Case B
recombination without flux leakage, and FUV fluxes are derived by stacking
stellar spectra for each cluster. Notably, Hermanowicz et al. (2013) reported
$\log(\mathrm{FUV/H}\alpha)$ values in the range of 1.5 to 2.5.

The centers of galaxies have a median of $\log(\mathrm{FUV/H}\alpha)=1.7$, the
lowest among all environments, indicating a greater impact of dust
attenuation, older stellar populations, or both. Furthermore, we found spiral
arms exhibit a higher median $\log(\mathrm{FUV/H}\alpha)=1.9$ – 2.0. The
distribution of FUV/H$\alpha$ is similar in inter-arm regions and disks with a
median of $\sim 2.1$.

AGN-hosting galaxies, which were discussed in Section 5.1, also exhibit a
lower log ratio (1.13) compared to non-AGN galaxies (1.88) in the center,
although a direct connection between AGN activity and this ratio has yet to be
established. Among the sampled galaxies, the center of NGC 1365 displays the
lowest log ratio, with $\log(\mathrm{FUV/H}\alpha)<1$. The classification of
the central source in this galaxy, as provided by Querejeta et al. (2021),
refers to an ellipse with a radius of more than 1.5 kpc, thereby encompassing
the central star-forming ring. As highlighted by Whitmore et al. (2023), this
central ring contains several bright, young star-forming regions, which are
notably luminous in narrow-band H$\alpha$ HST maps (private communication with
Ashley Barnes). This leads to an increased H$\alpha$ emission in the center,
with approximately 70 percent of this emission stemming from star-forming
regions rather than the AGN. Therefore, the low FUV/H$\alpha$ ratio observed
in this region cannot be attributed exclusively to the AGN’s influence.
Further investigation of the inner 5″(= 475 pc) of the NGC 1365 central source
reveals a smooth non-star-forming disk, reminiscent of early-type galaxies.
This region remains stable against gravitational collapse (Schinnerer et al.,
2023). Other galaxies hosting AGNs, such as NGC 1566, NGC 3627, and NGC 7496,
exhibit a log FUV/H$\alpha$ ratio exceeding 1.4 in their centers. We note that
the measurement of H$\alpha$ in the center of AGNs can be challenging, as its
spectra show broad features and multiple components that are not fitted by the
single Gaussian fitting approach used by Emsellem et al. (2022).

###  6.2 Radial profiles

Figure 13: Gaussian-smoothed radial profile of log FUV/H$\alpha$ for all
galaxies at a resolution of 175 pc. The median values are represented by a
black line, while the 16-84 percentile range of values is shown in gray. The
mean of the smoothed profile is displayed as green circles with error bars,
taking into account uncertainties in both H$\alpha$ and FUV. The radial
profile of each galaxy is presented with a dashed line. The observations are
limited to the field of view of MUSE. The radial profiles of two galaxies that
do not exactly follow the median trend are highlighted and discussed in
Section 6.2: IC 5332 (purple) and NGC 3627 (pink).

Figure 13 presents the variation of the log ratio with galactocentric radius
scaled to the effective radius ($R_{e}$) for the 15 galaxies in the sample,
obtained from Leroy et al. (2021a). The median profile across all 15 targets
is nearly flat with a typical value of $\log(\mathrm{FUV/H}\alpha)=1.9$, which
is consistent with the analysis we provide in Section 6.1. Most of our targets
have a flat radial profile, with a few spikes or dips. Most notably, the
galaxies IC5332 (log FUV/H$\alpha\sim 2.1$) and NGC3627 (log FUV/H$\alpha\sim
1.65$), show a significant variation.

The low-mass spiral galaxy IC5332, which is a candidate for XUV type 2 as
discussed in Section 4, shows a higher log FUV/H$\alpha$ ratio. The majority
of its H II regions have a ratio between 1.6 and 1.9, with a few regions
exhibiting higher ratios ($>$2) in spiral arm ends, indicating the presence of
older populations. The higher ratio of log (FUV/H$\alpha$) is linked to the
low dust content of IC5332, where most of its H II regions display minimal
reddening, $E(B-V)=0.05-0.1$.

Conversely, NGC3627, which extends up to 3
R${}_{\text{Gal}}$/R${}_{\text{eff}}$, demonstrates several dips in its radial
profile, corresponding to various H II regions combined with a few Milky Way
stars in its spiral arm. This combination results in a much lower ratio. The
galaxy hosts a massive star-forming region located at the end of the bar,
which shows a higher reddening (E(B-V)$>$1) and molecular gas
(I${}_{\text{CO}}$$>$200 K km s-1). In the central region, we observe a ratio
of 0.7, which, once again, is associated with high reddening and a substantial
amount of molecular gas. The H II regions display ratios of 0.9-1.1, often
correlated with nearby bright molecular clouds with intensities of
$I_{\text{CO}}$$>$25 K km s-1.

The nearly flat radial profile of the logarithmic ratio aligns well with the
findings of Mehta et al. (2023). In their study, they investigated the dust-
corrected FUV/H$\alpha$ ratio in intermediate-mass galaxies (with stellar
masses ranging from $10^{9.5}$ to $10^{10.2}~{}M_{\odot}$) at a redshift of
approximately 1. They observed a similar trend persisting up to a normalized
galactic radius of $R_{\text{gal}}$/$R_{\text{e}}<1.6$.

###  6.3 Physical parameters affecting log FUV/H$\alpha$

Given the relatively high physical resolution of the PHANGS-AstroSat data set
and the richness of the supporting data, we have a new opportunity to
understand what physical factors are driving changes in the log FUV/H$\alpha$
ratios on 175 pc scales. These effects have been explored in previous works
based on the integrated properties of galaxies or at coarser resolution (Lee
et al., 2009; Meurer et al., 2009). While $\log(\mathrm{FUV/H}\alpha)$ could
provide good insight into galactic evolution through, e.g., tracing the star
formation history (Lomaeva et al., 2022), the effects of dust attenuation and
reddening will alter this ratio, which requires carefully understanding the
effects of dust. There are a myriad of possible factors shaping the
FUV/H$\alpha$, so we assess whether these effects are detectable in our
sample. In addition to those explored below, previous works have proposed
several possible physical factors that change FUV/H$\alpha$ including the
porosity of the interstellar medium (ISM), stochastic effects from the number
of massive stars, and variations in the Initial Mass Function. Below, we focus
our work on factors where we have good observational proxies.

##### Metallicity

Stellar populations with lower metallicity have hotter atmospheres, resulting
in the production of more UV photons, particularly ionizing ones (Lee et al.,
2009). Conversely, metal-rich populations, such as those concentrated in
galaxy centers, tend to be cooler and more evolved, emitting a reduced amount
of ionizing flux compared to non-ionizing UV continuum. Theoretical
demonstrations by Lee et al. (2009), employing models from Bruzual & Charlot
(2003), showed that the effect of metallicity primarily influences H$\alpha$
emission rather than FUV emission due to assumptions regarding case B
recombination and the spatial treatment with ionizing photons. However, to
observe this effect, one requires a significant variation in metallicity, so
we expect this effect to be weak across our sample.

##### The Equivalent Width (EW) of H$\alpha$

The EW of H$\alpha$ is also used to trace the age of young stellar
populations, particularly in areas where the H$\alpha$ emission is bright
enough (Scheuermann et al., 2023). The EW of H$\alpha$ serves as an indicator
of the relative brightness of the nebular emission compared to the underlying
stellar continuum. Younger stars, with ages on the order of millions of years,
exhibit high EW values for H$\alpha$ as they are actively ionizing the
surrounding gas, producing intense nebular emission. Observations in both the
local universe and at higher redshifts confirm changes in the EW of H$\alpha$
with the age of H II regions (e.g., Cedrés et al., 2005; Villar et al., 2011).
On the other hand, evolutionary synthesis models predicted its functional form
(Zurita & Pérez, 2008). The EW of H$\alpha$ is a highly observable signature
of the balance between very young and older stars. At the scale of H II
regions, we expect to observe an anti-correlation between the EW of H$\alpha$
and $\log(\mathrm{FUV/H}\alpha)$, where bright H$\alpha$ is associated with
ages below 10 Myrs. For older stellar populations, where the EW of H$\alpha$
is below 10 Å, the relation is not notable.

##### Star Formation History

Sharp fluctuations in the star formation rate (SFR) could also result in
changes in the log FUV/H$\alpha$ ratio, given the varying lifetimes of O and B
stars. Consequently, this ratio may be used to trace variations in the star
formation rate on different timescales, ranging from 5 to 200 Myr (Kennicutt &
Evans, 2012). This is often referred to as the “burstiness” of star formation.
On the other hand, a temporary decrease or “gasp” in the star formation rate
should also be considered in this context (Meurer et al., 2009). Mehta et al.
(2023) investigated the time-evolution of the dust-corrected log FUV/H$\alpha$
and found under conditions of steady, constant star formation, an equilibrium
state is achieved with a log FUV/H$\alpha\sim 2$. This highlights how SFH
parameters such stellar mass, age, and metallicity might impact the
FUV/H$\alpha$ ratio in galaxies.

Figure 14: Spearman’s correlation matrix illustrating the interrelationships
between our predictor variables, analyzed at a resolution of 175 pc.

Here we aim to quantify the relationship between $\log(\mathrm{FUV/H}\alpha)$
and various observed quantities. Initially, we introduce a comprehensive model
encompassing the majority of observed and physical quantities to describe the
log ratio as outlined in Section 6.3.1. Next, we present a simplified version
of the model wherein dust reddening emerges as the primary factor accounting
for 50 percent of the variation in the log ratio.

####  6.3.1 Drivers of the FUV/H$\alpha$ Ratio

In this section, we present a comprehensive model to quantify the role of
different variables in predicting the log of the FUV/H$\alpha$ ratio. Previous
studies have demonstrated how various physical parameters of galaxies,
specifically resolved structural parameters, can be modeled using multivariate
linear regression in logarithmic space (e.g., Dey et al., 2019; Sun et al.,
2022b). We aim to predict log FUV/H$\alpha$ using variables such as E$(B-V)$,
$I_{\mathrm{CO}}$, H$\alpha$ EW, stellar age, mass, and metallicity using the
panchromatic PHANGS data. Leveraging the PHANGS MUSE observations, we employ
maps of stellar mass, light-weighted age, and light-weighed stellar
metallicity for our targets. The specific models used in that fit set the
range of expectations for our results, which we summarize here. Specifically,
they employ a young fitting template over observed lines and continuum
described in Emsellem et al. (2022). In that study, the E-MILES stellar
population models from Vazdekis et al. (2016) and Asa’d et al. (2017) are
expanded to include younger age groups. This is particularly important as we
are making a direct comparison of the ratio of young stellar populations
traced by H$\alpha$ at $<5$ Myr, to intermediate ages traced by FUV emission.
The selected templates incorporate the Padova isochrones and include 18 bins
for ages ranging from 6.3 Myr to 14.12 Gyr. Here, we note that most of the
ages measured from PHANGS-MUSE using the young template are older than 10
Myrs, which does not align with the typical age of 5 Myrs for H$\alpha$ bright
H II regions as highlighted by Kennicutt & Evans (2012); Hassani et al.
(2022). Therefore, to more accurately trace the ages of these younger regions,
we have incorporated the EW of H$\alpha$ emission as a weighting factor in the
LASSO analysis. Additionally, the stellar metallicity is binned from $-0.7$ to
0.22 for older stellar populations (age $>63$ Myr), while for younger
populations, the [Z/H] bins range from $-0.7$ to 0.41. Bright stars are masked
in our results to avoid potential distortions. For our 175-pc resolution, we
also include an environmental classification, which can assume values of 0 or
1. These environments are categorized as center, bar, spiral, interarm, and
disk. We perform the same analysis for the 1-kpc scale but exclude the
environmental classifications since the different environments are not well
resolved at this resolution.

We note two important cautions here. Firstly, we assume a linear relationship
in log space between each predictor and the outcome variable, although we
acknowledge that may not be the case. The combination of dust reddening with
stellar mass, age, and metallicity, may not exhibit a linear relation with the
log FUV/H$\alpha$, especially at a 1 kpc scale. The interplay and degeneracy
between dust, metallicity, and age of stars has been addressed in previous
studies (Lee et al., 2009). Moreover, due to this degeneracy, we anticipate
colinearity between parameters, particularly between $E(B-V)$ and CO
intensity, or age and metallicity. This co-linearity will impact our results,
as the coefficients of these parameters in the model will be influenced by
each other.

Figure 14 highlights the correlation between different predictors. We observe
a correlation between stellar metallicity and age, with a Spearman coefficient
of approximately 0.6, indicating an older population with higher metal
content. We also find a strong anti-correlation between the EW of H$\alpha$
and the age of the stellar population. A similar anti-correlation is also
observed between the EW of H$\alpha$ emission and stellar metallicity. It is
important to note that different environments do not exhibit any significant
correlation with physical or observed parameters. However, we do observe a
weak trend between the presence of a bar and the surface density of stellar
mass, which can be attributed to the concentration of stellar gas in the bar
of most galaxies (Stuber et al., 2023).

We use the Least Absolute Shrinkage and Selection Operator regression (LASSO;
Tibshirani, 1996) to understand how each predictor control the variation of
the log ratio. Following Dey et al. (2019), we use 5-fold cross-validation
(CV) to fine-tune the hyperparameters of the model, ensuring that the
performance is evaluated on different subsets of the data. LASSO is a
regularization method that helps prevent overfitting in linear regression
models by introducing a penalty to the absolute sizes of the coefficients.
This not only helps in model simplification but also in feature selection, as
some coefficients can be shrunk to zero, effectively excluding that predictor
from the model. The degree of regularization is controlled by the parameter
$\alpha$. By adjusting the $\alpha$ value through cross-validation, we make
sure it provides the best fit between our training and validation data. This
approach decreases the chances of overfitting and enhancing simplifcation of
the model. We apply LASSO regression to our linear model, which is represented
as:

$\log\left(\frac{\mathrm{FUV}}{\mathrm{H}\alpha}\right)={\alpha_{0}}+\sum_{i=1}^{n}\alpha_{i}[\log
X_{i}-\log\langle X_{i}\rangle].$ (8)

Here, $\alpha_{0}$ represents the intercept, and $\alpha_{i}$ denotes the
power-law exponents of our predictors. We standardize our predictors by
normalizing by the median values of each variable ($\langle X_{i}\rangle$). As
indicated by Equation 8, we use a log transform for all parameters, but we do
not apply the transform for E(B-V) and metallicity, which are already
intrinsically logarithmic.

Table 5:  The LASSO regression coefficients ($\alpha_{i}$) for different
predictors of log FUV/H$\alpha$ at 175 pc and 1 kpc resolution. The norm is
the median of each predictor which we subtract that in the log scale before
regression.

Parameters | 175 pc | 1 kpc
---|---|---
| Norm | $\alpha_{i}$ | Norm | $\alpha_{i}$
$E(B-V)$ | 0.17 $\mathrm{mag}$ | $-0.86\pm 0.01$ | 0.11 $\mathrm{mag}$ | $-0.61\pm 0.1$
$I_{\mathrm{CO}}$ | 2.51 $\mathrm{K~{}km~{}s^{-1}}$ | $-0.11\pm 0.01$ | 1.75 $\mathrm{K~{}km~{}s^{-1}}$ | $-0.10\pm 0.02$
EW(H$\alpha$) | 15 Å | $-0.36\pm 0.01$ | 11 Å | $-0.46\pm 0.02$
Age | 615 Myr | $-0.30\pm 0.01$ | 707 Myr | $-0.36\pm 0.03$
$\Sigma_{\star}$ | 174 $M_{\odot}~{}\mathrm{pc}^{-2}$ | $-0.07\pm 0.01$ | 140 $M_{\odot}~{}\mathrm{pc}^{-2}$ | $-0.18\pm 0.04$
$[\mathrm{Z/H}]$ |  $-0.29$ dex | $0.06\pm 0.01$ |  $-0.22$ dex | 0.00
EnvironmentsaaUsing environmental masks, we identify different morphological
regions of galaxies and assign a weight of 0 or 1 to each individual part.
|  |  |  |
Center | $\cdots$ | $-0.03\pm 0.01$ | $\cdots$ | $\cdots$
Bar | $\cdots$ | $-0.08\pm 0.01$ | $\cdots$ | $\cdots$
Spiral | $\cdots$ | 0.00 | $\cdots$ | $\cdots$
Interarm | $\cdots$ | $-0.02\pm 0.01$ | $\cdots$ | $\cdots$
Disk | $\cdots$ | $-0.02\pm 0.01$ | $\cdots$ | $\cdots$
$R^{2}$ | 0.67 | 0.66
Residual (dex) | 0.18 | 0.20

Note. — The residual value represents the scatter around the model, where we
use the standard deviation to measure it. The VIF for all parameters ranging
from 2 to 3.7. Notably, the VIF for stellar mass is 5.8 at 1 kpc scale.

We present results of the regression at 175 kpc and 1 kpc resolution in Table
5. The intercept term ($\alpha_{0}$) represents the expected log FUV/H$\alpha$
when all predictors are equal to their medians, which is $\approx 2.0$ at both
resolutions. The slope coefficients indicate the change in the log
FUV/H$\alpha$ while holding all other predictors constant. At 175 pc
resolution, more localized effects and variations within resolved structures
of galaxies may introduce additional complexities, which could account for the
observed differences. In general, we observe negative exponents for all
predictors, except for metallicity. A higher coefficient for dust reddening is
observed at both resolutions, indicating the importance of attenuation in the
log ratio. We also note that the slopes for the different environment
predictors are all weak, suggesting that variation in the log ratio that we
observe can be explained better by variations in other parameters in the
model. Similarly, the scaling for metallicity ($Z/H$) is also weak, though the
range of metallcities probed is relatively small.

Additionally, the H$\alpha$ EW also exhibits a negative slope, suggesting that
younger sources with prominent H$\alpha$ EW values may result in a lower log
FUV/H$\alpha$ ratio. However, it is important to note that the relationship
between the age of the stellar population and the H$\alpha$ EW is not linear
(Cedrés et al., 2005). We note the intrinsic correlation between the line
brightness and its EW, which is tight in star-forming regions.

Furthermore, we observe a negative slope in the relationship between age and
the FUV/H$\alpha$ ratio, which is surprising. On the scale of H II regions or
stellar clusters (i.e., $<50$ pc), we expect that a higher FUV/H$\alpha$ ratio
would correspond to an older population (i.e., $>100$ Myr) where the H$\alpha$
emission is faint (Ujjwal et al., 2022). However, at a resolution of 175 pc,
which is the best common resolution available, there might not be a clear
relation due to the mixing of diffuse H$\alpha$ emission with H II regions.
Taking into account the degeneracy between age, metallicity, and dust
attenuation, we propose that this negative slope may result from non-linear
relations between other parameters correlated with the stellar age. To clarify
how this could have originated from LASSO interpretation, we propose a
simplified model to evaluate its performance in the next section.

To assess the uncertainty associated with the estimated coefficients in the
regression results reported in Table 5, we employ a bootstrap resampling
method. This approach involves generating multiple bootstrap samples by
repeating the same LASSO regression with cross-validation. We calculate the
standard errors of the slopes by computing the standard deviation and across
the iterations. Our findings reveal that the standard deviation on
$\alpha_{i}$ at a scale of 1 kpc is below 0.1 for all predictors. Similarly,
the standard deviations are below 0.01 at 175 pc scale.

####  6.3.2 Simplified model

The LASSO regression develops a multifactor model and the method does not
isolate the drivers of the log(FUV/H$\alpha$) ratio automatically. However, it
does show some factors have a stronger influence on the variation (large
$\alpha_{i}$) than others. Here, we develop a simple model that explains most
of the variation in the FUV/H$\alpha$ ratio.

Figure 15: Log of FUV/H$\alpha$ versus the Balmer decrement E(B-V) in 1 kpc
size hexagonal apertures. The reddening is measured using MUSE/VLT
observations in different galaxies. The colorbar represents the CO(2-1) line
integrated intensity. The figure shows that $\log(\mathrm{FUV/H\alpha})$
depends on both $E(B-V)$ and $I_{\mathrm{CO}}$.

The strongest factor in the analysis is dust attenuation as measured by the
color excess $E$(B-V), suggesting the observed variations in FUV/H$\alpha$
could be driven by attenuation alone. To delve deeper and find a simplified
model to describe the log ratio, we run a model that includes only dust
reddening, E(B-V). This resulted in dust reddening coefficients of
approximately $-1.7$ at 175 pc scale. The goodness of fit, represented by
$R^{2}$, yielded values of 0.5. This indicates that dust reddening alone can
account for roughly half of the variation in the log ratio. Notably, a
standalone model of CO can predict the log ratio as effectively as a single
reddening component, with an $R^{2}$ of 0.4. However, the coefficient of the
CO component is $-0.4$. Figure 15 illustrates that a lower
$\log(\mathrm{FUV/H}\alpha)$ ratio (indicating higher attenuation) corresponds
to higher CO intensity, particularly in regions containing dense, dusty, and
massive star-forming regions. This trend holds not only on the 1 kpc scale, as
shown in Figure 15, but also at the 175 pc resolution. This suggests that dust
and molecular emission are tracing each other. This supposition is borne out
through measuring a significant correlation between CO emission and $E(B-V)$
on a kpc scale, supported by a Spearman rank coefficient of $\rho\approx
0.75$. This correlation remains robust even when we enhance the resolution to
175 pc, although it exhibits notable variations across diverse environments.

Table 6:  Regression coefficients for simplified models predicting $\log(\mathrm{FUV/H\alpha})$. Parameters | Normalization | $\alpha_{i}$ | $R^{2}$
---|---|---|---
Observed FUV/H$\alpha$ (175pc scale) |  |  |
$E(B-V)$ | 0.17$~{}\mathrm{mag}$ | $-1.68\pm 0.01$ | 0.50
$\alpha_{0}$ | 1.95 |
$I_{\mathrm{CO}}$ | 2.51$~{}\mathrm{K~{}km~{}s^{-1}}$ | $-0.41\pm 0.01$ | 0.41
$\alpha_{0}$ | 1.94 |
$E(B-V)$ | 0.17$~{}\mathrm{mag}$ | $-1.22\pm 0.01$ | 0.51
$I_{\mathrm{CO}}$ | 2.51$~{}\mathrm{K~{}km~{}s^{-1}}$ | $-0.15\pm 0.01$
$\alpha_{0}$ | 1.95 |
$E(B-V)$ | 0.17 $\mathrm{mag}$ | $-1.38\pm 0.01$ | 0.56
EW(H$\alpha$) | 15 Å | $-0.17\pm 0.01$
$\alpha_{0}$ | 1.94 |  |
$E(B-V)$ | 0.17 $\mathrm{mag}$ | $-1.38\pm 0.01$ | 0.64
EW(H$\alpha$) | 15 Å | $-0.34\pm 0.01$
Age | 615 Myr | $-0.34\pm 0.01$
$\alpha_{0}$ | 1.93 |
Attenuation-corrected FUV/H$\alpha$ (1 kpc scale) |  |  |
Age | 840$~{}\mathrm{Myr}$ | $0.84\pm 0.02$ | 0.49
$\Sigma_{*}$ | 168$\,M_{\odot}~{}\mathrm{pc}^{-2}$ | $-1.39\pm 0.02$
$[Z/H]$ | -0.19 dex | $0.33\pm 0.02$
$\alpha_{0}$ | 2.98 |  |

Furthermore, we find that the combination of reddening and H$\alpha$ EW led to
a better prediction of the model, increasing $R^{2}$ to 0.56. Interestingly, a
model that includes only reddening and ages behaves very similarly, with the
same $R^{2}$ as the previous model, indicating that H$\alpha$ EW effectively
traces the age of the stellar population. We note that even in the simplified
models, the coefficient for age remains negative. Finally, including both age
and H$\alpha$ EW in the model increased both $R^{2}$ values to over 0.6,
making them very similar to our comprehensive model. This also suggests that
the inclusion of stellar mass, metallicity and different environmental factors
has a negligible effect on controlling the log ratio.

Based on the proposed simplified model of a single dust reddening component,
we have successfully predicted the FUV/H$\alpha$ ratio with an $R^{2}\sim 0.5$
at scales of 1 kpc and 175 pc, respectively. Subsequently, the simplified
model exhibits higher scatter, approximately 0.2 to 0.25 dex, due to the
inclusion of fewer parameters. However, the collinearity among various
parameters complicates the interpretation of the individual effects of each
parameter on the log ratio. We summarized the simplified models and their
coefficients in Table 6.

####  6.3.3 Dust attenuation correction to FUV/H$\alpha$

Figure 16: The residual values of log FUV/H$\alpha$ versus age, corrected for
internal attenuation and adjusted by removing the influences of stellar mass
and metallicity. The black contours show a Kernel Density Estimation of the
observed data dataset, focusing on the data distribution between the 16th to
84th percentiles. This relation includes measurements from only the five
galaxies with NUV data available, which are necessary to measure the slope of
the UV continuum and hence the FUV attenuation correction.

As demonstrated in the previous section, dust attenuation is the primary
factor governing variations in the uncorrected log FUV/H$\alpha$. In this
section, we attempt to correct FUV/H$\alpha$ for dust attenuation and explore
which parameter(s) exert the most influence on this log ratio.

Using the available NUV AstroSat observations for five of our targets, we
first measure the slope of the UV continuum ($\beta$) to correct the FUV
emission for internal attenuation. Following Boquien et al. (2012), we define
$\beta$ as follows:

$\beta=\frac{\text{log}({F}_{\text{FUV}}/{F}_{\text{NUV}})}{\text{log}(\lambda_{\text{FUV}}/\lambda_{\text{NUV}})}-2$
(9)

with $F$ representing the flux density and $\lambda$ the central wavelength of
the UVIT filter. We use a prescription following Boquien et al. (2012) to
estimate $A_{FUV}$ using our measured $\beta$ values, thereby correcting the
FUV emission. We also use the Balmer decrement measurements and $E(B-V)$
presented previously to correct the H$\alpha$ emission, based on a typical
value of the total-to-selective value $R_{V}=3.1$ (Calzetti et al., 2000;
Belfiore et al., 2022). After correcting both FUV and H$\alpha$, we measure
the log ratio using 1 kpc hexagonal apertures and measure how well the
variation of ratio is predicted by the luminosity-weighted age, surface mass
density, and metallicity of the stellar population. We found that this model
can describe the variation of the dust-attenuation corrected log ratio with an
$R^{2}=0.49$. Our results show coefficients of 0.84 for age, $-1.39$ for the
stellar mass surface density, and 0.33 for the stellar metallicity, along with
an intercept of $\alpha_{0}=2.98$ (c.f. Table 6). Similar to the approach used
in Dey et al. (2019), we perform a partial regression and define the residual
of this parameter ($\Delta\log\mathrm{FUV/H}\alpha$ corrected), as:

$\displaystyle\Delta\,\log\,(\text{FUV}/\text{H}\alpha)_{\text{corr}}$
$\displaystyle=\log\,(\text{FUV}/H\alpha)_{\text{corr}}-\alpha_{0}$ (10)
$\displaystyle\quad-(-1.39\log\Sigma_{*}-\langle\Sigma_{*}\rangle)$
$\displaystyle\quad-(0.33[Z/H]-\langle[Z/H]\rangle)$

where $\langle\Sigma_{*}\rangle$ and $\langle[Z/H]\rangle$ are median
normalization factors, and $\alpha_{0}$ is the intercept reported in Table 6
for the dust-attenuation corrected log ratio. This relation effectively
removes the influence of stellar mass surface density and metallicity from the
dust-attenuated log ratio and highlights its variation with age. Figure 16
shows the positive correlation between ($\Delta\log\mathrm{FUV/H}\alpha$
corrected) and stellar age across five galaxies. FUV emission can come from
less massive stars with longer lifespans, unlike the more massive, short-lived
stars (typically less than 10 million years) responsible for H$\alpha$
emission (Kennicutt & Evans, 2012). Hence, the ratio should increase with the
average age of a galaxy’s stellar population, which in turn depends on the
galaxy’s star formation history.

The star formation histories, whether constant or bursty, will further
influence the evolution of the log ratio. After a burst of star formation, the
FUV/H$\alpha$ ratio will quickly rise, due to the sudden drop in H$\alpha$
emission. Thereafter, the ratio will decrease again, moving towards an
equilibrium value as the FUV-emitting stars eventually die. The models of
Mehta et al. (2023) show that the log of the FUV/Ha ratio will reach an
equilibrium state $100-200$ million years after the onset of a constant star
formation history (see also the model predictions from Weisz et al. (2012) for
log M${}_{\star}\sim 10$ galaxies). The ages shown in Figure 16 shows the
luminosity-weighted measurement of ages from Emsellem et al. (2022), which
trace the integrated star formation histories and show longer absolute
timescales (log(age)$\sim 9$).

##  7 Conclusion

We present the AstroSat UVIT observations of 31 massive spiral nearby galaxies
spanning over an order of magnitude in stellar mass. These galaxies are part
of the PHANGS Survey of nearby galaxies which is synthesizing a broad range of
multiwavelength data, including the ALMA and VLT/MUSE data that support this
work. UVIT data improves over previous UV observations with $\sim
1.4^{\prime\prime}$ resolution enabling studies of young stellar populations
and dust attenuation on $<200$ pc scales. This atlas contains data available
from dedicated observations collected as part of PHANGS as well as a uniform
reduction of the available data from the AstroSat archive. Hence, the atlas
contains data from galaxies in one to nine UV bands covering wavelengths from
1480 Å to 2790 Å. These data can provide new resolved observational
constraints to challenging questions such as attenuation curve, dust
attenuation, and UV SFR for 30% of the full PHANGS sample. The PHANGS-AstroSAT
atlas is available
online555https://www.canfar.net/storage/vault/list/phangs/RELEASES/PHANGS-
AstroSat/v1p0. This paper describes:

  1. 1.

Observations and quality assessment: The PHANGS-AstroSat atlas targets low-
inclination ($i<75^{\circ}$) massive ($M_{\star}>10^{9.57}~{}M_{\odot}$,
actively star-forming galaxies in the local universe (D$<22\,$Mpc). As
described in Section 2, we observe 11 galaxies for $\sim 3$ ks each in the
F148W band. For these and 20 other targets available in the archive, we apply
the standard UVIT data calibration processes in CCDLAB (Section 3; Postma &
Leahy, 2017). We found that the pipeline reduction yields a typical resolution
of 1.4″ at the FUV and 1.2″ at the NUV band. We provide science-ready maps of
our targets after background subtraction and foreground extinction
corrections. The UV background emission is $\sim 10^{-19}$ erg s-1
cm${}^{-2}\,$Å-1 for most of our targets at $\lambda=1480$ Å. We assess the
quality of our data by comparing AstroSat UVIT observations with GALEX maps
from Leroy et al. (2019). The comparison of our data set with GALEX
observations shows good flux scale agreement ($<10\%$ variations), as
illustrated in Figs. 6, 7.

  2. 2.

XUV disks: In Section 4 we present NGC 6744 as a new Type 1 XUV disk candidate
and IC 5332 as a candidate for a Type 2 XUV disk galaxy. NGC 6744 shows a very
large scale length in FUV and we identify two bright spiral arms beyond 1.5
$R_{25}$, without any R-band or H$\alpha$ emission. We did not find convincing
evidence other candidates that were previously unknown though we confirm all
previous classifications (Thilker et al., 2007).

  3. 3.

Structure of CO and FUV emission: In Section 5, we quantify the clumping and
smoothness of the FUV and CO emission using two non-parametric scalar
measurements: the clumping factor ($c$) and the Gini coefficient ($G$). Using
more than 4000 1-kpc hexagonal apertures in our targets, we find
$c_{\mathrm{CO}}=1.7$ versus a median of c${}_{\mathrm{FUV}}=1.3$ ,
illustrating the FUV emission is less clumped than the CO. In general, we find
that bars are the most clumped environments. Although the median clumping
factor from FUV and CO emission is similar across different environments, CO
clumping factors are slightly higher than FUV except in the center and spiral
arms. Furthermore, clumps of UV bright emission, which could have H II region
counterparts, have the most effect on increasing the clumping factor. The non-
parametric Gini coefficient also predicts a smoother distribution for FUV
emission compared to CO except in the center, which is in agreement with
clumping factor results.

  4. 4.

FUV to H$\alpha$ ratio: In pairing UVIT and MUSE H$\alpha$ maps, we used 1 kpc
size apertures and see a $\sim$2 dex difference in non-dust-corrected FUV and
H$\alpha$ luminosity (Section 6). We find $\log_{10}(\mathrm{FUV/H}\alpha)<2$
the centers and bars. The log ratio at the center of AGN galaxies is 65
percent lower than that in normal star-forming galaxies. The radial profile of
$\log_{10}(\mathrm{FUV/H}\alpha)$ is approximately flat outside centers, and
shows small variation from 1.7 to 2.1. We propose an empirical model to
predict the observed log ratio in our sample. The model considers how several
parameters predict FUV/H$\alpha$ including CO molecular gas emission,
$E(B-V)$, star formation history parameters (mass, age, metallicity), and the
equivalent width of H$\alpha$. A full model is able to explain the variation
of $\log_{10}(\mathrm{FUV/H}\alpha)$ at resolutions of 175 pc and 1 kpc,
achieving approximately $R^{2}\sim 0.65$. In evaluating different versions of
the model, we argue that dust attenuation is the dominant factor controlling
the variation of the log ratio in star forming disk galaxies. Finally, having
corrected both FUV and H$\alpha$ emissions for internal dust attenuation, we
find that when the influences of stellar mass and metallicity are excluded
from the FUV/H$\alpha$ log ratio, a positive correlation emerges between this
ratio and the age of the stellar population.

The new perspective on star-forming complexes offered by AstroSat UVIT, when
combined with our other $\sim 1\arcsec$ resolution surveys such as PHANGS-ALMA
and PHANGS-MUSE, provides fresh insights on UV emission in nearby galaxies.
Future work will be able to investigate hybrid UV+IR star formation rate
calibrations, the attenuation curve, and the relation of the excess of dust to
UV luminosity versus the stellar color (IRX-$\beta$) at sub-kpc parsec scales
over a broad population of nearby galaxies.

Figure 17: Same as the Fig 5. Figure 18: Same as the Fig 5.

##  Appendix A
Radial profiles

Figures 17,18 present the radial profiles for all PHANGS targets that are not
presented in Fig. 5.

We are grateful for the comments of an anonymous referee that improved the
quality of this paper. This work was conducted as part of the PHANGS
collaboration. This publication uses the data from the AstroSat mission of the
Indian Space Research Organisation (ISRO), archived at the Indian Space
Science Data Centre (ISSDC). HH, ER, JN, EWK, and HC acknowledge the support
of the Natural Sciences and Engineering Research Council of Canada (NSERC),
funding reference number RGPIN-2017-03987, and the Canadian Space Agency
funding reference numbers SE-ASTROSAT19, 22ASTALBER. EWK acknowledges support
from the Smithsonian Institution as a Submillimeter Array (SMA) Fellow and the
Natural Sciences and Engineering Research Council of Canada. RSK and SCOG
acknowledge funding from the European Research Council via the ERC Synergy
Grant “ECOGAL” (project ID 855130), from the German Excellence Strategy via
the Heidelberg Cluster of Excellence (EXC 2181 - 390900948) “STRUCTURES”, and
from the German Ministry for Economic Affairs and Climate Action in project
“MAINN” (funding ID 50OO2206). The team in Heidelberg also thanks for
computing resources provided by The Länd and DFG through grant INST 35/1134-1
FUGG and for data storage at SDS@hd through grant INST 35/1314-1 FUGG. MB
gratefully acknowledges support from the ANID BASAL project FB210003 and from
the FONDECYT regular grant 1211000. JMDK gratefully acknowledges funding from
the DFG through an Emmy Noether Research Group (grant number KR4801/1-1), as
well as from the European Research Council (ERC) under the European Union’s
Horizon 2020 research and innovation programme via the ERC Starting Grant
MUSTANG (grant agreement number 714907). COOL Research DAO is a Decentralized
Autonomous Organization supporting research in astrophysics aimed at
uncovering our cosmic origins. MC gratefully acknowledges funding from the DFG
through an Emmy Noether Research Group (grant number CH2137/1-1). HAP
acknowledges support by the National Science and Technology Council of Taiwan
under grant 110-2112-M-032-020-MY3. KK, OE gratefully acknowledge funding from
the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in the
form of an Emmy Noether Research Group (grant number KR4598/2-1, PI Kreckel)
and the European Research Council’s starting grant ERC StG-101077573 (“ISM-
METALS”). KG is supported by the Australian Research Council through the
Discovery Early Career Researcher Award (DECRA) Fellowship (project number
DE220100766) funded by the Australian Government. KG is supported by the
Australian Research Council Centre of Excellence for All Sky Astrophysics in 3
Dimensions (ASTRO 3D), through project number CE170100013. J.K. is supported
by a Kavli Fellowship at the Kavli Institute for Particle Astrophysics and
Cosmology (KIPAC). HH acknowledges the use of ChatGPT to refine the English-
language presentation of the ideas in this paper.

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