# The Atacama Cosmology Telescope: Microwave Intensity and Polarization Maps
of the Galactic Center

Yilun Guan Department of Physics and Astronomy, University of Pittsburgh,
Pittsburgh, PA, USA 15260<EMAIL_ADDRESS>Susan E. Clark Institute for
Advanced Study, 1 Einstein Drive, Princeton, NJ 08540, USA  Brandon S. Hensley
Department of Astrophysical Sciences, Peyton Hall, Princeton University,
Princeton, NJ, USA 08544  Patricio A. Gallardo Department of Physics, Cornell
University, Ithaca, NY 14853, USA  Sigurd Naess Center for Computational
Astrophysics, Flatiron Institute, New York, NY, USA 10010  Cody J. Duell
Department of Physics, Cornell University, Ithaca, NY 14853, USA  Simone Aiola
Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA
10010  Zachary Atkins Joseph Henry Laboratories of Physics, Jadwin Hall,
Princeton University, Princeton, NJ, USA 08544  Erminia Calabrese School of
Physics and Astronomy, Cardiff University, The Parade, Cardiff, CF24 3AA, UK
Steve K. Choi Department of Physics, Cornell University, Ithaca, NY 14853, USA
Department of Astronomy, Cornell University, Ithaca, NY 14853, USA  Nicholas
F. Cothard Department of Applied and Engineering Physics, Cornell University,
Ithaca, NY 14853, USA  Mark Devlin Department of Physics and Astronomy,
University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA, USA 19104
Adriaan J. Duivenvoorden Joseph Henry Laboratories of Physics, Jadwin Hall,
Princeton University, Princeton, NJ, USA 08544  Jo Dunkley Joseph Henry
Laboratories of Physics, Jadwin Hall, Princeton University, Princeton, NJ, USA
08544  Department of Astrophysical Sciences, Peyton Hall, Princeton
University, Princeton, NJ, USA 08544  Rolando Dünner  Instituto de Astrofísica
and Centro de Astro-Ingeniería, Facultad de Física, Pontificia Universidad
Católica de Chile, Av. Vicuña Mackenna 4860, 7820436, Macul, Santiago, Chile
Simone Ferraro Lawrence Berkeley National Laboratory, One Cyclotron Road,
Berkeley, CA 94720, USA  Berkeley Center for Cosmological Physics, UC
Berkeley, CA 94720, USA  Matthew Hasselfield Center for Computational
Astrophysics, Flatiron Institute, New York, NY, USA 10010  John P. Hughes
Department of Physics and Astronomy, Rutgers, the State University of New
Jersey, 136 Frelinghuysen Road, Piscataway, NJ 08854-8019, USA  Brian J.
Koopman Department of Physics, Yale University, New Haven, CT 06520, USA
Arthur B. Kosowsky Department of Physics and Astronomy, University of
Pittsburgh, Pittsburgh, PA, USA 15260  Mathew S. Madhavacheril Perimeter
Institute for Theoretical Physics, 31 Caroline Street N, Waterloo ON N2L 2Y5
Canada  Jeff McMahon  Department of Astronomy, University of Chicago, Chicago,
IL USA  Department of Physics, University of Chicago, Chicago, IL 60637, USA
Kavli Institute for Cosmological Physics, University of Chicago, 5640 S. Ellis
Ave., Chicago, IL 60637, USA19  Enrico Fermi Institute, University of Chicago,
Chicago, IL 60637, USA  Federico Nati Department of Physics, University of
Milano-Bicocca, Piazza della Scienza 3, 20126 Milano (MI), Italy  Michael D.
Niemack Department of Physics, Cornell University, Ithaca, NY 14853, USA
Department of Astronomy, Cornell University, Ithaca, NY 14853, USA  Kavli
Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, NY
14853, USA  Lyman A. Page Joseph Henry Laboratories of Physics, Jadwin Hall,
Princeton University, Princeton, NJ, USA 08544  Maria Salatino  Physics
Department, Stanford University, Stanford 94305 CA, USA  Kavli Institute for
Particle Astrophysics and Cosmology, Stanford 94305 CA, USA  Emmanuel Schaan
Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720,
USA  Berkeley Center for Cosmological Physics, UC Berkeley, CA 94720, USA
Neelima Sehgal Physics and Astronomy Department, Stony Brook University, Stony
Brook, NY 11794  Cristóbal Sifón Instituto de Física, Pontificia Universidad
Católica de Valparaíso, Casilla 4059, Valparaíso, Chile  Suzanne Staggs Joseph
Henry Laboratories of Physics, Jadwin Hall, Princeton University, Princeton,
NJ, USA 08544  Eve M. Vavagiakis  Department of Physics, Cornell University,
Ithaca, NY 14853, USA  Edward J. Wollack NASA / Goddard Space Flight Center,
Greenbelt, MD, 20771, USA  Zhilei Xu Department of Physics and Astronomy,
University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA 19104, USA
MIT Kavli Institute, Massachusetts Institute of Technology, 77 Massachusetts
Avenue, Cambridge, MA 02139, USA

###### Abstract

We present arcminute-resolution intensity and polarization maps of the
Galactic center made with the Atacama Cosmology Telescope (ACT). The maps
cover a 32 deg2 field at 98, 150, and 224 GHz with $|l|\leq 4^{\circ}$,
$|b|\leq 2^{\circ}$. We combine these data with Planck observations at similar
frequencies to create coadded maps with increased sensitivity at large angular
scales. With the coadded maps, we are able to resolve many known features of
the Central Molecular Zone (CMZ) in both total intensity and polarization. We
map the orientation of the plane-of-sky component of the Galactic magnetic
field inferred from the polarization angle in the CMZ, finding significant
changes in morphology in the three frequency bands as the underlying dominant
emission mechanism changes from synchrotron to dust emission. Selected
Galactic center sources, including Sgr A*, the Brick molecular cloud
(G0.253+0.016), the Mouse pulsar wind nebula (G359.23-0.82), and the Tornado
supernova remnant candidate (G357.7-0.1), are examined in detail. These data
illustrate the potential for leveraging ground-based Cosmic Microwave
Background polarization experiments for Galactic science.

##  1 Introduction

Some of the most extreme interstellar environments in the Galaxy are found in
the Galactic center (e.g., Battersby et al., 2020). The inner $\sim 500$ pc of
the Milky Way is home to the Central Molecular Zone (CMZ), the densest
concentration of molecular gas in the Galaxy, with a mean density of $\sim
10^{4}$ cm-3 (Güsten, 1989; Ferrière et al., 2007). The surface density of
dense gas greatly exceeds that found in nearby star-forming molecular clouds.
Standard prescriptions predict that the CMZ should be an extremely active site
of star formation, and yet the observed star formation rate is low; by some
estimates, an order of magnitude or more below predictions (e.g., Longmore et
al., 2013; Barnes et al., 2017; Nguyen et al., 2021, and references therein).

The apparently inefficient star formation in the CMZ makes this region an
ideal testbed for star formation theories, with many factors proposed to
explain the observations. These include the strong magnetic field in the
Galactic center (Crutcher et al., 1996; Chuss et al., 2003; Ferrière, 2011),
the rate of mass inflow to the CMZ (Sormani & Barnes, 2019), the strength and
compressibility of turbulence in the CMZ (Federrath et al., 2017), and the
possibility that we are observing a relatively quiescent period between
episodic bursts of star formation (Kruijssen et al., 2014; Krumholz &
Kruijssen, 2015). Furthermore, the CMZ is in some respects a nearby analogue
of nuclear rings in other galaxies, including high-redshift starbursts. The
Galactic center is thus an opportunity for up-close study of the physics
relevant to the cosmic history of star formation (Kruijssen & Longmore, 2013;
Ginsburg et al., 2019).

The magnetic field in the vicinity of the Galactic center has long been
studied with radio polarimetry (Ferrière, 2009; Morris, 2015). The so-called
non-thermal radio filaments – thin strands of radio-frequency emission – were
some of the earliest observations to shed light on the magnetic field
structure toward the Galactic center. The non-thermal radio filaments are, for
the most part, strikingly perpendicular to the Galactic plane, and the
intrinsic magnetic field inferred from the Faraday de-rotated polarized
synchrotron emission tends to lie parallel to the long axis of these filaments
(Morris & Yusef-Zadeh, 1985; Yusef-Zadeh & Morris, 1987a; Lang et al., 1999).

Polarized dust emission provides a complementary means of probing the magnetic
field structure in the CMZ. Interstellar dust grains emit partially polarized
thermal radiation because they are aspherical and preferentially align their
short axes parallel to the ambient magnetic field (Purcell, 1975). The
polarization angle of the dust emission is thus a line-of-sight integrated
probe of the plane-of-sky component of the magnetic field orientation.
Polarized dust emission has been measured at high angular resolution in small
regions toward a number of CMZ molecular clouds (e.g., Novak et al., 2000,
2003; Chuss et al., 2003; Matthews et al., 2009; Roche et al., 2018).
Recently, the balloon-borne experiment PILOT presented a 240 $\mu$m map of the
polarized dust emission over the entire CMZ region at $2.2^{\prime}$
resolution (Mangilli et al., 2019), along with comparisons to the lower-
resolution 353 GHz polarization data measured by the Planck satellite (Planck
Collaboration Int. XIX, 2015).

The Atacama Cosmology Telescope (ACT) measures the polarized microwave sky
with higher angular resolution than the Planck satellite and greater
sensitivity on small scales. In this paper we present new dedicated maps of
the Galactic center in total intensity and linear polarization in three ACT
frequency bands. We combine the ACT data with Planck data to augment the map
sensitivity on larger angular scales. The frequency coverage of the maps
presented here probe a range of physical emission mechanisms, enabling a
comprehensive view of the Galactic center environment. In polarization these
maps probe both polarized dust and synchrotron emission, and in total
intensity the maps additionally show features from free-free emission and
molecular line emission from transition frequencies that fall within the ACT
passbands. These data illustrate the potential of sensitive CMB polarization
experiments for Galactic science.

We describe the ACT observations in Section 2 and the mapmaking and Planck
coadd procedures in Section 3. In Sections 4 and 5, we present the maps in
total intensity and polarization, respectively, and discuss derived properties
including emission mechanisms, magnetic field orientation, and polarization
fraction. In Section 6, we identify notable Galactic center objects and
compare to observations at other frequencies. We conclude in Section 7.

##  2 Observations

ACT is a 6-meter off-axis Gregorian telescope located at an elevation of 5190
m on Cerro Toco in the Atacama Desert in Chile (Fowler et al., 2007; Thornton
et al., 2016). ACT scans the millimeter-wave sky with arcminute resolution,
complementary to the full-sky lower angular-resolution measurements from
satellite missions such as the Wilkinson Microwave Anisotropy Probe (WMAP;
Bennett et al., 2013) and Planck (Planck Collaboration I, 2014).

In 2019, the target ACT observating fields were expanded to include the
Galactic center region. Between June 6 and November 29, 2019 a total duration
of $\sim 35$ hours of data were taken with three Advanced ACTPol dichroic
detector arrays PA4, PA5, and PA6 (Henderson et al., 2016; Ho et al., 2017;
Choi et al., 2018), at three frequency bands f090, f150, and f220 centered
roughly at 98 GHz, 150 GHz, and 224 GHz, respectively. The beam full-width-
half-maximum (FWHM) at each band is $2.0^{\prime}$, $1.4^{\prime}$, and
$1^{\prime}$, respectively. The observation field extends roughly from
$-89\degree$ to $-97\degree$ in declination and $-33\degree$ to $-25\degree$
in right ascension. This study focuses specifically on a 32 deg2 field near
the CMZ with Galactic longitude $|l|\leq 4^{\circ}$ and Galactic latitude
$|b|\leq 2^{\circ}$.

In this paper we present the maps made using the nighttime observations only,
which constitute roughly two-thirds of the total data collected. The daytime
observations are affected by a time-dependent beam deformation due to the
heating from the Sun that is challenging to correct for in detailed high-
resolution maps, and hence those data are excluded from this analysis.
Correcting for this beam deformation will be a subject of future study, and
the daytime observations may be included for future versions of these maps.

##  3 Mapmaking

###  3.1 Mapmaking with ACT

Figure 1: Comparison between Planck only maps (left column) and ACT+Planck
coadded maps (right column) in total intensity. Rows from top to bottom
correspond to f090, f150, and f220 respectively. Each map extends from
$|l|\leq 2^{\circ}$, $|b|\leq 1^{\circ}$ and is plotted on a logarithmic color
scale from 0.3–30 MJy sr-1 for f090 and f150, and from from 3–100 MJy sr-1 for
f220.

Figure 2: A side-by-side comparison between Planck only (left) and the
ACT+Planck coadded (right) for f150 in polarized intensity.

Figure 3: Polarization maps in Stokes $Q$ (left column) and $U$ (right column)
in Galactic coordinates and using the IAU polarization convention. Top to
bottom are the f090, f150, and f220 maps, respectively.

The instrument records observations in the form of time-ordered data (TOD) in
units of $\sim$ 10 minutes. We largely follow the mapmaking pipeline as
described in Aiola et al. (2020) with a few key differences, as we briefly
summarize below.

First, we cut bad samples affected by glitches in each TOD. To prevent bright
sources in the Galactic center region from being mistaken for glitches, we
mask sources brighter than 5 mK with a radius of $3^{\prime}$ prior to
applying the glitch finder. We note that this mask is only applied when
identifying glitches and not used during mapmaking. Timestreams with outlying
statistical properties in terms of noise levels and optical responsiveness are
then flagged and removed from the analysis. We further split the dataset into
two independent subsets for each frequency band and detector array
respectively, resulting in 12 datasets in total. We then obtain the sky maps
for each dataset by solving the mapping equation,

$d=Pm+n,$ (1)

for a set of Stokes parameters ($I$, $Q$, $U$), where $d$ is the pre-processed
time-streamed data, $P$ is the pointing matrix, $m$ is the output map of
interest, and $n$ is the noise model. This equation yields a maximum
likelihood solution for $m$ by inverting

$(P^{T}N^{-1}P)m=P^{T}N^{-1}d,$ (2)

where $N$ is the detector-detector noise covariance.

There are two notable differences between the pipeline used in this study and
that presented in Aiola et al. (2020). First, we have adopted a new
calibration method that improves gain stability and reduces biases from
thermal contamination as compared to the method in ACT Data Release (DR) 4
(Aiola et al., 2020) (see Appendix A for more details). The second difference
relates to the handling of point sources and extended hot regions that are
common in the Galactic center region but uncommon in CMB fields. Directly
applying the mapmaking pipeline in ACT DR4 leads to stripes around the bright
sources caused by model errors as explained in Næss (2019). This happens for
two reasons. (1). A pixelated map does not capture the sub-pixel behavior of
the sky. These residuals are proportional to the gradient of the signal across
a pixel and are often fractionally small. However, if the sky is sufficiently
bright, such as in the brightest parts of the Galactic center, they can still
end up being large in absolute terms. Since the map $m$ in Equation 1 cannot
capture these residuals, the model forces them to be interpreted as part of
the noise $n$. (2). The correlated noise model used in the mapmaker induces a
non-local response to the sub-pixel noise, leading to biases on the scale of
the noise correlation length. To avoid this problem, we first identify the
regions that source the strongest model errors, namely the brightest parts of
the Galaxy, and then eliminate model errors in these pixels by allocating an
extra degree of freedom for each sample that hit them, as described in Næss
(2019).

A caveat concerning these maps is that the bright parts of the Galaxy were not
masked when building the noise model $N$. The noise model estimator assumes
that the time-ordered data is noise-dominated ($d\approx n$), and uses this to
measure the noise covariance directly from $d$. This breaks down when the
telescope scans across the Galactic center, resulting in an overestimate of
the noise amplitude especially on smaller scales. This has two consequences:
(1). The data are weighted sub-optimally in Equation (2), resulting in
slightly higher noise. Since the maps are strongly signal-dominated, this can
be ignored. (2). Because the noise model is contaminated by the same signal it
is applied to, there is a small loss in signal power in the maps; pixels where
noise happens to add constructively to the signal have more power in $d$ than
in pixels where they partially cancel. Since we use inverse variance
weighting, the latter are up-weighted compared to the former. The size of this
effect is limited because the problematically bright regions make up a small
fraction of the total samples used to build $N$. We have not measured the
precise size of this effect, but estimate it to be $\lesssim 1\%$ based on
experience with other high-S/N regions, and hence we expect it to have
negligible impact on the interpretation of the maps in this paper. This
deficiency will be rectified in the upcoming ACT DR6 maps.

A final known issue requiring mitigation is temperature-to-polarization
(T-to-P) leakage. ACT typically scans a given region of the sky both during
its rising and setting. As the Galactic center region is at relatively low
declination, rising scans and setting scans are poorly cross-linked (for more
information on ACT scan strategy see Stevens et al. (2018)). Furthermore, the
ACT beam is known to leak temperature to polarization at the percent level.
This beam leakage effect averages down effectively in the nominal CMB maps
which are well cross-linked, but in the Galactic center region the T-to-P
leakage is apparent at a $\sim 1$–$2\%$ level that contaminates the
polarization maps in the bright Galactic plane. To reduce the contamination
from beam leakage, we build a 2D leakage beam model for each dataset using
observations of Uranus made in the same observation year (2019), and deproject
the expected T-to-P leakage from the polarization maps in each dataset (see
Appendix B for more details).

Following these methods, we produced two-way split maps of the Galactic center
region at $0.5^{\prime}$ resolution in Plate Carreé (CAR) projection for each
frequency band (f090, f150, f220) and detector array (PA4, PA5, PA6) resulting
in a total of 12 maps.

###  3.2 Coadd with Planck

Figure 4: Multi-frequency view of the Galactic center region in both total
intensity (upper panel) and polarized intensity (lower panel). Red, green, and
blue correspond to f090, f150, and f220, respectively. In the upper panel, the
maps are scaled logarithmically from 0.2 to 2 MJy sr-1 for f090, from 0.214 to
2.14 MJy sr-1 for f150, and from 1.15 to 10.15 MJy sr-1 for f220. The
polarization maps shown in the lower panel are first smoothed with a Gaussian
kernel (FWHM$=3.5^{\prime}$) and then scaled linearly from 0 to 1 MJy sr-1 for
f090, to 1.79 MJy sr-1 for f150, and to 8.2 MJy sr-1 for f220.

The large angular scales in the ACT maps are affected by atmospheric noise
contamination and complicated co-variances at large scales. These modes can be
recovered, however, by coadding ACT maps with maps from Planck, which dominate
the signal-to-noise at large scales $\ell\lesssim 1000$. In particular, we
have used a similar algorithm as presented in ACT DR5 (Naess et al., 2020), in
combination with the Planck High Frequency Instrument (HFI) maps processed
through the NPIPE pipeline (Planck Collaboration Int. LVII, 2020), which are
two-way split maps featuring improved noise level and systematic control as
compared to the previous Planck data releases.

Figure 5: Upper panel: Known radio sources found in the Galactic center
region. The background image shows a zoomed-in view of the multi-frequency
3-color image presented in the upper panel of Figure 4. Lower panel:
Annotations of selected radio and dusty sources in the multi-frequency
polarized intensity image (presented in the lower panel of Figure 4). The maps
are smoothed with FWHM=$2^{\prime}$ to make objects more visible.

As the coadding algorithm is presented in detail in Naess et al. (2020), we
only briefly summarize the steps and note differences here. First, we re-
project the Planck maps and noise models from HEALPix111http://healpix.sf.net
(Górski et al., 2005) projection with $N_{\rm side}=2048$ into the ACT
Galactic center observation footprint in CAR projection with $0.5^{\prime}$
pixelization using bi-cubic interpolation. We have used the same passbands as
in Naess et al. (2020) and similarly matched the Planck 100 GHz maps with ACT
f090 maps, 143 GHz with ACT f150, 217 GHz with ACT f220. This process assumes
that the ACT and Planck passbands are equivalent. We note that this introduces
additional scale dependence to the effective band-centers (Naess et al.,
2020). This is expected to have negligible impact on the results presented
here but is relevant for component-separation analysis, which will be the
subject of follow-up work.

We then solve for the maximum likelihood coadded maps using a block-diagonal
equation

$\begin{pmatrix}m_{0}\\\ m_{1}\\\ \vdots\end{pmatrix}=\begin{pmatrix}B_{0}\\\
B_{1}\\\ \vdots\end{pmatrix}B_{\rm out}^{-1}m+n,$ (3)

where $m_{i}$ refers to each individual map, $B_{i}$ refers to its
corresponding beam transfer function, $m$ refers to the final coadded map with
a desired beam $B_{\rm out}$, which is the ACT beam in this case. $n$ refers
to the map noise, which is assumed to be Gaussian and block-diagonal across
individual maps, i.e., individual maps have independent noise realizations. Of
the noise models presented in Naess et al. (2020), we have adopted the
constant correlation noise model, though the choice makes little difference in
practice as we are considering only a small patch of sky with close to uniform
noise levels. We invert Equation (3) to find a maximum-likelihood solution to
the coadded map at f090 and f150, respectively. Because the PA4 array had a
poor detector yield over the course of the observation, maps at f220 are
treated differently from the other two frequencies. The resulting excess noise
in the ACT f220 maps leads to a lack of convergence when solving for a coadded
map through a maximum likelihood approach. Therefore, we instead perform a
straightforward inverse-variance weighting in Fourier space to obtain the
coadded map at f220 (see Appendix C for more details).

One caveat in using the Planck HFI maps is that a Cosmic Infrared Background
(CIB) monopole model was deliberately included on a per-frequency basis due to
a lack of sensitivity to the absolute emission level. We therefore subtracted
the CIB monopole in each coadded map following Table 12 in Planck
Collaboration et al. (2020a).

band | Planck dataset | ACT datasets | total
---|---|---|---
f090 | 100 GHz | f090 PA5+PA6 | 6
f150 | 143 GHz | f150 PA4+PA5+PA6 | 8
f150 | 217 GHz | f220 PA4 | 4
Table 1: Subsets of maps coadded at each frequency band. All input maps are
two-way split maps. The column “total” shows the total number of maps coadded
in each band. For example, 6 different maps went into making the f090 coadd
map, consisting of two splits from ACT PA4, ACT PA5, and Planck 100 GHz,
respectively.

This procedure yields a total of three coadded maps in both temperature and
polarization at f090, f150, and f220, as summarized in Table 1. We present a
side-by-side comparison between Planck maps and our three coadded maps in
total intensity in Figure 1, and a similar comparison for polarized intensity
for f150 in Figure 2. It is apparent that the addition of ACT data
significantly improves the angular resolution of the maps in both temperature
and polarization. The coadd polarization maps are presented in Figure 3 in
Galactic coordinates. We use the IAU polarization convention, in which the
polarization angle measures 0∘ towards Galactic North and increases counter-
clockwise (Hamaker & Bregman, 1996). The ACT Collaboration has adopted the IAU
convention for all ACT data products since DR4. This is in contrast to the
COSMO convention (Górski et al., 2005) adopted in, e.g., the Planck data
releases, that is related to the IAU convention via a sign flip of Stokes $U$,
i.e., $U_{\rm COSMO}=-U_{\rm IAU}$.

A detailed discussion of these maps is presented in Section 4 for total
intensity maps and in Section 5 for polarization maps. The final coadded maps
have median noise levels of $36~{}\mu$K-arcmin at f090, $33~{}\mu$K-arcmin at
f150, and $270~{}\mu$K-arcmin at f220.

##  4 Total intensity maps

Figure 1 shows the total intensity maps for both Planck-only and the coadded
maps for our three frequency bands (f090, f150, f220). Many prominent features
that were obscured or unresolved in the Planck maps become apparent with the
addition of ACT data, and qualitative changes in map morphology with frequency
are evident. The Galactic Center Radio Arc (GCRA), a prominent filament in the
Galactic center, is visible at both f090 and f150 near the center of the
coadded maps and to a lesser extent at f220, consistent with it being a strong
source of synchrotron radiation Paré et al. (2019).

The ACT frequency coverage probes a variety of emission mechanisms, including
synchrotron, free-free, thermal dust, and molecular line emission, at
different levels in each of the three bands. To better visualize the different
structures probed at each frequency band, we combine the coadded maps from
three frequency bands into a multi-color image shown in the upper panel of
Figure 4. The red, green, and blue image channels represent the f090, f150,
and f220 maps, respectively, after appropriate re-scaling. The intensity
scaling (as detailed in the Figure 4 caption) was chosen to highlight
structures in different bands and to make feature identification easier. An
annotated zoom-in of the three-color intensity map in Figure 4 is provided in
the top panel of Figure 5.

The coherent structures visible in the different colors of Figures 4 and 5
arise from spatial variations in the relative strengths of the various
emission mechanisms. The radio spectrum of supernova remnants (SNR) originates
primarily from synchrotron emission (Weiler & Sramek, 1988), and thus objects
like the SNR candidate G357.7-0.1 (“the Tornado”) (Milne, 1970) and SNR0.9+0.1
(Helfand & Becker, 1987) appear reddish yellow in Figure 4. Similarly, Sgr A*
and the GCRA (see, e.g., Yusef-Zadeh et al., 1984; Pedlar et al., 1989) are
strikingly highlighted in this color, consistent with their strong synchrotron
emission spectrum. Pulsar wind nebulae (PWN), like the Crab Nebula, also emit
highly polarized synchrotron emission with a flat spectral index (Gaensler &
Slane, 2006), in contrast to SNRs which generally emit synchrotron with a
slightly lower polarization fraction and a steeper spectrum.

Thermal emission from interstellar dust dominates Galactic emission at far-
infrared/submillimeter frequencies. Known molecular clouds like the Brick
(G0.253+0.016; e.g., Longmore et al., 2012), the 20 km s-1 Cloud
(G359.889–0.093) and 50 km s-1 Cloud (G0.070-0.035; e.g., Güsten & Downes,
1980), and the Three Little Pigs (G0.145-0.086, G0.106-0.082, and
G0.068-0.075; see, e.g., Battersby et al., 2020, for an overview of these
molecular clouds) thus appear bright blue/green in Figure 5.

In general, however, the presence of strong molecular line emission in the CMZ
precludes the simple interpretation that low frequencies correspond to
synchrotron emission and high frequencies correspond to dust emission. Even in
the relatively broad Planck and ACT passbands, line emission can dominate the
total intensity in the Galactic center maps. Indeed, Planck Collaboration X
(2016) found that 88.6 GHz HCN emission can alone account for up to 23 % of
the total intensity in the Planck 100 GHz band in this region. CO(1–0) at
115.3 GHz and CO(2–1) at 230.5 GHz contribute significantly to the observed
emission in the Planck 100 and 217 GHz bands, respectively (Planck
Collaboration X, 2016), while other lines such as HCO+ (89.2 GHz), CS (98.0,
147.0, and 244.9 GHz), 13CO(1–0) (110.2 GHz), CN (113.2, 113.5 GHz), H2CO
(140.8 and 218.2 GHz), NO (150.2, 150.5 GHz), SiO (217.1 GHz), SO (219.9 GHz),
and 13CO(2–1) (220.4 GHz), among others, are also known to be present in the
Galactic center (e.g., Liszt & Turner, 1978; Sandqvist, 1989; Kramer et al.,
1998; Lang et al., 2002; Takekawa et al., 2014; Pound & Yusef-Zadeh, 2018; Lu
et al., 2021; Schuller et al., 2021) and will contribute to the observed
emission in the ACT and Planck frequency channels.

The very bright CO(1–0) emission poses a particular challenge for our
analysis, as it falls comfortably within the Planck 100 GHz passband but
largely outside that of ACT f090 (see Naess et al., 2020, Figure 2). These two
frequency channels have been combined without taking the differences in
passbands into account, leading to CO(1–0) being emphasized on large Planck-
dominated scales in the coadded map, but not on small ACT-dominated scales.
This likely explains the haziness of the emission in purple in Figure 4, where
the low-frequency channel (red) contains significant CO(1–0) emission in the
Planck map but is dominated by other, less prominent emission mechanisms in
the ACT map. A quantitative interpretation of the frequency spectra of
particular regions in the Galactic center will therefore require careful
consideration of bandpass effects, and possibly the use of external
spectroscopic data (e.g., Dame et al., 2001; Eden et al., 2020) and/or the CO
component maps from Planck (Planck Collaboration X, 2016). Such spectral
analysis will be the subject of future work, and for now we urge caution when
interpreting the colors in Figure 4 in terms of emission mechanisms or
spectral indices.

##  5 Polarization maps

Figure 6: A visualization of magnetic field orientations using line-integral-
convolution (LIC) with a $1^{\circ}$ kernel. Contours in the map trace
magnetic orientations. Rows represent f090, f150, and f220 respectively. Total
intensity maps are shown in the background with the same color scales in
Figure 1. Figure 7: Polarization fractions (background) and magnetic field
orientation (line segments) are shown for our three bands (f090, f150, and
f220). To estimate the magnetic field orientations, the polarization field is
smoothed with a Gaussian kernel FWHM=$2^{\prime}$, and then resampled with a
pixel size of $2^{\prime}$. Line segments with large uncertainty in
polarization angle $\delta\psi\geq 15^{\circ}$ are masked.

Figure 3 presents the full-resolution Stokes $Q$ and $U$ maps obtained through
the mapmaking algorithm at each frequency band. A striking feature of the maps
is the strong polarization signal of the GCRA, extending roughly from
$b=-0.5^{\circ}$ to $b=0.5^{\circ}$ in both f090 and f150. The signal is
weaker in f220, which is dominated by polarized dust emission. Strong
polarized signals can be generally seen near the CMZ along the Galactic plane
across all frequency bands, with especially prominent polarization features
near regions such as Sgr A∗ and Sgr B2. This suggests that the observed
polarization signals are not dominated by diffuse emission along the line of
sight (LOS), but rather by emission directly from the CMZ. Since we are
focusing on high S/N regions ($\gtrsim 3$) that are negligibly impacted by
debiasing, we do not debias the polarization quantities (Plaszczynski et al.,
2014).

To create a three-color polarization image analogous to that in total
intensity, we first compute the polarized intensity $P=\sqrt{Q^{2}+U^{2}}$ in
each band. We synthesize the three polarized intensity maps into a three-color
image using f090, f150, f220 as the red, green, and blue channels
respectively. The result is shown in the lower panel of Figure 4. The
polarized emission has a strikingly different morphology than total intensity
(cf. upper panel of Figure 4). The polarized GCRA stands out distinctively
from the background in red, indicating dominance of f090, consistent with the
prominence of synchrotron radiation in this region.

One quantity of interest is the polarization angle, defined as

$\psi=\frac{1}{2}\arctan\left(\frac{U}{Q}\right).$ (4)

The polarization angle is directly related to the plane-of-sky magnetic field
orientation by a 90∘ rotation. Dust grains tend to align their short axes
parallel to the magnetic field, while they radiate photons preferentially
polarized parallel to their long axes. The synchrotron polarization angle, or
Electric Vector Position Angle (EVPA), is similarly orthogonal to the local
magnetic field orientation for optically thin emission. Hence, the magnetic
field orientation is orthogonal to the polarization angle in both emission
mechanisms. We note, however, that dust and synchrotron emission do not
necessarily trace the same magnetic field, as they generally probe different
volumes along the LOS. The observed magnetic field morphology at a given
frequency depends on the relative contribution of different emission
components, which in turn depends on the spatial distribution of dust density
versus cosmic ray density and the underlying magnetic field orientation and
strength (see Han (2017) for a review).

Figure 6 presents a visualization of the inferred magnetic field orientation
in each of our bands using line integral convolution (LIC; Cabral & Leedom,
1993) with a kernel size of $0.5^{\circ}$. Each contour in the map traces the
magnetic field orientation. The magnetic field is approximately parallel to
the Galactic plane near the CMZ for both f090 and f150, and is noticeably
tilted for f220 within the range $|l|\lesssim 1.5^{\circ}$. In particular,
within a box of $|l|<1.5^{\circ}$, $|b|<0.15^{\circ}$ we measure the mean
polarization angle to have a tilt of $\simeq 20^{\circ}$ with respect to the
Galactic plane, consistent with the $\simeq 22^{\circ}$ tilt previously noted
by, e.g., PILOT (Mangilli et al., 2019).

The f090 map is noticeably more disordered, with especially prominent features
at the GCRA, where the plane-of-sky magnetic field is aligned with the
orientation of the arc. This $90^{\circ}$ flip in polarization angle at the
GCRA has been observed by the QUIET collaboration (Ruud et al., 2015) at both
43 GHz and 97 GHz. This orthogonal feature is less prominent at f150 and
disappears at f220, as expected from a synchrotron-dominated signal.

The polarization fraction $p=\sqrt{Q^{2}+U^{2}}/I$ in each band is shown in
Figure 7. In each panel, we overlay the magnetic field orientation in the CMZ
at $2^{\prime}$ resolution. Along the Galactic plane the polarization fraction
is generally low, $p\lesssim 2\%$. This is consistent with the previous
observations from, e.g., Planck (Planck Collaboration et al., 2020b) and PILOT
(Mangilli et al., 2019) that found polarization fractions at the percent level
($\lesssim 1.5\%$) in the Galactic center region. We see coherent magnetic
fields even within regions of relatively low polarization fraction, in
agreement with both cloud-scale observations and the relatively few wide-area
dust polarization measurements, both of which tend to find very ordered
magnetic fields (Chuss et al., 2003; Pillai et al., 2015; Mangilli et al.,
2019). The large-scale coherence in the inferred magnetic field direction
suggests that the polarized emission is dominated by the CMZ. The low
polarization fraction could be due to one of several effects, or to a
combination of them. Perhaps the most likely is that the magnetic field
orientation fluctuates both along the line of sight and within the beam
smoothing radius, resulting in depolarization. There are so many emitting
regions along the line of sight in the Galactic disk that small variations in
the magnetic field orientation average out in the line-of-sight integration,
such that observed deviations from the mean magnetic field orientation are
small. We note, however, that simulations of the Galactic magnetic field used
to interpret PILOT data suggest that this effect may not be sufficient on its
own to account for the entirety of the observed depolarization (Mangilli et
al., 2019). Another possibility is that the mean field has a significant LOS
component. Because magnetically-aligned dust grains spin around their short
axes, the net dust emission is more strongly polarized for regions with a
predominantly plane-of-sky magnetic field than for regions where the magnetic
field is more parallel to the LOS. However, a significant LOS magnetic field
component would not be expected to dominate the entirety of the CMZ if the
magnetic field has a significant azimuthal component. Finally, it may be that
the mean field in the CMZ is itself a product of superimposed, misaligned
structures that each have large scale coherence, e.g., the twisted ring
geometry proposed for the distribution of dust density in the CMZ (Molinari et
al., 2011). While possible, such a scenario demands great uniformity in the
relative total and polarized intensities in each component to avoid dispersion
in the observed polarization angles. On balance, we favor a coherent magnetic
field in the CMZ dust, with LOS disorder as the primary driver of low
polarization fractions, but more detailed modeling of the present data is
warranted to assess the relative importance of each of these effects.

##  6 Notable objects

Figure 8: GCRA and Sgr A∗. The left panel shows the polarized intensity in the
region, measured from f090 coadded. Contours show levels of total intensity at
f090 with a spacing of 2 MJy sr-1 up to 30 MJy sr-1. The right panel shows the
inferred magnetic field orientations from the f090 map as line segments in
$0.5^{\prime}$ pixelization (full resolution). Segments are shown with varying
opacity that scales linearly with the S/N in polarized intensity and saturates
when $S/N=3$. In the background we show a radio image of the region from
MeerKAT (Heywood et al., 2019) which observes at 1.28 GHz in
$6^{\prime\prime}$ pixelization. The expected location of Sgr A∗ is indicated
with a white cross mark in both panels. Note that the MeerKAT image is shown
for visualization purposes only, as no primary beam corrections have been
applied, and the entire Galactic plane is seen through the primary beam
sidelobes. Caution should be taken when interpreting the numerical values in
this image (see Heywood et al. (2019) for a detailed discussion).
Figure 9: Molecular cloud known as “the Brick”. Left: total intensity measured
from ACT+Planck f220 coadd map is plotted in the background. The Herschel
$500\,\mu$m measurements (Molinari et al., 2016) are shown as contours
indicating 50th, 70th, and 90th percentiles from lighter to darker contours.
Right: total intensity measured by Herschel $500\,\mu$m is shown in the
background. We show the magnetic field orientation inferred from the f220 map
as line segments. Segments are shown with varying opacity that scales linearly
with the S/N in polarized intensity and saturates when $S/N=3$. Figure 10: A
cloud triad known as “the Three Little Pigs” consisting of G0.145-0.086
(“Straw Cloud”), G0.106-0.082 (“Sticks Cloud”), and G0.068-0.075 (“Stone
Cloud”). The data are plotted following Figure 9, with the left panel showing
the ACT+Planck f220 map with the Herschel 500 $\mu$m image overlaid as
contours (indicating 50th, 70th, and 90th percentiles from lighter to darker
colors), and the right panel showing the Herschel 500 $\mu$m map with the
magnetic field orientations inferred from the f220 map overlaid as line
segments. Segments are shown with varying opacity that scales linearly with
the S/N in polarized intensity and saturates when S/N$=3$.  Figure 11:
G359.23–0.82 or “the Mouse” is a pulsar wind nebula (PWN) traveling with high
velocity ($\sim$300 km s-1) with respect to ISM, causing a comet-like tail.
The left panel shows the total intensity in f090 with magnetic field
orientation over-plotted in line segments. Both the background and magnetic
field are smoothed to a resolution of $2.2^{\prime}$ to increase the signal-
to-noise ratio. Segments are shown with varying opacity that scales linearly
with the S/N in polarized intensity and saturates when S/N$=3$. The middle
panel shows the polarized intensity in f090 after smoothed to a resolution of
$2.2^{\prime}$. The right panel shows a radio image of the region from MeerKAT
(Heywood et al., 2019) which observes at 1.28 GHz in $6^{\prime\prime}$
pixelization, with the magnetic field orientation from f090 over-plotted as
line segments similar to the leftmost panel.  Figure 12: G357.7-0.1, or “the
Tornado”, is typically classified as a supernova remnant. The left plot shows
the total intensity in its neighborhood in f090 coadded map. Line segments
indicate The right plot shows the magnetic field orientations inferred from
f090. They are shown with varying opacity that scales linearly with the S/N in
polarized intensity and saturates when S/N$=3$. The right panel shows the
corresponding polarized intensity map in f090. Both maps are shown at the full
resolution from mapmaking ($0.5^{\prime}$). Figure 13: $l=1.3$ molecular
complex. The left plot shows the total intensity in f220 (smoothed with
FWHM=$1^{\prime}$) with contours indicating the 50th, 70th, 90th percentiles
in the Herschel 500 $\mu$m map. The right plot shows the Herschel 500 $\mu$m
map with magnetic field orientation inferred from the f220 map as an overlay,
after smoothed to a resolution of $1.4^{\prime}$. Segments are shown with
varying opacity that scales linearly with the S/N in polarized intensity and
saturates when S/N$=3$.

With arcminute resolution in three frequency bands, we detect many known radio
and infrared sources, some of which have not been previously observed at ACT
frequencies. Although the main focus of this paper is presentation of the
Galactic center coadded maps, in this Section we demonstrate the fidelity of
these maps and their broad potential for different scientific investigations
by highlighting select objects. All objects discussed in this section are
marked in Figure 5, which includes additional selected radio sources listed in
LaRosa et al. (2000) and submillimeter sources from the CMZoom Survey
(Battersby et al., 2020) visible in our maps. This list of notable sources is
non-exhaustive, and in particular, our maps extend to a wider range in
Galactic longitude than either the LaRosa et al. (2000) or Battersby et al.
(2020) catalogs.

###  6.1 Sgr A and GCRA

Sagittarius A (Sgr A) is a complex radio source located at the center of our
Galaxy. It consists of Sgr A East, an extended non-thermal source with a
radius of $\sim 3^{\prime}$, and a thermal source Sgr A West, which has three-
arm spiral morphology and lies within Sgr A East (e.g., Ekers et al., 1983;
Yusef-Zadeh & Morris, 1987b; Anantharamaiah et al., 1991). Infrared monitoring
of stellar orbits in the vicinity of Sgr A has also revealed the existence of
a supermassive black hole Sgr A∗ that lies within Sgr A West (e.g., Ghez et
al., 2008) and acts as the dynamical center of our Galaxy (Backer & Sramek,
1999).

The region of sky surrounding Sgr A∗ has been the subject of extensive multi-
frequency observations both in imaging and polarimetry (e.g., Stolovy et al.,
1996; Bower & Backer, 1998; Melia et al., 2000; Baganoff et al., 2003; Chuss
et al., 2003). Polarized observations in the millimeter bands, in particular,
are important for understanding the accretion process near the black hole and
associated relativistic emission (e.g., Agol, 2000; Melia et al., 2001).
Linear polarization of Sgr A∗ at millimeter wavelengths was first reported by
the Submillimetre Common-User Bolometer Array (SCUBA; Aitken et al., 2000),
which they interpret as synchrotron-dominated polarized emission sourced by
the gas in the vicinity of the black hole. The observed polarization fraction
of Sgr A∗ is $\sim 3\%$ at 2 mm. Subsequent interferometric imaging surveys
(e.g., Macquart et al., 2006; Marrone et al., 2006) measured a $\sim 2\%$
polarization fraction at 3.5 mm, and larger values at higher frequencies.
Strong emission centered on Sgr A∗ is visible in the coadded maps, showing up
clearly in the multi-frequency image with a yellow color in total intensity
(see the upper panel in Figure 5), implying a predominance of synchrotron
emission in the region. Its location indicates that the emission is likely
dominated by Sgr A∗ itself instead of the overlapping components in Sgr A that
are unresolved with the ACT beam. Regions surrounding Sgr A∗ are polarized at
$2-4\%$ level, as seen in Figure 7 for f090 and f150, and show up as a reddish
“blob” in the multi-frequency polarimetry (see the lower panel in Figure 5).
This may be due to synchrotron emission from the nearby non-thermal filaments
within a beam smoothing radius. The polarized emission in the vicinity of Sgr
A∗ has a lower polarization fraction of $\sim 1.5\%$ at all three bands,
consistent with the depolarization noted by SCUBA (Aitken et al., 2000) at 2
mm. The slightly lower polarization fraction seen in the ACT data is likely
due to a beam depolarization effect from the larger ACT beam ($\sim
2^{\prime}$) in comparison to the SCUBA beam ($\sim 34^{\prime\prime}$ at 2
mm).

In Figure 8 we present a zoom-in view of the region surrounding Sgr A∗. The
left panel shows the polarized signal in f090 overlaid with contours from the
total intensity in f090. Strong emission from Sgr A∗ is seen in total
intensity but not in polarization, where the emission is more diffuse and
extends $\sim 3^{\prime}$ away from the central source. This is further
evidence that the polarized signal in the vicinity of Sgr A∗ is emitted by the
surrounding non-thermal filaments, while the emission from Sgr A∗ itself is
highly depolarized. In the right panel we show the inferred magnetic field
orientations from the polarized signal at f090 overlaid on top of a radio
image of the same region from MeerKAT (Heywood et al., 2019), which observes
at 1.28 GHz with a $6^{\prime\prime}$ beam. The magnetic field morphology
inferred from our f090 map closely follows the underlying non-thermal
filamentary structure. The morphology is also in broad agreement with previous
Caltech Submillimeter Observatory (CSO; Chuss et al., 2003) observations at a
wavelength of 350 $\mu$m with a $20^{\prime\prime}$ beam.

Figure 8 also shows the GCRA, a prominent radio feature located at $\sim
l=0^{\circ}10^{\prime}$, which consists of a bundle of thin filaments running
perpendicular to the Galactic plane (e.g., Yusef-Zadeh & Morris, 1987a;
Anantharamaiah et al., 1991). The GCRA is known to be a highly polarized
synchrotron source, though its origin is still poorly understood. The strong
synchrotron emission implies that free electrons are present in the GCRA and
are accelerated to relativistic speeds in the presence of a strong magnetic
field in the region. Various models have been proposed to explain the source
of electrons and the acceleration mechanism (see, e.g., Serabyn & Morris,
1994, for a review), though the matter is still under debate.

In millimeter bands, the GCRA has previously been detected at 7 mm (Reich et
al., 2000), 3 mm (Pound & Yusef-Zadeh, 2018), and 2 mm (Staguhn et al., 2019),
which the latter notes was the highest-frequency detection of the GCRA at the
time. Polarized emission from the GCRA has also been previously detected at 2
and 3 mm by Culverhouse et al. (2011), and at 3 mm and 7 mm by Ruud et al.
(2015). In our coadded maps, GCRA appears in total intensity in both f090 and
f150. The associated polarized emission can also be seen clearly in f090 and
f150 with polarization fractions reaching $\sim 30\%$. This is considerably
higher than the $\sim 10\%$ peak polarization noted by the QUaD Galactic Plane
Survey (Culverhouse et al., 2011) at the same frequencies, likely due to the
improved angular resolution in our coadded maps ($2^{\prime}$ at f090,
$1.4^{\prime}$ at f150) in comparison to Culverhouse et al. (2011)
($5^{\prime}$ at 100 GHz, $3.5^{\prime}$ at 150 GHz). The polarized emission
from the southern portion of the GCRA is also visible in f220, which is likely
the highest frequency at which this structure is detected to date (note
especially the f220 $Q$ map in Figure 3). In addition to being fainter at 220
GHz on account of the falling synchrotron spectrum, the GCRA is also obscured
by emission from dust along the line of sight. The uniformity of the polarized
emission observed in the Arc as seen in Figure 3 implies that a highly ordered
magnetic field exists along the Arc that deviates sharply from the large scale
magnetic field geometry (see Figure 6). In particular, the magnetic field
orientation inferred from f090 (as seen in the right panel of Figure 8) aligns
closely with the filamentary structure perpendicular to the Galactic plane.
This is in broad agreement with the morphology observed at 43 GHz and 96 GHz
by QUIET with lower angular resolution (Ruud et al., 2015).

###  6.2 The Brick

G0.253+0.016, also known as “the Brick”, is a dense, massive molecular cloud
in the CMZ, and a prominent infrared dark cloud (Carey et al., 1998; Longmore
et al., 2012). In the context of understanding the low star formation rate in
the Galactic center environment, the Brick is a particularly interesting case
study. Despite its high mass $(>10^{5}M_{\sun})$ and density
$(>10^{4}~{}\mathrm{cm}^{-3})$, evidence of star formation is nearly absent in
the Brick, and thus it may provide an ideal opportunity to study the initial
conditions of high-mass star formation (Lis et al., 1994; Longmore et al.,
2012; Kauffmann et al., 2013; Mills et al., 2015; Walker et al., 2021). A
number of factors have been invoked to explain the dearth of star formation in
G0.253+0.016, including solenoidal turbulence driven by strong shear in the
CMZ (Federrath et al., 2016; Kruijssen et al., 2019; Dale et al., 2019;
Henshaw et al., 2019), or strong cloud scale magnetic fields
($B\sim\mathrm{mG}$, Pillai et al., 2015).

The Brick stands out at high contrast to the background in the coadded total
intensity maps at both 150 and 220 GHz. Our polarization measurements at these
frequencies probe the magnetic field structure in the dust toward G0.253+0.016
at $\sim$arcminute scales. These observations complement $20^{\prime\prime}$
resolution polarization data at 350 $\mu$m from the Caltech Submilimeter
Observatory (CSO; Dotson et al., 2010; Pillai et al., 2015). We find that the
inferred magnetic field orientation is aligned parallel to the long axis of
the Brick on the plane of the sky (Figure 9), and the polarization angles are
very ordered in this region, in agreement with the CSO data at smaller angular
scales. Pillai et al. (2015) use the strong coherence of the magnetic field
orientation in the Brick to compare the inferred magnetic field strength to
the gas velocity dispersion measured from $\mathrm{N_{2}H^{+}}$ emission
(Kauffmann et al., 2013). Those authors find that magnetic fields dominate
over turbulence in the Brick. The coherent magnetic field structure in our
observations is consistent with the expectation that turbulence in the Brick
is sub-Alfvénic at the scales probed by ACT. The ACT polarized emission is
brightest at the northern part of the Brick, with a peak polarization fraction
of $3.6\%$. The polarized intensity is lower in the southern portion of the
cloud, and the SNR on the polarized intensity drops below 3. This
depolarization may be due in part to unresolved polarization structure within
the ACT beam, and/or to incoherent contributions to the polarized emission
along the line of sight.

###  6.3 The Three Little Pigs

The cloud triad G0.145-0.086, G0.106-0.082, and G0.068-0.075 visible in Figure
10 has been dubbed “the Three Little Pigs.” All three clouds have been noted
as a set of compact dusty sources in the CMZoom Survey (Battersby et al.,
2020), while G0.068-0.075 also appears in the SCUBA-2 Compact Source Catalog
(Parsons et al., 2018). As Figure 10 illustrates, each cloud is also apparent
in the 500 $\mu$m data from Herschel Infrared Galactic Plane Survey Herschel
(Hi-GAL; Molinari et al., 2016). Interestingly, the $3^{\prime\prime}$
resolution 230 GHz observations with the Submillimeter Array as part of the
CMZoom Survey have revealed a dearth of substructure in G0.145-0.086 (“Straw
Cloud”), somewhat more substructure in G0.106-0.082 (“Sticks Cloud”), and yet
more in G0.068-0.075 (“Stone Cloud”).

ACT f220 measurements give a first look at the magnetic field geometry in
these clouds at arcminute resolution. The Straw Cloud, perhaps owing to a
lower column density or lack of dense substructure, has a magnetic field
orientation that deviates little from the large scale field structure. In
contrast, both the Sticks and Stone Clouds have polarization angles in their
interiors that are highly misaligned with the large scale magnetic field.
Similar to the depolarization observed toward the Brick, the cancellation of
polarized emission from dust in different regions within the cloud and/or
other dust along the line of sight may explain the low polarized intensities
observed, particularly in the Stone Cloud.

###  6.4 The Mouse

G359.23–0.82, also known as “the Mouse”, is a pulsar wind nebula powered by
the young X-ray source PSR J1747–2958 (Predehl & Kulkarni, 1995; Camilo et
al., 2002). G359.23–0.82 was originally discovered in radio continuum data
from the Very Large Array (VLA), and derives its nickname from its bright
compact nebula “head” and extended radio “tail” (Yusef-Zadeh & Bally, 1987).
The Mouse is strongly linearly polarized at centimeter wavelengths (Yusef-
Zadeh & Gaensler, 2005). Distances to PSR J1747–2958 and the Mouse are
uncertain, but they are not at the Galactic center: observations of neutral
hydrogen absorption set the maximum distance to G359.23–0.82 at $\sim 5.5$ kpc
(Uchida et al., 1992). Gaensler et al. (2004) argue for a distance of $\sim 5$
kpc, a value now commonly adopted (e.g., Klingler et al., 2018). At 5 kpc, the
transverse velocity of PSR J1747–2958 is $306\pm 43$ km s-1 (Hales et al.,
2009). The Mouse is a striking example of a bow shock nebula, formed by the
interaction of the pulsar with the ambient ISM as it travels at supersonic
speeds (e.g., Gaensler & Slane, 2006).

The Mouse is a prominent object in the ACT f090 map, both in total and
polarized intensity (Figure 11). In particular, polarized emission is detected
significantly across the peak of the Mouse, which is expected for a pulsar
wind nebula. Significant polarized emission is also detected along its tail,
and exhibits a similar morphology as seen by MeerKAT at 1.28 GHz (Heywood et
al., 2019) with a $6^{\prime\prime}$ beam, albeit at lower resolution in the
ACT data. The implied magnetic field orientation in the f090 band is roughly
parallel to the Mouse’s extended tail, consistent with observations at 3.5 and
6 cm by the VLA (Yusef-Zadeh & Gaensler, 2005). The Mouse is traveling
eastward in declination, which is roughly toward the lower lefthand corner of
Figure 11.

###  6.5 The Tornado

G357.7-0.1, “the Tornado,” is typically classified as a supernova remnant,
though its unusual properties have prevented a definitive explanation
(Gaensler et al., 2003; Chawner et al., 2020). The Tornado has been long
observed in radio imaging and polarimetry (e.g., Milne, 1970; Shaver et al.,
1985; Law et al., 2008), which consistently show a bright “head” region and a
“tail” region roughly $10^{\prime}$ in extent. Recently, mid- and far-infrared
dust emission has been detected with Spitzer and Herschel, revealing a large
dust reservoir in the head region ($\sim 17$ M⊙) and consistent with
interstellar matter swept up in a supernova blast wave (Chawner et al., 2020).
The head of the Tornado has also been detected by Chandra in X-rays without
evidence for embedded point sources (Gaensler et al., 2003), lending further
credence to its classification as a supernova remnant. However, the provenance
of the tail is still unresolved (see Chawner et al., 2020, for a recent
discussion).

The Tornado is prominent in the f090 and f150 Stokes $Q$ and $U$ maps, but not
f220 (see Figure 3). Likewise, the region stands out in reddish-brown in the
three-color polarization map (Figure 5). This suggests the prominence of
synchrotron emission in this source. A closer examination of the Tornado in
the f090 band is presented in Figure 12. Here we see the extended tail region
in total intensity but not in polarization, while the head is prominent in
both. This morphology is consistent with 4.9 GHz polarimetric observations by
Shaver et al. (1985). The inferred magnetic field at f090 is approximately
perpendicular to the extended tail in the eastern side of the Tornado, and is
tilted towards the head on the western side. This is also in a broad agreement
with the magnetic field morphology noted by (Shaver et al., 1985) at 4.9 GHz.
We observe a maximum polarization fraction of the Tornado in f090 of $8.5\%\pm
1\%$, slightly lower than the $\sim 10\%$ observed at 4.9 GHz at significantly
higher resolution ($12\times 26^{\prime\prime}$ beam, Shaver et al., 1985). It
is likely that much of the difference is due to more beam depolarization in
the ACT data.

###  6.6 $l=1.3$ Complex

The combination of ACT and Planck data used in the coadded maps enables large
regions to be mapped with fidelity on both large and small angular scales.
Likewise, the high sensitivity of the polarimetry permits mapping of more
diffuse regions of molecular clouds, not just bright cores. These capabilities
are highlighted in the $20\times 30^{\prime}$ maps of the $l=1.3$ complex in
Figure 13.

The $l=1.3$ complex is a large, high-velocity-dispersion molecular cloud
complex extending from roughly 1.2–1.6∘ in Galactic longitude (Bally et al.,
1988). The elevated abundance of SiO and high ratio of CO(3–2) to CO(1–0)
emission in some clouds within the complex suggest the presence of strong
shocks, perhaps from cloud-cloud collisions or supernova explosions
(Huettemeister et al., 1998; Oka et al., 2001; Rodriguez-Fernandez et al.,
2006; Tanaka et al., 2007; Parsons et al., 2018; Tsujimoto et al., 2021). This
complex may sit at the intersection of a dust lane with the nuclear ring,
supplying it with material (Huettemeister et al., 1998; Fux, 1999; Rodriguez-
Fernandez et al., 2006; Liszt, 2008).

Total emission from the $l=1.3$ in f220 and Herschel 500 $\mu$m (Molinari et
al., 2016) is presented in Figure 13, with good morphological correspondence
between the two maps. In the right panel, we overlay the f220 magnetic field
orientation on the higher resolution Herschel map. While many density
structures show clear alignment with the magnetic field orientation, this is
not universally observed. The highest intensity regions have comparatively low
polarized intensities, suggesting elevated magnetic field disorder or a loss
of grain alignment in the densest regions.

##  7 Conclusion

We have presented new arcminute-resolution maps of the Galactic center region
at microwave frequencies by combining data from ACT and Planck. Known radio
features appear at high significance in both total intensity and polarization
in three frequency bands. The polarization maps provide a frequency-dependent
probe of magnetic fields, demonstrating a change in the observed magnetic
field morphology as the fractional contributions of synchrotron radiation and
thermal dust emission from different regions within the Galactic center along
the line of sight vary with frequency. With wide-field maps at higher angular
resolution, we identified known radio sources and molecular clouds, some of
which have not previously been observed in polarization at microwave
frequencies. With three frequency bands, our total intensity maps reveal the
rich physical environment in the CMZ with spatially varying combinations of
different emission mechanisms, including synchrotron, free-free, dust, and
molecular line emission in the CMZ. Separation of these emission components
will be the subject of a follow-up paper.

The coadded maps produced in this work will be made publicly available on
LAMBDA. These maps are suitable for tracing magnetic field morphology across
the Galactic center region and measuring the total and polarized emission from
individual sources. However, caution is urged for multi-frequency analyses due
to the bandpass mismatch between ACT and Planck that results in a slight
scale-dependence of effective band centers for different emission mechanisms.
As discussed in Section 4, CO(1-0) emission falls within the Planck 100 GHz
passband but not f090, amplifying bandpass mismatch effects in the resulting
coadded map.

ACT has continued to observe the Galactic center during 2020, collecting a
similar amount of data to that used in this work. In addition, the daytime
data from both 2019 and 2020 can, in principle, be corrected for thermal
telescope distortions (Aiola et al., 2020), which would again double the total
amount of data. Therefore, ACT maps with half the pixel noise variance of
those presented here are possible based solely on data that has already been
collected. Additionally, we plan to apply the mapping techniques used here to
approximately 70 degrees of the Galactic plane covered by ACT from 2019 and
2020. Furthermore, the addition of the low-frequency array to ACT in 2020 (Li
et al., 2018; Simon et al., 2018) will also allow us to map the Galactic plane
at 27 GHz and 39 GHz, likely yielding new insights on the Galactic center
environment.

The next observational step at these frequencies will be the Large Aperture
Telescope of the Simons Observatory (Ade et al., 2019), anticipated to see
first light in 2023 from the same site in Chile. This new instrument will have
the same 6-meter diameter primary as ACT, but with an instrumented focal plane
of 5 times larger area (Zhu et al., 2021). The nominal scan strategy will
continuously cover the entire sky in the declination range between
$+25^{\circ}$ and $-40^{\circ}$, providing coverage of over 100 degrees of the
Galactic plane in five frequency bands in both total intensity and
polarization. The five-year map noise should improve on ACT by roughly a
factor of three. The Galactic center will be observed at higher frequencies by
the CCAT-prime project (Choi et al., 2020) and will also be a good target for
future balloon-borne instruments, which can achieve sub-arcminute resolution
with similar sensitivity at even higher frequencies, e.g., BLAST Observatory
(Lowe et al., 2020). By 2030, we can also anticipate data from CMB-S4 (CMB-S4
Collaboration et al., 2016), with an additional map noise improvement by a
factor of four. This unrivaled combination of resolution, sky coverage, and
sensitivity at microwave frequencies will enable many new inquiries into the
properties of the Milky Way.

This work was supported by the U.S. National Science Foundation through awards
AST-0408698, AST-0965625, and AST-1440226 for the ACT project, as well as
awards PHY-0355328, PHY-0855887 and PHY-1214379. Funding was also provided by
Princeton University, the University of Pennsylvania, and a Canada Foundation
for Innovation (CFI) award to UBC. ACT operates in the Parque Astronómico
Atacama in northern Chile under the auspices of the Comisión Nacional de
Investigación (CONICYT). Computations were performed using Princeton Research
Computing resources at Princeton University, the Niagara supercomputer at the
SciNet HPC Consortium and on the Simons-Popeye cluster of the Flatiron
Institute. SciNet is funded by the CFI under the auspices of Compute Canada,
the Government of Ontario, the Ontario Research Fund—Research Excellence, and
the University of Toronto. The development of multichroic detectors and lenses
was supported by NASA grants NNX13AE56G and NNX14AB58G. Detector research at
NIST was supported by the NIST Innovations in Measurement Science program.
Research at Perimeter Institute is supported in part by the Government of
Canada through the Department of Innovation, Science and Industry Canada and
by the Province of Ontario through the Ministry of Colleges and Universities.
S.E.C. acknowledges support by the Friends of the Institute for Advanced Study
Membership. EC acknowledges support from the STFC Ernest Rutherford Fellowship
ST/M004856/2 and STFC Consolidated Grant ST/S00033X/1, and from the European
Research Council (ERC) under the European Union’s Horizon 2020 research and
innovation programme (Grant agreement No. 849169). SKC acknowledges support
from NSF award AST-2001866. R.D. thanks CONICYT for grant BASAL CATA
AFB-170002. J.P.H. would like to thank Fernando Camilo and Ian Heywood for
providing the MeerKAT image from their 2019 Nature paper in digital form; he
also acknowledges financial support from NASA grant NNX15AK71G and NSF
Astronomy and Astrophysics Research Grant number AST-1615657 to Rutgers
University. E.S. is supported by the Chamberlain fellowship at Lawrence
Berkeley National Laboratory. NS acknowledge support from NSF grant number
AST-1907657. Zhilei Xu is supported by the Gordon and Betty Moore Foundation.

##  Appendix A Calibration method

In ACT DR4 (Aiola et al., 2020), the data were calibrated from raw data
acquisition units to physical units with

$d^{\rm pW}=d^{\rm DAQ}\times R_{\rm BS}\times g_{\rm atm}\times f,$ (A1)

where $d^{\rm pW}$, $d^{\rm DAQ}$ represent detector data in physical unit and
data acquisition unit respectively, $R_{\rm BS}$ represents the intrinsic
responsivity of each detector measured from the most recent bias step, $g_{\rm
atm}$ is the atmospheric correction factor and $f$ represents an optical
flatfield. Both $g_{\rm atm}$ and $f$ are estimated from detector
responsiveness to the atmospheric signal which is treated as a common mode for
all detectors. In the presence of non-atmospheric thermal contamination
signatures this approach to estimating the common mode may bias the
calibration. Preliminary analyses on the Advanced ACT data collected after
2017 have shown evidences for the presence of such thermal contamination.
Hence, the calibration method needs to be updated to account for this bias.

To circumvent the problem, we switch from a common-mode based calibration to a
planet based approach, given by

$d^{\rm pW}=d^{\rm DAQ}\times R_{\rm BS}\times f_{\rm p},$ (A2)

where we have dropped the atmospheric correction $g_{\rm atm}$. $f_{\rm p}$
now represents an optical flatfield measured from detector responsiveness to
emission from Uranus instead of the atmosphere. This leads to an improved
calibration model and better gain stability, and this is expected to be the
standard calibration method for future data releases of ACT.

One caveat to note is that ACT DR4 (and earlier) maps are calibrated to Planck
maps in a final step. This step, however, is missing here due to the
preliminary nature of the Advanced ACT data in 2019, but since the Planck
calibration is found to be close to the planet calibrations for ACT DR4, and
we are using an improved calibration model from DR4, we do not expect this to
be a concern and estimate a $O(1\%)$ uncertainty in global gain calibration as
a result.

##  Appendix B Beam leakage correction

To reduce the contamination in the polarization maps caused by beam leakage
effects, we modeled the observed polarization maps $P_{\rm obs}$ as a sum of a
beam-convolved sky map $P_{\rm sky}$ and a leakage component, given by

$P_{\rm obs}(\mathbf{x})=P_{\rm
sky}(\mathbf{x})+\int\frac{d^{2}k}{(2\pi)^{2}}~{}\tilde{T}_{\rm
sky}(\mathbf{k})\tilde{B}_{\rm
eff}(\mathbf{k})e^{-i\mathbf{k}\cdot\mathbf{x}},$ (B1)

where the leakage component is a convolution of the beam-convolved temperature
map $T_{\rm sky}$ with an effective beam $B_{\rm eff}$ given in Fourier space
by

$\tilde{B}_{\rm
eff}(\mathbf{k})\equiv\tilde{B}_{T}^{-1}(\mathbf{k})\tilde{B}_{P}(\mathbf{k})~{}~{}~{}.$
(B2)

This represents a combination of deconvolving the temperature beam $B_{T}$ and
convolving with a leakage beam $B_{P}$. Our strategy is then to build a model
of $\tilde{B}_{\rm eff}(\mathbf{k})$, convolve it with the temperature map,
and deproject it from the observed polarization maps for each dataset.

As we assumed Uranus signal is unpolarized, any signal we measure in the
polarization is a sign of T-to-P leakage. Hence, we modeled $\tilde{B}_{\rm
eff}$ using observations of Uranus made in the same observation season, with
the following steps:

  1. 1.

We made Uranus planet maps for each Uranus observation in a source-centered
reference frame with the scan direction as the horizontal axis.

  2. 2.

We reprojected each planet map into the Galactic coordinate system. As the
$Q$/$U$ reference frame needs to be rotated depending on the scan directions,
and the Galactic center observations consist of two scan directions taken
during rising and setting respectively, we accounted for the difference in
scan directions by rotating the $Q$/$U$ reference frame for both rising and
setting scan directions and performed a weighted average (for each planet map)
depending on the total number of rising and setting scans made during Galactic
center observations.

  3. 3.

We stacked all reprojected Uranus maps for each dataset (per frequency band
per array) with inverse-variance weighting to obtain estimates of $B_{T}$ and
$B_{P}$ for each dataset.

  4. 4.

We performed a real space cut to remove noise outside a radius of $r_{\rm
max}$ in $B_{T}$ and $B_{P}$ for each dataset, then calculated $\tilde{B}_{\rm
eff}(\mathbf{k})=\tilde{B}_{P}/\tilde{B}_{T}$ in Fourier space.

  5. 5.

We further cleaned $\tilde{B}_{\rm eff}(\mathbf{k})$ with a $k$-space cut
$k\leq k_{\rm max}$ to get rid of small scale noise in the beam model. We then
re-filled the $k$-space outside $k_{\rm max}$ by mirroring the value at
$k=k_{\rm max}$ with $\tilde{B}_{\rm eff}(\mathbf{k})|_{k>k_{\rm
max}}=\tilde{B}_{\rm eff}(\mathbf{k}~{}k_{\rm max}/k)$. In practice, the
details of the extrapolating function make little difference. This specific
function is chosen to ensure that the transition at $k=k_{\rm max}$ is smooth,
and it extends naturally to infinity.

In Table 2, we listed the choices of $r_{\rm max}$ and $k_{\rm max}$ for each
dataset. As a result of these steps, we obtained $\tilde{B}_{\rm
eff}(\mathbf{k})$ for each dataset respectively. Treating the coadded
temperature map as the “true” sky model $T_{\rm sky}$ at each frequency band,
we predicted the expected T-to-P leakage from the derived leakage beam model
$\tilde{B}_{\rm eff}(\mathbf{k})$ and subsequently deprojected the expected
leakage from each observed map.

array | freq | $r_{\rm max}$ | $k_{\rm max}$
---|---|---|---
PA4 | f150 | 5′ | 14500
PA4 | f220 | 4′ | 20000
PA5 | f090 | 8′ | 10500
PA5 | f150 | 5′ | 16500
PA6 | f090 | 8′ | 10500
PA6 | f150 | 5′ | 16500
Table 2: Parameters used when building a 2D leakage beam model (in step 4 and
step 5).

##  Appendix C Coadded f220 maps

Maps at f220 are much noisier than the two other bands, so using the same
coadding pipeline results in a slow convergence. Instead, we adopted a simpler
Fourier-based coadding algorithm. Denote the ACT f220 map as $m_{1}$, and
Planck 217 GHz map as $m_{2}$. The coadd map $m_{\rm coadd}$ was obtained with
a simple inverse-variance coadding as

$m_{\rm
coadd}=\left(N_{1}^{-1}+N_{2}^{-1}\right)^{-1}\left(N_{1}^{-1}m_{1}+N_{2}^{-1}m_{2}\right),$
(C1)

where $N_{1}$ and $N_{2}$ are noise covariance matrices assumed for ACT and
Planck maps respectively. Specifically, we assumed simple Fourier-based noise
model with $N_{1}=w_{1}^{2}[1+(\ell/\ell_{\rm knee})^{-\alpha}]$ and
$N_{2}=w_{2}^{2}b_{1}/b_{2}$, with $w_{1}=268.97~{}\mu$K′,
$w_{2}=124.22~{}\mu$K′ the noise levels of ACT and Planck maps respectively,
$\ell_{\rm knee}=4000$, and $\alpha=-3$. $b_{1}$ and $b_{2}$ represent the
beam model in ACT and Planck respectively, and the factor $b_{1}/b_{2}$ in
$N_{2}$ represents the effect of a combination of deconvolution of the Planck
beam and convolution with the ACT beam in the Planck noise model. In addition,
we further applied a high-pass Butterworth filter in the ACT map with
$\ell_{c}=2500$ and $\alpha=-5$ along the two cross-linked scan directions
with a width given by the beam FWHM in Fourier space. This helps suppress
excess noise along the scan directions. We then applied an additional high-
pass Butterworth filter with $\ell_{c}=200$ and $\alpha=-10$ to suppress the
large-scale atmospheric noise in the ACT f220 map. Both of these filters are
included in the ACT noise model $N_{\ell}$.

The inverse-variance map of the output coadd map was estimated using the same
weighted average from Planck inverse-variance map ${\cal N}_{1}^{-1}$ and ACT
inverse-variance map ${\cal N}_{2}^{-1}$, given by

${\cal N}_{\rm coadd}=\overline{c}_{1}^{2}{\cal
N}_{1}+\overline{c}_{2}^{2}{\cal N}_{2}$ (C2)

with $\overline{c}_{1}$, $\overline{c}_{2}$ defined as the $\ell$-space mean
of $c_{1,2}\equiv N_{1,2}^{-1}/(N_{1}^{-1}+N_{2}^{-1})$ from $\ell=2500$ to
$5000$ where we expect to be dominated by white noise.

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