# BreakBRD Galaxies III: Evolutionary Clues Through an Analysis of Gas Content

David V. Stark Space Telescope Science Institute
3700 San Martin Dr
Baltimore, MD 21218, USA  William H. Miller III Department of Physics and
Astronomy
Johns Hopkins University
Baltimore, MD 21218, USA  Sarah Tuttle  Department of Astronomy, University of
Washington
3910 15th Ave. NE, Room C319
Seattle, WA 98195-0002, USA  Stephanie Tonnesen  CCA, Flatiron Institute
162 5th Avenue
New York, NY 10010, USA  Zachary Tu  Department of Astronomy, University of
Washington
3910 15th Ave. NE, Room C319
Seattle, WA 98195-0002, USA

###### Abstract

By combining newly obtained deep GBT 21cm observations with optical
spectroscopic data, we present an analysis of the gas content of BreakBRD
galaxies, a population denoted by their blue star-forming centers and red
quenched disks that do not appear to follow the typical inside-out evolution
of spiral galaxies. We confirm previous results that the neutral atomic
hydrogen (H I) gas fractions of BreakBRDs are on-average lower than those of
typical galaxies on the star-forming sequence (SFS), and find that their H I
fractions are generally higher than Green Valley (GV) galaxies. H I depletion
times for BreakBRDs are roughly an order of magnitude lower than those of SFS
galaxies, in stark contrast with GV galaxies that typically have much longer
depletion times than SFS galaxies. The nuclear gas-phase metallicities of
BreakBRDs have a broader distribution than SFS galaxies and are skewed towards
slightly above-average values. BreakBRDs are systematically offset from the
Baryonic Tully-Fisher Relation towards lower baryonic mass at a given rotation
velocity. They also have higher typical H I asymmetries than SFS galaxies, and
of those galaxies with spatially resolved gas velocity fields from the SDSS-IV
MaNGA survey, two-thirds are either highly distorted or completely misaligned
relative to the stellar disk. Evidence supports a scenario where BreakBRDs are
in an early phase of quenching, and there is mixed evidence that their
behavior is related to past merger activity.

††facilities: SDSS, GBT††software: GBTIDL, lifelines

##  1 Introduction

One of the most fundamental ways galaxies can be categorized is through their
star-formation activity. Since the peak of cosmic star formation around $z\sim
2$, the non star-forming (quenched) population, also referred to as the “red
sequence” based on its position in color-magnitude space, has grown
significantly (Bell et al., 2004; Faber et al., 2007). While numerous
mechanisms have been identified or proposed to drive quenching, large
statistical studies of galaxy populations have shown that quenching is a
function of both stellar mass and environment (Peng et al., 2010), and that it
typically occurs “inside-out,” where galaxy bulges tend to quench before disks
(White & Frenk, 1991; Chiappini et al., 1997; van den Bosch, 1998; Boissier &
Prantzos, 1999; Pérez et al., 2013; López Fernández et al., 2018; Li et al.,
2015; Erroz-Ferrer et al., 2019; Lin et al., 2019).

However, the inside-out mechanism is not the only way quenching can manifest.
Outside-in quenching has also been observed, and such behavior is generally
expected from environmental processes like ram-pressure stripping that
preferentially impact disks (Rasmussen et al., 2006; Koopmann & Kenney, 2004a,
b). Environmental processes can affect all galaxies, but they are expected to
more severely impact low-mass galaxies with shallower potential wells (e.g.,
Kolcu et al., 2022). However, observational evidence for this behavior is
inconsistent; Schaefer et al. (2017) find a link between outside-in quenching
and dense environments, while Welikala et al. (2008) find local environments
tend to drive the reverse. Spindler et al. (2018) see environmentally-driven
quenching independent of galactocentric radius. Alternatively, tidal
interactions and bar instabilities can cause gas to be redistributed from the
disk to the central regions of galaxies (Masters et al., 2010; Ellison et al.,
2011; Moreno et al., 2015), which could lead to red quenched disks and blue
star forming bulges, at least temporarily, if enough material were to flow
inwards. The ability of mergers and/or bars to drive gas inwards may be mass-
dependent, being more impactful for massive galaxies (Ellison et al., 2011;
Carles et al., 2016). At the same time, mergers are also thought to be
responsible for the buildup of spheroidal components, which may drive inside-
out quenching by stabilizing any gas in the central regions (Martig et al.,
2009). Inflows will also potentially drive active galactic nuclei (AGN)
activity that will likely preferentially affect central regions as well.

One specific class of galaxies that shows apparent outside-in evolution is the
“Break Bulges in Red Disks” (BreakBRD) sample introduced by Tuttle & Tonnesen
(2020). This subsample is defined as having recent central star formation
identified via the 4000$\AA$ break (hence, “break bulges”) but optically red
disks inconsistent with recent star formation. Tuttle & Tonnesen (2020) found
that this population is ubiquitous in both stellar mass and environment
parameter space, indicating that no single mass- or environment-dependent
process is driving their behavior, and that their gas content is lower than
that of typical star-forming galaxies. Radial star formation profiles derived
from a subset of BreakBRDs with SDSS-IV MaNGA (Bundy et al., 2015) integral
field unit (IFU) spectroscopic data has confirmed that their disks are indeed
older than their bulges and their bulges are still actively star-forming, but
that star formation is not enhanced above what is expected from the star-
forming sequence (McKay et al., in preparation).

Kopenhafer et al. (2020) identified BreakBRD analogs in the IllustrisTNG
cosmological hydrodynamic simulation, finding a higher rate of satellite and
splashback galaxies among BreakBRDs, but that there nonetheless exists a
significant fraction of BreakBRDs that are central in their halos and have no
clear driver for their behavior. They also found lower (but not zero) and more
centrally concentrated star formation, a more concentrated distribution of
dense star-forming gas, and a history of gas loss in their outskirts since
$z\sim 0.5$. Most of their BreakBRD analogs were quenching systems. Tonnesen
et al. (2023) followed this work with an analysis of the simulated CGM,
finding it to be less massive, more compact, and more metal rich than typical
star-forming galaxies.

These past studies support a scenario where BreakBRDs are quenching from the
outside-in, possibly due to a gas disk that has either been lost or stabilized
against star formation. The original analysis of BreakBRDs in Tuttle &
Tonnesen (2020) only had a limited, shallow census of the gas reservoir,
namely the neutral atomic hydrogen (H I). Although H I is not the direct fuel
for active star-formation, it is the large-scale reservoir out of which star-
forming molecular clouds condense, making it an important quantity in order to
understand the long-term star formation potential of a galaxy. Furthermore, as
H I is often extended relative to stellar disks, it can be a sensitive tracer
of external influences (Holwerda et al., 2011), making it useful for
determining what may be driving BreakBRD evolution. In this study, we present
new 21cm observations for a sample of BreakBRDs. These data provide robust
measurements with either detections or strong upper limits (H I-to-stellar
mass ratio $\lesssim$ 0.05) over most of the sample. Our new 21cm observations
are combined with optical spectroscopic data from the SDSS Legacy
Spectroscopic survey (York et al., 2000) and the SDSS-IV MaNGA survey for a
subset of fourteen BreakBRDs. With these data, we analyze the H I properties,
nuclear gas-phase elemental abundances, and spatially resolved gas kinematics
of breakBRDs, all in an effort to more clearly constrain how they fit into the
galaxy evolution paradigm.

This paper is organized as follows. In Section 2 we discuss the BreakBRD and
control samples comprised of typical star-forming and quenching galaxies, the
H I data, and optical spectroscopic data. In Section 3 we present our results,
specifically examining H I fractions and depletion times, nuclear gas-phase
metallicities, and gas kinematics of BreakBRDs relative to our control
samples. In Section 4, we discuss how our results inform whether BreakBRDs are
indeed a unique subset of quenching galaxies, and what may be driving their
behavior. A summary of our results is presented in Section 5. Throughout this
paper we assume a flat cosmology with $H_{0}=70\,{\rm km\,s^{-1}\,kpc^{-1}}$,
$\Omega_{m}=0.3$, and $\Omega_{\Lambda}=0.7$.

##  2 Sample and Data

###  2.1 The BreakBRD sample

BreakBRD galaxies are characterized as having star-forming bulges and
quiescent disks. They are defined as having no ongoing AGN activity. For our
study, we start with the same sample of BreakBRDs presented in Tuttle &
Tonnesen (2020), whose selection we briefly summarize here. The BreakBRDs are
selected from a sample of $\sim$70,000 local ($z<$0.05) face-on SDSS galaxies
used for bulge/disk decomposition by Lackner & Gunn (2012). To ensure robust
bulge-disk decomposition, the parent sample is first limited to galaxies with
absolute $r$-band magnitude $M_{r}<-17$, axial ratio $b/a>0.7$, and bulge/disk
colors of $0.2<g-r<0.9$. The small fraction (125 total) of galaxies with non
star-forming disks ($g-r>0.6$) and recent central star formation (based on
$D_{N}4000$) are the BreakBRDs. The sample is further refined by rejecting
galaxies classified as AGN using [O III]/H$\beta$ vs. [N II]/H$\alpha$ Balwin,
Phillips, and Tellervich (BPT) diagrams (Baldwin et al., 1981; Kewley et al.,
2006).

###  2.2 HI data

Global H I properties used in this study are derived from 21cm observations
carried out with single-dish radio telescopes. A total of 73 BreakBRD galaxies
fall within the footprint of the Arecibo Legacy Fast ALFA (ALFALFA) survey
(Haynes et al., 2018). These cases were identified by crossmatching the
BreakBRD optical positions with the ALFALFA H I positions from the
$\alpha$.100 source catalog using a match radius of 2′. For any match on-sky,
we also require the optical redshift to fall within 1.5 times the H I $W_{50}$
linewidth from the H I redshift. For galaxies falling within the ALFALFA
footprint but lacking counterparts in the ALFALFA catalog, we extract each of
their spectra from the original ALFALFA data cubes in $4\arcmin\times
4\arcmin$ boxes centered on each galaxy position. We then measure the $rms$
noise directly in the spectra and estimate an H I upper limit using Equation 4
of Stark et al. (2021) assuming an intrinsic galaxy linewidth of $200\,{\rm
km\,s^{-1}}$.

ALFALFA observations had a fixed integration time of less than one minute per
beam, which, combined with the required S/N of 4.5 for detections in the
$\alpha$.100 catalog, yielded limited sensitivity to H I mass at the distance
of the breakBRD sample. Only 29% of the 73 breakBRDs had published ALFALFA
measurements. For galaxies lacking ALFALFA data, or those that had weak
ALFALFA detections ($S/N<5$) or upper limits ($M_{\rm HI}/M_{*}>0.05$), were
observed using the Robert C. Byrd Green Bank Telescope (GBT) under project
codes AGBT20B-261 and AGBT22A-436. In total, 98 galaxies were observed. The
L-band receiver and Versatile GBT Astronomical Spectrometer (VEGAS) backend
were used. Observations were performed using position switching with five
minute scans. Integration times varied, set either by the time needed to reach
an integrated flux $S/N>5$ or an H I upper limit of $M_{\rm HI}/M_{*}<0.05$.
Our approach resulted in 80% of breakBRDs observed with the GBT yielding
detections. Periodic observations of bright radio standards were used to check
the pointing and flux calibration of our observations (see Appendix A for
further information).

GBT data were reduced using the pipeline designed for the HI-MaNGA survey
(Masters et al., 2019; Stark et al., 2021), which we briefly summarize here.
Each scan is inspected for contamination from Global Positioning Satellite
(GPS) interference at 1385 MHz (a recession velocity of $\sim 8500\,{\rm
km\,s^{-1}}$ for the H I rest frequency). Any of the ten second sub-
integrations containing this signal are rejected. Scans are then averaged and
any remaining narrow radio frequency interference (RFI) features flagged. The
data are boxcar and hanning smoothed to a spectral resolution of $\sim
10\,{\rm km\,s^{-1}}$. A polynomial baseline is fit and subtracted from each
spectrum using only signal-free regions. The rms noise is measured from a
large contiguous, flat, and signal-free region of the baseline-subtracted
spectrum. For spectra with clear 21cm signals (based on a by-eye inspection of
the spectrum at the expected location of the 21cm emission line), the
integrated flux, systemic velocity, and linewidth are measured. For non-
detections, a flux upper limit is estimated assuming a linewidth of $200\,{\rm
km\,s^{-1}}$.

Cases of likely source confusion are also identified following the methodology
outlined in Stark et al. (2021). Cases where multiple optical counterparts lie
within a 1.5 beam radius of each observation while also having optical
redshifts within 1.5$W_{50}$ of the detected H I emission line velocity
centroid are flagged as possibly confused. In a second step, the H I mass
likelihood distribution is estimated for each optical counterpart using $u-i$
color, stellar mass, and mean $r$-band surface brightness, which is then
converted into an observed 21cm flux likelihood using the galaxies’ luminosity
distances and angular separation from the beam center (the latter accounts for
the declining beam sensitivity away from its center). The likelihood
distributions are then used to estimate the probability that at least 20% of
the H I flux in an observation comes from galaxies other than the primary
target. Any galaxies where this probability is $>$10% are removed from our
analysis. 37/98 (38%) GBT observations and 8/73 (11%) ALFALFA observations are
flagged as confused via this step.

A sample of our complete catalog containing both ALFALFA and GBT measurements
is presented in Table 1. The full version is available as a machine readable
table in the online version of this article. It contains 98 GBT observations
and 73 ALFALFA observations for 118 unique galaxies. Users should be aware
that many galaxies have entries for both GBT and ALFALFA data; even when the
GBT data is deeper, we include the parameters from the ALFALFA survey for
completeness and because use-cases for the catalog may differ between users.
From our data set, we generate a final catalog for our analysis (with one
measurement per galaxy) using the following decision tree to determine which
observation to use in cases where a galaxy has both GBT and ALFALFA data: (1)
If both observations are unconfused detections, the one with the higher $S/N$
is used. (2) If both observations are non-detections, the one yielding the
stronger upper limit is used. (3) If one observation is a confused detection
and the other an unconfused detection, the unconfused detection is used. (4)
If one observation is an unconfused detection and the other an upper limit,
the detection is used. (5) If one observation is a confused detection and the
other an upper limit, the upper limit is typically used, except for a handful
of cases where the flux measured from the confused detection is actually lower
than the upper limit (this can occur in cases with very deep GBT data and
relatively shallow ALFALFA data), in which case the confused detection is
adopted as a flux upper limit. (6) If both observations are confused
detections, neither is used in any analysis of H I properties.

The following is a complete description of the columns in the catalog.

  * •

SDSS ID: The identifier for a given galaxy.

  * •

$V_{\rm opt}$: optical heliocentric recession velocity.

  * •

$t_{on}$: Total on-source time for a given galaxy. This does not include the
time spent in the OFF position.

  * •

$\sigma_{rms}$: rms noise measured in the spectrum after boxcar and hanning
smoothing to a resolution of ${\sim}10\,{\rm km\,s^{-1}}$.

  * •

$F_{\rm HI}$: total integrated 21cm flux.

  * •

$\sigma_{F_{HI}}$: uncertainty on integrated 21cm flux.

  * •

$f_{\rm peak}$: peak flux density of the 21cm profile.

  * •

$\log{M_{\rm HI}}$: Integrated H I mass for detections calculated using
Equation 5 from Stark et al. (2021).

  * •

$\log{M_{\rm HI,lim}}$: Derived H I upper limit for non-detections assuming an
intrinsic linewidth of $200\,{\rm km\,s^{-1}}$, calculated using Equations 3
or 4 from Stark et al. (2021) for GBT and ALFALFA data, respectively.

  * •

$W_{F50}$: H I linewidth measured between the points where the flux density is
equal to half of the peak flux density on each side. The peak flux densities
on each side of the profile are typically measured at the nearest “horn” for a
classic two-horned profile, or at the central peak for a Gaussian-shaped
profile. The channel where the flux density equals half the peak value is
estimated by fitting a line on either side of the profile for the data with
flux density between 0.1-0.9 times the peak flux density. The linewidths are
corrected for instrumental broadening following Springob et al. (2005), but
are not corrected for turbulence.

  * •

$V_{\rm HI}$: Heliocentric recession velocity derived from the H I emission
line. This is calculated by taking the midpoint of where the flux density
reaches half of its peak value on either side of the profile (where the peak
flux density is either from the nearest “horn” for a two-horned profile, or
the central peak for a Gaussian-shaped profile).

  * •

$p_{conf}$: Probability that the H I data are confused estimated following the
methodology described in Stark et al. (2021). The value specifically conveys
the likelihood that at least 20% of the H I flux arises from a galaxy other
than the primary target.

  * •

source: Source of the 21cm observation. G: Green Bank Telescope; A: ALFALFA
survey (Arecibo telescope; Haynes et al. 2018).

=5cm

Table 1: H I Measurements for the BreakBRD sample∗

SDSS ID | $V_{\rm opt}$ | $t_{\rm on}$ | $\sigma_{rms}$ | $F_{\rm HI}$ | $\sigma_{F_{\rm HI}}$ | $f_{\rm peak}$ | $M_{\rm HI}$ | $M_{\rm HI,lim}$ | $v_{\rm HI}$ | $W_{\rm F50}$ | $p_{\rm conf}$ | sourceaaG: GBT, A: ALFALFA
---|---|---|---|---|---|---|---|---|---|---|---|---
|  $\rm km\,s^{-1}$, | s | mJy | $\rm Jy\,km\,s^{-1}$ | $\rm Jy\,km\,s^{-1}$ | mJy | $\log{M_{\odot}}$ | $\log{M_{\odot}}$ | ${\rm km\,s^{-1}}$ | ${\rm km\,s^{-1}}$ |  |
SDSS J011344.94$+$154100.3 | 13191 | 1195 | 1.35 | 0.74 | 0.09 | 5.92 | 9.79 |  $\cdots$ | 13284 | 207 | 0.93 | G
SDSS J011728.10$+$144215.9 | 12891 | 2989 | 0.71 | 0.84 | 0.06 | 4.41 | 9.81 |  $\cdots$ | 12823 | 292 | 0.00 | G
SDSS J011858.59-002606.4 | 14090 | 5976 | 0.57 | 0.17 | 0.03 | 2.07 | 9.21 |  $\cdots$ | 14227 | 104 | 0.00 | G
SDSS J012132.95-005811.7 | 14690 | 6576 | 0.64 | 0.32 | 0.04 | 2.78 | 9.51 |  $\cdots$ | 14611 | 224 | 0.00 | G
SDSS J074602.40$+$184301.7 | 13848 | 597 | 1.88 | 1.76 | 0.17 | 11.39 | 10.20 |  $\cdots$ | 13863 | 369 | 0.89 | G
SDSS J075008.55$+$500243.1 | 7041 | 557 | 1.68 | 1.21 | 0.12 | 8.65 | 9.45 |  $\cdots$ | 7035 | 211 | 0.00 | G
SDSS J075158.32$+$154241.0 | 14090 | 10465 | 0.48 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ | 8.95 |  $\cdots$ |  $\cdots$ | 0.00 | G
SDSS J075822.53$+$121208.8 | 14990 | 897 | 1.74 | 2.82 | 0.18 | 9.31 | 10.47 |  $\cdots$ | 14876 | 434 | 0.96 | G
SDSS J080643.29$+$453938.0 | 10193 | 9049 | 0.49 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ | 8.68 |  $\cdots$ |  $\cdots$ | 0.00 | G
SDSS J082127.82$+$122333.3 | 12591 | 5372 | 0.75 | 0.24 | 0.05 | 1.97 | 9.25 |  $\cdots$ | 12492 | 235 | 0.00 | G

**footnotetext: This table is available in its entirety in the online version
of this manuscript.

###  2.3 Optical Spectroscopic Data

Fourteen BreakBRD galaxies have optical integral field unit (IFU)
spectroscopic data from the Sloan Digital Sky Survey (SDSS-IV) MaNGA Nearby
Galaxies at Apache Point Observatory (MaNGA) survey (Bundy et al., 2015). We
use the final release (SDSS DR17) data products generated by the latest MaNGA
Data Analysis Pipeline (DAP, Westfall et al. 2019). Our study makes use of the
stellar and gas kinematic maps generated by this pipeline.

For our full breakBRD sample (not just the fourteen with MaNGA data), we also
use spectroscopic measurements from the SDSS Legacy Survey (York et al.,
2000), specifically making use of several emission line fluxes. These data are
limited to the central 3″ of each galaxy so do not necessarily reflect global
galaxy conditions.

###  2.4 Global Star Formation Rates and Stellar Masses

For consistency, total galaxy star formation rates and stellar masses are
taken from Chang et al. (2015), who model spectral energy distributions (SEDs)
for the full SDSS Legacy spectroscopic sample using SDSS optical and WISE
infrared photometry. The star formation rates from this analysis importantly
incorporate dust-obscured star formation. We use the median of the SFR and
$M_{*}$ likelihood distributions generated for each galaxy.

###  2.5 Control Sample Generation

Control samples for comparison to the BreakBRD sample are drawn from the SDSS-
IV MaNGA survey, specifically the subset of MaNGA with global 21cm
measurements from the HI-MaNGA follow-up survey (Masters et al., 2019; Stark
et al., 2021). Two basic control samples are generated. The first is composed
of “typical” star-forming galaxies that lie on the “Star Forming (Main)
Sequence”, selected as having EW(H$\alpha$)$>6\AA$ measured within their half-
light radii using MaNGA IFU data (Sánchez et al., 2014; Cano-Díaz et al.,
2019) and [N II]/H$\alpha$ and [O III]/H$\beta$ line ratios inconsistent with
AGN. This control sample is given the shorthand, “SFS” throughout this paper.
The second control sample is composed of galaxies in the “Green Valley”
between star forming and quenched galaxies, selected as having
$3<$EW(H$\alpha$)$<6\AA$, and is given the shorthand, “GV” throughout this
paper. As has been discussed in Tuttle & Tonnesen (2020), and further in our
own study, BreakBRDs contain star formation, but show evidence of possible
quenching. The star-forming and green-valley control samples provide a means
of understanding how BreakBRDs are consistent with, or distinct from, these
two key stages of galaxy evolution.

For each analysis, we customize the control sample generation depending on
which galaxy parameters must be fixed. In some cases, it may be sufficient to
simply match on stellar mass, while in other cases, we may need to match other
properties (e.g., redshift, axial ratio) to avoid bias caused by secondary
observational effects. In all cases, we always match in stellar mass to within
0.2 dex of each primary galaxy, with the value of 0.2 dex chosen since it is a
typical systematic stellar mass uncertainty (Kannappan & Gawiser, 2007). If
any additional properties are fixed for a given analysis, they will be
mentioned in the corresponding text in Section 3.

To improve the statistical strength of our analysis, when possible we generate
control samples that are larger than the primary sample but have the same
distribution of properties. To generate these control samples, for each
primary galaxy, we draw a unique random control galaxy from all available
galaxies with properties similar to the primary. This process is repeated
until there are no remaining control galaxies for all galaxies in the primary
sample. In the figures demonstrating comparisons between the breakBRD and
control samples, the number of galaxies in each sample is provided in the
figure caption.

##  3 Results

In the following section we present the results of our analysis investigating
how the gas content and properties of BreakBRDs differ from the population of
SFS and GV galaxies. In Section 3.1, we explore differences in global HI-to-
stellar mass ratios and H I depletion times. In Section 3.2, we investigate
central gas-phase metallicities, and in Section 3.3, we identify key
differences in gas kinematic properties.

###  3.1 H I Fractions and Depletion Times

The left panel of Figure 1 shows the H I-to-stellar mass ratio ($M_{\rm
HI}/M_{*}$) versus stellar mass ($M_{*}$) relationship for all BreakBRDs and
the corresponding control samples, which are matched in stellar mass. The
total numbers in each sample are indicated in the right-hand panel of Figure
1. All the plotted samples contain significant numbers of upper limits,
limiting the comparison of the $M_{\rm HI}/M_{*}$ distributions to some
degree, but it is clear that the BreakBRD sample does not generally extend to
as high $M_{\rm HI}/M_{*}$ as the SFS control sample, and it appears to have a
distribution roughly comparable to the GV sample. To further investigate the
distributions, the right panel of Figure 1 plots the cumulative distribution
function (CDF) of $M_{\rm HI}/M_{*}$ for the three samples. Due to the
presence of upper limits, we employ survival analysis methods to estimate the
CDF, specifically using the Kaplan-Meier estimator from the lifelines Python
package (Davidson-Pilon, 2019) to determine the survival function, $S$, for
$M_{\rm HI}/M_{*}$, from which the CDF is calculated as $1-S$. It can be
clearly seen that the CDF for BreakBRD galaxies has a median value of $M_{\rm
HI}/M_{*}$ roughly 0.5 dex below that of the SFS control sample. This analysis
confirms earlier results from Tuttle & Tonnesen (2020) that used a smaller
subset of data from just ALFALFA, but the deeper H I observations also show
that BreakBRDs are not uniformly gas-poor either. At fixed stellar mass, their
gas fractions still span $\sim$1 dex. The CDF for the GV control does not have
a well-constrained mean value due to the large fraction of upper limits, but
the portion of the CDF we do constrain implies lower typical gas fractions
compared to BreakBRDs. Therefore, BreakBRDs appear to have typical gas
fractions intermediate between those in the Green Valley and the Star Forming
Sequence.

Figure 1: (left) HI-to-stellar mass ratio versus stellar mass for BreakBRD
galaxies and the SFS and GV control samples. H I detections and non-detections
are shown for each sample. (right) The cumulative distribution function (CDF)
of HI-to-stellar mass ratio for the BreakBRDs and the control samples,
determined using the Kaplan-Meier estimator. Shaded regions indicate the 68%
confidence intervals. BreakBRDs have typcial H I-to-stellar mass ratios
intermediate between SFS and GV galaxies.

We next investigate gas consumption timescales in BreakBRDs using the H I
depletion time, $\tau_{\rm HI}$, defined as

$\tau_{HI}=M_{HI}/SFR$ (1)

where $SFR$ is the global star formation rate. Although this metric makes
simplistic assumptions (e.g, constant SFR, no gas replenishment, and all gas
available for star formation) it provides a rough indicator of the future star
formation potential of a galaxy.

The left panel of Figure 2 shows $\tau_{HI}$ vs. stellar mass for BreakBRDs as
well as the SFS and GV control samples, which are identical to those used to
study the $M_{\rm HI}/M_{*}$ vs. $M_{*}$ relation. As in Figure 1 there are
large numbers of upper limits, but the distribution of $\tau_{HI}$ for the SFS
control sample clearly extends to higher values generally unpopulated by
BreakBRDs. The right panel of this figure shows the estimated CDFs, which
emphasizes this point further. Using these CDFs, we estimate a median
$\tau_{HI}$ of just over 1 Gyr for BreakBRDs, whereas the SFS control sample
has a median $\tau_{HI}\sim 10\,{\rm Gyr}$.

The distribution of $\tau_{HI}$ for the GV control sample is very distinct
from that of the BreakBRDs. Although the details of the GV sample H I
distribution carry large uncertainties, it is still clear that the GV
population contains a larger fraction of galaxies with very long depletion
times ($>>10$ Gyr) compared to the SFS and especially the BreakBRD samples.
The long depletion times of GV galaxies occur despite them having the lowest
typical H I fraction, implying that the long depletion times are the result of
extremely low SFRs. This is distinct from BreakBRDs which have H I fractions
that are lower but global SFRs that are broadly consistent with SFS galaxies,
implying it is the decrease in H I content, not SFR, that is driving their
short depletion times. The implications of this interpretation will be
discussed further in Section 4.

Figure 2: (left) H I depletion time versus stellar mass for BreakBRD galaxies
and the SFS and GV control samples. Detections and non-detections are shown
for each sample. Symbols are the same as in Figure 1. (right) CDFs of H I
depletion time for the three samples, again determined using the Kaplan-Meier
estimator. Shaded regions indicate the 68% confidence interval. BreakBRDs have
H I depletion times that are shorter than both SFS and GV galaxies.

###  3.2 Gas Phase Metallicity

We next examine the gas-phase metallicities of BreakBRDs, specifically
focusing on central metallicities obtained via optical spectroscopic
measurements in the central 3$\arcsec$ of all galaxies from the SDSS Legacy
spectroscopic survey.

Nuclear gas phase metallicities ($12+\log{O/H}$) are estimated using the O3N2
strong line method of Marino et al. (2013) that combines the [O
III]5008Å/H$\beta$ and [N II]6585Å/H$\alpha$ line ratios. To ensure the
metallicity calibration is being applied appropriately, we exclude any
galaxies whose line ratios fall on the LI(N)ER or Seyfert region of the [O
III]/H$\beta$ vs. [N II]/H$\alpha$ and [O III]/H$\beta$ vs. [S II]/H$\alpha$
BPT diagrams, yielding a total of 77 BreakBRD galaxies for this analysis. The
majority of galaxies excluded here are classified as “composite”. Despite
cutting the sample, Kolmogorov-Smirnov (K-S) tests performed on the stellar
mass and SFR distributions indicate that the subset of BreakBRDs used here is
a representative subset of the full BreakBRD sample. Unlike in Section 3.1, we
do not require H I observations for this analysis. The control samples are
matched in stellar mass. Due to the combination of weaker emission lines and
larger fractions of AGN/Seyfert/LINER BPT classifications among the GV sample,
there are fewer galaxies eligible for our analysis. A total of 29 BreakBRDs
had no viable match among the GV sample. Therefore, we separate our analysis
of BreakBRD and GV galaxies from that of BreakBRDs and SFS galaxies, only
plotting the BreakBRDs with available matches in the former.

Figure 3 shows the distribution of nuclear gas-phase metallicity for BreakBRDs
and the control samples. While both the BreakBRD and SFS control sample
distributions have approximately equal mean metallicity of $12+\log{O/H}\sim
8.55$, the BreakBRD metallicity distribution is significantly more skewed
towards larger metallicities (Anderson-Darling and Shapiro-Wilk tests confirm
the nuclear metallicity distribution deviates significantly from Normal). A
K-S test on the nuclear metallicity distribution of the two samples returns
only a $5\times 10^{-5}$% chance that they follow the same distribution.
Meanwhile, the BreakBRD and GV samples again have very similar mean values,
and a K-S test returns a p-value of 0.25, indicates their metallicity
distributions are the same.

Figure 3: Normalized distribution of central SDSS fiber metallicity
measurements for BreakBRDs alongside the SFS control (left) and GV control
(right) samples. BreakBRDs have a broader distribution of gas-phase
metallicity compared to SFS galaxies, and the distribution is skewed towards
above-average values. BreakBRDs and GV galaxies appear to have consistent
metallicity distributions.

###  3.3 Gas and Stellar Kinematics

Integrated 21cm observations not only provide information regarding total gas
content, but also basic kinematic information through linewidths and spectral
profile asymmetries, although it should be noted that any spectral profile
asymmetry could be due to asymmetries in kinematics or gas density
distributions. Spatially resolved velocity fields using optical IFU data are
available for a small subset of our sample, and provide an even more powerful
approach to identifying disturbances in the internal motions of galaxies. This
information can provide valuable clues as to what is driving the evolution of
galaxies.

####  3.3.1 The Baryonic Tully-Fisher Relation

The Baryonic Tully-Fisher Relation (BTFR) is a fundamental scaling relation
between baryonic mass (in this case, stellar plus cold gas mass) and rotation
velocity for disk galaxies. The BTFR holds over several orders of magnitude,
from gas-rich dwarfs to giant galaxies (McGaugh et al., 2000; Stark et al.,
2009). Therefore, deviations may highlight galaxies undergoing unique
evolutionary processes.

In Figure 4, we plot the BTFR for 15 BreakBRDs and a control sample of 105 SFS
galaxies matched on stellar mass and axial ratio. Matching on axial ratio is
to ensure that differences in the distribution of inclination do not lead to
any spurious results, although both samples are inclination-corrected. The
small sample size used here is largely due to the requirement of peak $S/N>5$
on H I detections (where peak $S/N=f_{\rm peak}/\sigma_{\rm rms}$), which was
enforced because linewidths become significantly more unreliable at lower
$S/N$ (Stark et al., 2013). We also require the detections to not be confused.
We do not plot the GV control sample here as there are too few galaxies with
strong enough H I detections.

We plot the BTFR following the methodology of Goddy et al. (2023). Rotation
velocity is calculated as $V_{\rm rot}=W_{F50}/2\sin{i}$ where inclination is
estimated using Equation 5 from Masters et al. (2019). To match the baryonic
mass used in Goddy et al. (2023), we add 0.24 dex to the logarithm of our
stellar masses to convert from a Chabrier to Salpeter IMF, and we multiply our
stellar masses by a factor of 1.07 as an approximation for the contribution of
molecular hydrogen to the baryonic mass budget. Lastly, we multiply our H I
masses by 4/3 to correct for the contribution of Helium. In Figure 4, we
overplot the derived forward fitted BTFR from (Goddy et al., 2023) that does
not apply a turbulence correction to the velocity measurements (to be
consistent with our own data), and is reproduced here for convenience:

$M_{\rm bary}=2.75V_{\rm rot}+4.39$ (2)

The forward ordinary least-squares fit is purposefully used instead of the
bisector fit because we will examine baryonic mass residuals from the BTFR
(the forward fit minimizes scatter along this axis).

Residuals from the BTFR are shown in the right panel of Figure 4. A K-S test
applied to the residuals for BreakBRDs and the SFS control samples indicates
they are significantly different with only a 1.2% chance they follow the same
underlying distributions. We find median residuals of $-0.14\pm 0.07$ and
$0.08\pm 0.03$ for BreakBRDs and SFS galaxies, respectively, and the standard
deviations of the residuals are estimated to be $0.72\pm 0.26$ and $0.57\pm
0.09$. Uncertainties on these quantities are estimated from bootstrapping. We
conclude that BreakBRDs sytematically deviate from typical star forming
galaxies around the BTFR, having less total baryonic mass for their rotation
velocity.

Figure 4: (left) The BTFR for BreakBRDs and a control sample of SFS galaxies.
GV galaxies are not analyzed due the low rate of strong H I detections needed
for reliable $W_{F50}$ measurements. The fitted relation is from (Goddy et
al., 2023). (right) Baryonic mass residuals from the BTFR for the two samples.
BreakBRDs fall systematically below the BTFR.

####  3.3.2 H I Profile Asymmetry

We next examine the distribution of 21cm profile asymmetries in our samples.
Asymmetries can indicate a lopsidedness in either the H I distribution or its
kinematics, and may be indicative of a number of processes including tidal
interactions, gas accretion, and/or disk instabilities (see Section 4.2.2 for
futher discussion). In this work, H I profile asymmetry, $A_{HI}$, is defined
as the logarithmic ratio of 21cm flux above and below a galaxy’s systemic
velocity:

$A=\log{\frac{F_{l}}{F_{h}}}$ (3)

where $F_{l}$ and $F_{h}$ are the integrated fluxes above and below the
systemic velocity. The use of a logarithm is mostly for convenience when
calculating this quantity; it ensures the absolute magnitude of the asymmetry
is independent of whether $F_{l}$ or $F_{h}$ is larger.

The choice of how to define the systemic velocity of the H I profile is an
important one. Most past studies of integrated H I asymmetry use the systemic
velocity derived from the H I profile itself. This may be, for example, a
luminosity weighted mean over the spectral profile or the midpoint between the
two sides of the profile where the flux density reaches 50% of the maximum
value. Regardless, defining a galaxy’s systemic velocity from the H I profile
itself runs the risk of hiding the real asymmetry because the H I systemic
velocity is typically defined to be near the center of the H I distribution.
Alternatively, one may use an independent measure of a galaxy’s systemic
velocity, and in our case, SDSS fiber spectroscopy provides a useful
alternative, although this approach is not without its own set of potential
systematic errors. For example, while these measurements are nominally
conducted on the photometric centers of galaxies, the optical center may not
exactly match the kinematic center of a given galaxy. We opt to conduct our
analysis both ways, first defining the systemic velocity from the H I profile
($V_{HI}$), and second, using the independent redshift measured from optical
SDSS single fiber spectroscopy. The former runs the risk of underestimating
the true H I asymmetry, while the latter runs the risk of overestimating the
true H I asymmetry, so they likely both bracket the true values, and if trends
are seen using both definitions, they are more likely robust. The first
definition of H I asymmetry also enables better comparison to past work, while
we see the latter as less biased.

For our analysis, we again limit ourselves to cases with H I peak $S/N>5$ to
ensure reliable estimation of the systemic H I velocity. The SFS control
sample is matched in stellar mass and axial ratio, the latter because H I
profile shape, and the potential of seeing H I asymmetries, is inclination-
dependent. Once again, we can only consider non-confused detections here. We
do not analyze GV galaxies here due to the lack of high $S/N$ H I detections.

Figure 5 shows the distribution of the absolute magnitude of asymmetry for the
BreakBRD and SFS sample under both asymmetry definitions. In both cases the
distributions of asymmetry are significantly different between the BreakBRD
and SFS samples. K-S tests applied to both cases estimate that the BreakBRD
and control samples have only 1.7% and 3.8% chance of having the same
distribution on the left- and right-hand sides of Figure 5, respectively. We
note that the requirement for peak $S/N>5$ in this analysis was to ensure that
the central H I velocity is well-constrained, but the requirement is not
technically necessary for the analysis using the optical velocity to define
the galaxy systemic velocity. Lowering the $S/N$ requirement to $>3$ increases
the sample by $\sim 2$ and yields an even stronger difference in the H I
asymmetry distributions. We conclude that BreakBRDs are more likely to have
asymmetric H I profiles compared to SFS galaxies, indicating more frequent
asymmetry either in their H I distributions, kinematics, or both.

Figure 5: Distribution of H I profile asymmetries for BreakBRD and SFS
samples, defining the systemetic velocity using the H I profile itself (left)
and velocity estimated from optical spectroscopy (right). Both cases show more
high-asymmetry profiles among the BreakBRD galaxies than the SFS galaxies.

####  3.3.3 Kinematic Misalignment

Kinematic quantities from integrated H I spectra can be difficult to interpret
given that one cannot disentangle the flux and velocity distributions.
Thankfully, fourteen BreakBRD galaxies have spatially resolved optical
spectroscopic data from the SDSS-IV MaNGA survey. Using this data we conduct a
visual, qualitative comparison of the orientation of the gas and stellar
velocity fields.

Figure 6 shows the $SDSS$ $gri$ image, stellar velocity field, and gas
velocity field for each of the fourteen BreakBRDs with MaNGA data. Each galaxy
is labeled with a visual classification for the gas/stellar velocity field: A
(aligned gas/stellar velocity fields), M (misaligned gas/stellar velocity
fields), D (strongly distorted gas velocity field), U (uncertain). The key
difference between misaligned and distorted gas velocity fields is that
misaligned cases still show clear ordered rotation, whereas distorted cases
are dominated by noncircular motions whose kinematic position angle cannot
easily be discerned. These classifications are all done by-eye, as strong
disagreement in the gas and stellar velocity fields is often clearly visible
in spatially resolved maps, but subtle differences may be missed.

In one case (8550-12703), we are unable to determine the level of agreement
between the gas and stellar velocity field, possibly due to an extremely face-
on configuration. Of the remaining 13, 3/13 show co-rotating velocity fields
(0.13-0.36), 5/13 show clear misalignment (0.26-0.52), and 5/13 show clear
distortion (0.26-0.52). All confidence intervals are 68% and come from
binomial statistics. Therefore, the majority of BreakBRDs (10/13, 68%
confidence interval (0.64-0.86) do not show simple co-rotating gas and stars.
This finding, combined with the increased H I asymmetry, may indicate
BreakBRDs frequently host disturbed/reforming gas disks. Unfortunately, only a
few galaxies contain velocity fields and $S/N>5$ H I spectra good enough to
reliably measure H I asymmetry, so we cannot robustly determine whether the
behavior in optical velocity is correlated with the H I asymmetry.

Figure 6: SDSS $gri$ images, stellar velocity fields, and gas velocity fields
for the fourteen BreakBRDs with MaNGA IFU data, where blue indicates positive
values and red indicates negative values. Labels in the bottom left panel of
each $gri$ image indicate the classification of the gas velocity field(A:
aligned, M: misaligned, D: distorted, U: uncertain). Approximately two-thirds
of galaxies show either strongly misaligned or distorted gas velocity fields
relative to their stellar velocity fields.

##  4 Discussion

In Section 3, we presented the gas properties of BreakBRD galaxies,
particularly in relation to galaxies on the star-forming sequence (SFS) and
Green Valley (GV), in an effort to find clues into their origin, specifically
focusing on H I-to-stellar mass ratio ($M_{\rm HI}/M_{*}$), H I depletion time
($\tau_{HI}$), central gas-phase metallicity ($12+\log{O/H}$), and gas
kinematics. We find that BreakBRDs have H I fractions that fall between
typical values for SFS and GV galaxies. In contrast, their H I depletion times
are on average much lower than both SFS and GV galaxies. BreakBRDs have a
broader distribution of gas-phase metallicities, one that skews towards
slightly higher metallicities than the control sample. In comparison to the
SFS control, they fall systematically low on the BTFR, and they have
significantly higher rates of H I profile asymmetry, even after accounting for
different definitions of the kinematic center. The majority of galaxies with
spatially resolved velocity fields show either highly disturbed internal gas
motions or significant misalignment with stellar rotation.

In the following section, we discuss how these results inform our
understanding of how BreakBRDs fit into our understanding of galaxy evolution,
first discussing whether BreakBRDs are truly a quenching population, and then
whether their behavior is driven by merger activity.

###  4.1 Are BreakBRDs on the path towards quenching?

Tuttle & Tonnesen (2020) describe the BreakBRD population as not clearly
fitting into a single evolutionary paradigm, e.g., they are not all satellites
potentially undergoing ram-pressure stripping, or barred galaxies experiencing
gas inflows. From their analysis, it was unclear whether they fell into the
population of quenching galaxies, or normal galaxies undergoing a
redistribution in the location of star formation (due to e.g., gas flows), or
some combination of the two.

Investigations into the spatially resolved star formation properties of
BreakBRDs using MaNGA data (McKay et al., in preparation) show that BreakBRDs
have clearly deficient disk star formation relative to “typical” SFS galaxies
that show “inside-out” evolution (disks bluer than bulges). The specific star
formation rates (sSFRs) of the disks fall below the main-sequence values, but
are not yet consistent with fully quenched galaxies. Meanwhile, BreakBRDs have
bulge and global sSFRs that are generally consistent with SFS galaxies. These
findings disfavor any scenario where gas has been redistributed from the disk
to the bulge, which would likely drive an enhancement in central sSFR.
Instead, these findings favor a scenario where galaxy-wide star formation is
shutting down from the outside-in.

Our own results show BreakBRDs have much lower gas content and H I depletion
times than SFS galaxies. These results imply that BreakBRDs are exhausting
their gas supply. Presumably, given the outside-in cessation of star
formation, the disk H I has either been depleted or directly removed, with no
further accretion of gas to balance the loss. While we have no spatial
information on the H I distribution, the simulations of Tonnesen et al. (2023)
show BreakBRDs have lower than average CGM masses, consistent with them
lacking gas infall to replenish their H I disks. (Kopenhafer et al., 2020)
find that BreakBRDs in simulations have lower overall dense gas mass, but
comparable dense gas within their central 2 kpc compared to typical star-
forming galaxies. These studies, combined with the McKay et al. (in
preparation) results, imply that the short H I depletion times in BreakBRDs
are the result of lower overall gas content but still nominally normal levels
of SF. If instead the cold gas in BreakBRDs had been redistributed towards the
center, the overall H I content should not evolve significantly unless there
was a major starburst, which we do not see.

Their more rapid gas exhaustion distinguishes BreakBRDs from the general
population of quenching galaxies that occupy the Green Valley. Most Green
Valley galaxies show extremely long gas depletion times (see our Fig. 2, as
well as Janowiecki et al. 2020), implying that although some gas may be
present, it does not have the appropriate conditions to transition into stars.
We see two possible reasons for why BreakBRDs have shorter depletion times.
They may be in an earlier stage of quenching, where the gas supply has
declined but there is still enough gas to sustain “normal” levels of SF for
now. Alternatively, BreakBRDs may have experienced events (e.g., tidal
interactions) that triggered nuclear inflows enabling gas to reach densities
to trigger more rapid star formation. In the first scenario, we expect
BreakBRDs to have higher gas fractions than Green Valley galaxies, which they
do on-average (Figure 1). The gas fractions expected in the second scenario
may depend on the mode and time elapsed since gas was perturbed and driven
inwards. If BreakBRDs are the result of very recent interactions, we might
expect their gas fractions to be comparable to other Green Valley galaxies. If
significant time has passed since the initial inflow, BreakBRDs may have
consumed enough gas in star formation to lead to reduced gas fractions
compared to galaxies in the Green Valley. However, these expectations may
break down if the star formation trigger was the result of a merger with
another galaxy that deposited material onto the BreakBRD. It is also important
to remember that the results of McKay et al. (in preparation) do not show
evidence for systematically higher SFR in BreakBRDs, disfavoring a “triggered
burst” scenario. We conclude that the H I fractions and SFRs more clearly
support the early-stage quenching scenario.

The slight increase in nuclear metallicity observed in much of the BreakBRD
sample may be another reflection of this population moving off the star
forming sequence. The fundamental mass-metallicity relation states that at
fixed stellar mass, galaxies with higher SFR will have lower metallicity
(Mannucci et al., 2010; Andrews & Martini, 2013). Such behavior implies that
at fixed mass, enhanced metallicity is a natural byproduct of the quenching
process. While a full review of the origin of the FMR is beyond the scope of
this work, an increase in the metallicity during the quenching phase may be
linked to a deficit of cold gas (which we have established for the BreakBRDs)
that would be sustained by fresh accretion and normally dilute metallicity.
Thus, their enhanced nuclear metallicities may be a reflection of them
transitioning to the quenched regime. A related result was reported by
Tonnesen et al. (2023), who found simulated BreakBRDs had elevated CGM
metallicities. Based on the lower than average CGM mass and weak SFR of
BreakBRDs, they argue that the most likely explanation for the enhanced
metallicities is a lack of pristine gas infall. We do note, however, that
while the distribution of central BreakBRD metallicities is skewed towards
higher values, it still shows a broad range.

There is an alternative explanation for at least some BreakBRDs: they are
previously quenched galaxies that experienced a recent accretion event,
potentially rebuilding a gas disk and reigniting star formation (see Kannappan
et al. 2009 for a discussion of such events). Such a scenario is supported by
the high rate of misaligned gas/stellar disks, which imply an external gas
origin. Accretion of gas may arise from mergers with a wide range of mass
ratios, potentially explaining the large diversity of H I fractions for
BreakBRDs. In such a scenario, there may be significant debris and any newly
formed gas disk may take significant time to resettle, which would in-turn
explain the high H I profile asymmetries. Tonnesen et al. (2023) find that the
CGM around BreakBRDs is misaligned as well, but it also has low angular
momentum. This combination will lead to gas to most likely enter the disk at
the center, reigniting star formation there first, and consistent with our
observations (van de Voort et al., 2015; Trapp et al., 2022). It is unclear in
the long run whether such BreakBRDs would move up into the star forming
sequence, or down into the quenched regime. A sample of galaxies similar to
BreakBRDs via their blue-centered color gradients, blue sequence E/S0s, are
thought to contain a significant number of rebuilding disks, although this
behavior is most common at lower stellar masses beyond those of our sample
(Kannappan et al., 2009). It is also worth noting that blue-sequence E/S0s
often have notably blue disks, while BreakBRDs are defined as having red
disks, so it is unclear if these populations can be treated similarly.
Furthermore, Kopenhafer et al. (2020) find that simulated BreakBRDs are indeed
quenching, disfavoring the idea that BreakBRDs may be rejuvenated for any long
period of time.

###  4.2 Are BreakBRDs the result of mergers

####  4.2.1 Morphological Evidence

Only a handful of BreakBRD galaxies show morphologies in their optical images
clearly indicative of ongoing merger activity (e.g., SDSSJ14454 in Figure 6).
However, blue-centered color gradients, which reflect a higher than normal
ratio of central to disk SF likely due to gas inflows, are another possible
indicator of past interactions (Kannappan, 2004; Gonzalez-Perez et al., 2011;
Stark et al., 2013). Bars may also drive such behavior, but the bar fraction
in BreakBRDs is $\sim$30-40% (Tuttle & Tonnesen, 2020), typical for the
general galaxy population based on SDSS imaging data (Masters et al., 2011),
and not high enough to explain the entire BreakBRD population. If breakBRDs
are typical SFS galaxies that experienced tidal- or bar-driven gas inflows,
these inflows would have to be significant enough to starve the disk of gas,
yet modest enough to not drive strong central starbursts (which BreakBRDs do
not have; McKay et al. in preparation).

To further test morphological signatures of merger activity, we employ the
major and minor merger classifications from Nevin et al. (2019) who combine
several classical quantitative morphological parameters (e.g., image
asymmetry, Gini, M20) from SDSS images into a single classifier using Linear
Discriminant Analysis (LDA). The LDA technique is estimated to be sensitive to
merger signatures for $>2$Gyr for both major and minor mergers. Results for
the BreakBRD sample and a stellar-mass and redshift matched SFS and GV control
samples (the latter to ensure similar physical resolution) are shown in Figure
7. All samples show broad distributions in merger probability. Kolmogorov-
Smirnov (K-S) and Anderson-Darling (A-D) tests are used to determine if the
distributions are different. Considering the BreakBRD and SFS control samples,
we calculate K-S and A-D p-values of $2\times 10^{-4}$ and $<0.001$ for the
major merger probability distributions, and p-values of 0.69 and $>0.25$ for
the minor merger probability distributions. Considering the BreakBRD and GV
control samples, we calculate K-S and A-D p-values of 0.05 and 0.04 for the
major merger probability distributions, and p-values of 0.31 and 0.20 for the
minor merger probability distributions. In other words, the minor merger
probabilities are effectively the same between all three samples, while the
major merger probabilities are significantly different between the BreakBRD
and SFS samples, and marginally different between the BreakBRD and GV samples.
However, even in cases where the KS/AD tests indicate significant differences,
meaningful differences in the distributions are difficult to discern by eye.
If we define anything with probability $>0.5$ as a merger, then BreakBRDs have
a major and minor merger rate (and 95% confidence interval) of 0.25
(0.18-0.33) and 0.46 (0.38-0.55). Meanwhile the SFS control sample has major
and minor merger rates of 0.19 (0.16-0.21) and 0.47 (0.44-0.50), and the GV
control sample has major and minor merger rates of 0.22 (0.16-0.31) and 0.54
(0.45-0.62). Therefore, based on available imaging data, BreakBRDs do not
appear to have a significantly higher merger fraction (at more than a
$2\sigma$ level) than the general star forming galaxy population.

Figure 7: Probabilities of BreakBRD and control galaxies being major mergers
(left) and minor mergers (right). All probabilities are based on the LDA
technique of Nevin et al. (2019) applied to SDSS imaging data. The minor
merger probabilities are statistically the same. The major merger probability
distributions are distinct, but there is no significant difference in the
fraction of major mergers classified as having probability $>0.5$.

####  4.2.2 Dynamical Evidence

The cold gas in galaxies typically reflects the influence of mergers and
interactions for $>$1Gyr after the initial encounter (Holwerda et al., 2011),
and therefore may be more sensitive to merger activity than analysis of
optical morphology. One of our results is that BreakBRDs systematically fall
below the BTFR, but the question that arises is whether this is due to them
having lower than average baryonic mass or higher than average linewidth.
Similar offsets below the BTFR have been seen by McGaugh & Wolf (2010) for
observations of ultra-faint Milky Way dwarfs, and by Glowacki et al. (2020)
among gas-poor galaxies in the Simba suite of simulations. Both authors cite
gas-loss/quenching as a possible cause of this deviation, and McGaugh & Wolf
(2010) correlate the offsets with tidal forces. Puech et al. (2010), who
analyzed the stellar mass TFR, also see large offsets associated with major
mergers, although their scatter is more symmetric about the mean relation. If
the gas disks of BreakBRDs were substantially disrupted by past or ongoing
mergers, more scatter in the BTFR would be expected as the linewidth no longer
traces simple circular velocity. Indeed, we find above-average H I asymmetries
for BreakBRDs, which could be linked to merger activity. However,
interpretation of H I asymmetries must be made cautiously; while there are
studies that have argued that high asymmetries are associated with mergers and
tidal interactions (Angiras et al., 2007; van Eymeren et al., 2011; Ramírez-
Moreta et al., 2018; Scott et al., 2018; Bok et al., 2019; Watts et al.,
2020), a similar breadth of literature has argued other processes, such as
cold gas accretion (Bournaud et al., 2005; Sancisi et al., 2008; Ramírez-
Moreta et al., 2018) and lopsided dark matter halos (Jog, 1997) may also play
a role, especially given the existence of highly asymmetric disks in the
absence of any clear companion (Richter & Sancisi, 1994; Haynes et al., 1998;
Espada, 2011). Watts et al. (2023) note that H I asymmetries can trace
disturbances that do not impact the central regions of galaxies,

It is noteworthy that the high rate of H I asymmetries in BreakBRDs is coupled
with a high rate (roughly two-thirds) of gas velocity fields that are
distorted or completely misaligned from their stellar components. This rate is
much higher than the overall galaxy population; using a broad sample of 1220
galaxies from the first year of SDSS-IV MaNGA observations (with no additional
selections made on other properties), Jin et al. (2016) find that the typical
rate of misalignment is a few percent on average, although the fraction
depends significantly on properties like stellar mass, star formation rate,
and specific star formation rate. At the typical sSFR of the BreakBRD sample
($\sim 10^{-10}\,{\rm yr^{-1}}$), the rate of misaligned disks is still only a
few percent. Our rate of 38% (23%-56%) therefore appears significant.

Misaligned gas can only arise if the gas arrived from outside the galaxy and
has an angular momentum vector misaligned with the stars. Such gas may have
arrived through cosmological accretion or the accretion of a small gas-rich
companion (Serra et al., 2012; Lagos et al., 2015). Alternatively, a
misaligned disk can result from a strong merger where the disk completely
reforms on top of the stellar remnant (van de Voort et al., 2015). Any
infalling gas should not have any preferred orientation angle, which means the
misaligned disks we observe do not capture all the recently formed disks
because some may have ended in a co-rotating gas/star configuration, in which
case our fraction of misaligned disks represent a lower limit on the total
number of recently (re)formed disks.

If the kinematic behavior of BreakBRDs is due to past merger activity, we must
reconcile that with the lack of merger evidence from imaging data. Tonnesen et
al. (2023) note that the combination of low CGM mass and extended misalignment
in simulated BreakBRDs could lead the induced central star-formation to be
very long-lived due to a slow CGM infall rate. Such behavior could explain the
lack of clear optical merger signatures if they dissipate more rapidly.

We conclude that the kinematic data supports recent external gas infall in
many BreakBRDs, but it is unclear whether the gas disk misalignment,
asymmetries, and BTFR offsets can be directly tied to merger events.

####  4.2.3 Metallicity evidence

BreakBRDs also show a clear broadening of their metallicity distribution, one
that is notably skewed towards slightly higher gas-phase metallicities
compared to the control sample. If BreakBRDs are in a state of quenching (see
Section 4.1), the slight increase in metallicity is expected as the population
moves off the star-forming sequence (although there may be a mass dependence
for this behavior; Kumari et al. 2021). Therefore, systematic shifts in
metallicity do not necessarily point to merging activity, but given the other
potential evidence (mostly dynamical), we can consider how mergers might
affect and be consistent with our observed metallicity trends.

Several studies have explored the influence of merger activity on central gas-
phase metallicity, finding that galaxies undergoing interactions tend to have
periods with lower central metallicity caused by dilution from inflowing gas
(Michel-Dansac et al., 2008; Rupke et al., 2010; Perez et al., 2011). However,
the timescale and stage of a merger is a major factor. For example,
simulations have shown that the dilution phase from major mergers and flyby
interactions is $<500\,{\rm Myr}$ in many cases, and is followed by a net
enhancement in central metallicity at the moment of, or soon after, final
coalescence (Montuori et al., 2010; Bustamante et al., 2018). These results
suggest the metallicity enhancement we observe is still consistent with a
recent merger scenario, as long as BreakBRDs are post-coalescence. Torrey et
al. (2012) also predict that metallicity can increase after mergers, and this
metallicity offset correlates with the gas content of the merger. However, net
enhancements in metallicity are mainly expected for high redshift galaxies. At
$z\sim 0$, nuclear metallicities are expected to generally drop after a
merger, although Torrey et al. (2012) predict the net dilution to be less as
gas fraction increases. We have examined whether BreakBRDs show any
correlation between H I fraction and offset from the mass-metallicity relation
of Marino et al. (2013), but find none (Figure 8). This null result may not be
surprising as the range of H I fractions in our data is much smaller than the
simulations of Torrey et al. (2012) that show a correlation. Nonetheless, this
analysis does not indicate a link between higher metallicities and mergers.

Figure 8: Residuals from the mass-metallicity relation of Marino et al. (2013)
versus $\log{M_{\rm HI}/M_{*}}$ for the BreakBRD sample. We see no positive
correlation between H I content and metallicity enhancement, as seen in
mergers in the simulations of Torrey et al. (2012).

##  5 Conclusions

Using newly obtained deep 21cm observations from the GBT, in combination with
optical spectroscopy, we have presented an analysis of the gas properties of
BreakBRD galaxies, a population characterized by their star forming bulges and
weakly or non-star-forming disks. We compare the properties of BreakBRDs with
control samples of galaxies on the Star Forming Sequence (SFS) and Green
Valley (GV). Our key results are:

  * •

BreakBRDs have a wide range of gas fractions at fixed stellar mass, but their
distribution has a lower mean value than typical SFS galaxies, and a higher
mean value than GV galaxies.

  * •

BreakBRDs have significantly lower average H I depletion times compared to SFS
galaxies, in contrast with GV galaxies that tend to have much longer depletion
times than SFS galaxies.

  * •

The distribution of nuclear gas-phase metallicity is notably broader than that
for the main star-forming population, and is skewed towards higher values. It
is similar to GV galaxies, among the subset where metallicity can be
estimated.

  * •

BreakBRDs falls systematically low on the Baryonic Tuller-Fisher Relation
(BTFR) relative to SFS galaxies.

  * •

BreakBRDs have high H I asymmetries and 2/3 of those with MaNGA IFU
spectroscopy show misaligned or disturbed gas velocity fields. However, it is
unclear from our data set whether the optical and H I kinematic properties are
directly correlated.

A unified picture of BreakBRD evolution from these data remains difficult to
determine, and it is still possible that BreakBRDs represent a variety of
ongoing evolutionary processes. However, their gas deficiency coupled with
resolved star formation properties is generally consistent with a population
transitioning into the quenched regime, but perhaps at an early stage.
Metallicities skewed towards higher values at fixed mass compared to galaxies
on the star-forming sequence may also support this scenario, with the caveat
that the metallicity distribution of BreakBRDs is quite broad, with some being
metal deficient. Frequent high H I asymmetry and star/gas velocity
misalignment could indicate that BreakBRDs have been subject of gas infall
and/or tidal interactions, although imaging data generally do not clearly
indicate recent merger activity. It is also possible that a subset of
BreakBRDs represent previously quenched galaxies with recent gas infall that
rejuvenated their central star formation.

Resolving the large-scale gas distribution may provide a clearer understanding
of BreakBRD evolution. Spatially resolved H I is an ideal tracer of recent
interactions, and will more clearly indicate whether there are inflows or
resettling disks, and they will enable us to assess whether H I asymmetries
and disturbed/counter-rotating gas are related to each other. Such
observations will also allow us to determine whether the H I is significantly
truncated beyond the bulges of BreakBRDs, or present but with densities or
dynamics that prevent it from contributing to star formation. Furthermore, it
will enable us to measure disk angular momentum more accurately and determine
whether metallicity enhancements and gas-star misalignment are associated with
high and low angular momentum, respectively, as has been reported by Tonnesen
et al. (2023). New Karl G. Jansky Very Large Array observations are underway
for a pilot sample of BreakBRDs and will allow us to address these questions.
Additionally, while an initial assessment by Tuttle & Tonnesen (2020)
demonstrated that BreakBRDs are found in a wide range of environments, a more
detailed analysis of environment (including multiple metrics) and its
relationship to BreakBRD properties would be beneficial. Such a study is
planned as future work.

We thank the anonymous referee for their careful review which helped improve
this paper. This work was made possible through NSF grant 1813462 and STScI
DDRF Grant D0001.82511. The authors thank Rebecca Nevin for sharing her
imaging-based merger probabilities for our sample. The authors acknowledge the
work of the entire ALFALFA collaboration who have contributed to the many
aspects of the survey over the years. The Green Bank Observatory is a facility
of the National Science Foundation operated under cooperative agreement by
Associated Universities, Inc. Funding for the Sloan Digital Sky Survey IV has
been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy
Office of Science, and the Participating Institutions. SDSS-IV acknowledges
support and resources from the Center for High Performance Computing at the
University of Utah. The SDSS website is www.sdss4.org. SDSS-IV is managed by
the Astrophysical Research Consortium for the Participating Institutions of
the SDSS Collaboration including the Brazilian Participation Group, the
Carnegie Institution for Science, Carnegie Mellon University, Center for
Astrophysics — Harvard & Smithsonian, the Chilean Participation Group, the
French Participation Group, Instituto de Astrofísica de Canarias, The Johns
Hopkins University, Kavli Institute for the Physics and Mathematics of the
Universe (IPMU) / University of Tokyo, the Korean Participation Group,
Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik
Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-
Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für
Extraterrestrische Physik (MPE), National Astronomical Observatories of China,
New Mexico State University, New York University, University of Notre Dame,
Observatário Nacional / MCTI, The Ohio State University, Pennsylvania State
University, Shanghai Astronomical Observatory, United Kingdom Participation
Group, Universidad Nacional Autónoma de México, University of Arizona,
University of Colorado Boulder, University of Oxford, University of
Portsmouth, University of Utah, University of Virginia, University of
Washington, University of Wisconsin, Vanderbilt University, and Yale
University. (Davidson-Pilon, 2019)

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##  Appendix A Flux Calibration

On a cadence on every four days (or the spacing between observing sessions, if
they were separated by more than four days), we carried out spectroscopic
observations of bright radio sources in order to check the telescope pointing
solution and calibrate the flux scale. These observations were done using
position switching with scan times of 30 seconds. The data from both
polarizations were averaged and the mean flux measured over the bandpass while
avoiding the central region of the spectrum dominated by Milky Way emission.
We applied that default GBTIDL flux calibration and the resulting flux
densities were compared to the true flux densities of measured calibrators
taken from Ott et al. (1994), or Perley & Butler (2017) if available. The
results are shown in Figure 9. A subset of targets that we observed (Virgo A,
Hydra A, 3C227, 3C348, 3C353) are not shown in Figure 9 due to them being
potentially poor flux calibrators (see Ott et al. 1994 and Perley & Butler
2017 for reasoning).

We find the ratio of true flux density to measured flux density (using the
default GBTIDL calibration coefficients) is $1.190+/-0.003$. Our results are
highly consistent with a recent study by Goddy et al. (2020) who conducted a
similar analysis using data from 2016 to 2019 and found an offset of 20% in
the true-to-measured flux density of bright calibrators. Based on these
results, we multiply all our measured fluxes and flux densities by 1.2 after
processing our data using the standard GBTIDL calibration.

Figure 9: True source flux density at 1.4 GHz divided by measured source flux
density using the default GBTIDL flux calibration as a function of time for
observations of standard radio sources during programs GBT20B-261 and
GBT22A-436. The horizontal dashed line shows the mean of the data.