2021/10/122022/03/02

galaxies: general — galaxies: formation — galaxies: evolution – galaxies:
spiral

#  Passive spiral galaxies deeply captured by Subaru Hyper Suprime-Cam

Rhythm Shimakawa11affiliation: National Astronomical Observatory of Japan
(NAOJ), National Institutes of Natural Sciences, Osawa, Mitaka, Tokyo
181-8588, Japan ∗ NAOJ Fellow<EMAIL_ADDRESS>(RS) Masayuki
Tanaka11affiliationmark:  Connor Bottrell22affiliation: Kavli Institute for
the Physics and Mathematics of the Universe (WPI), UTIAS, University of Tokyo,
Kashiwa, Chiba 277-8583, Japan Po-Feng Wu33affiliation: Academia Sinica
Institute of Astronomy and Astrophysics, Taipei 10617, Taiwan Yu-Yen
Chang33affiliationmark:  44affiliation: Department of Physics, National Chung
Hsing University, 40227, Taichung, Taiwan Yoshiki Toba33affiliationmark:
55affiliation: Department of Astronomy, Kyoto University, Kitashirakawa-
Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan 66affiliation: Research Center for
Space and Cosmic Evolution, Ehime University, 2-5 Bunkyo-cho, Matsuyama, Ehime
790-8577, Japan Sadman Ali11affiliationmark:

###### Abstract

This paper presents a thousand passive spiral galaxy samples at $z=$ 0.01–0.3
based on a combined analysis of the Third Public Data Release of the Hyper
Suprime-Cam Subaru Strategic Program (HSC-SSP PDR3) and the GALEX–SDSS–WISE
Legacy Catalog (GSWLC-2). Among 54871 $gri$ galaxy cutouts taken from the HSC-
SSP PDR3 over 1072 deg2, we conducted a search with deep-learning
morphological classification for candidates of passive spirals below the star-
forming main sequence derived by UV to mid-IR SED fitting in the GSWLC-2. We
then classified the candidates into 1100 passive spirals and 1141 secondary
samples based on visual inspections. Most of the latter cases are considered
to be passive ringed S0 or pseudo-ringed galaxies. The remainder of these
secondary samples has ambiguous morphologies, including two peculiar objects
with diamond-shaped stellar wings. The selected passive spirals have a similar
distribution to the general quiescent galaxies on the EWHδ–Dn4000 diagram and
concentration indices. Moreover, we detected an enhanced passive fraction of
spiral galaxies in X-ray clusters. Passive spirals in galaxy clusters are
preferentially located in the midterm or late infall phase on the phase-space
diagram, supporting the ram pressure scenario, which has been widely advocated
in previous studies.

The source catalog and $gri$-composite images are available on the HSC-SSP
PDR3 website (https://hsc.mtk.nao.ac.jp/ssp/data-release/). Future updates,
including integration with a citizen science project dedicated to the HSC
data, will achieve more effective and comprehensive classifications.

##  1 Introduction

To understand the fate of spiral galaxies, including our own Galaxy, passive
spiral galaxies (termed ‘anemic’ spirals; [van den Bergh (1976)]) yield
important insights into their transition processes from the star-forming to
the quiescent phase. They would also uniquely give a hint at the formation and
destruction mechanisms of spiral structures (see theoretical studies, e.g.
[Lin & Shu (1964), Goldreich & Lynden-Bell (1965), Sellwood & Carlberg (1984),
Bertin et al. (1989), D’Onghia et al. (2013), Dobbs & Baba (2014)]).

From a historical perspective, since the discovery of the significant presence
of passive spirals in rich clusters (van den Bergh, 1976), the physical
origins of passive spirals have been widely linked to environmental phenomena
(Goto et al., 2003; Yamauchi & Goto, 2004; Koopmann & Kenney, 2004; Boselli et
al., 2006; Moran et al., 2007; Blanton & Moustakas, 2009; Masters et al.,
2010). Additionally, cold gas observations have reported that passive spiral
galaxies are Hi-deficient but not in molecules compared to normal star-forming
spirals, suggesting a lack of persist gas supply owing to truncation of the
outer Hi disk, which may originate from environmental effects in some cases
(e.g. Bothun & Sullivan (1980); Giovanelli & Haynes (1983); Cayatte et al.
(1994); Bravo-Alfaro et al. (2001); Elmegreen et al. (2002)). Ram pressure
stripping in clusters or falling subclusters (referred to as pre-processing)
can lead to strangulation and slow star formation quenching (Gunn & Gott,
1972; Fujita, 2004; Kawata & Mulchaey, 2008; Hamabata et al., 2019; Rhee et
al., 2020).

However, a consensus has yet to be reached on these matters, as their
abundance is overwhelmingly small (e.g., $\sim 6$% in Masters et al. (2010),
and see also references therein). The Sloan Digital Sky Survey (SDSS; York et
al. (2000)) has played a key role in sample collection over the last two
decades. In addition, UV and IR photometric data, such as the Galaxy Evolution
Explorer (GALEX; Martin et al. (2005)) and the Wide-field Infrared Survey
Explorer (WISE; Wright et al. (2010)) are crucial to make a reliable selection
(Fraser-McKelvie et al., 2016). Conversely, much deeper and sharper images
with larger aperture telescopes are highly desired to conduct an in-depth
passive spiral search, which can cover faint spiral structures missing in
previous surveys and also resolve pseudo-rings with smaller pitch angles of
spiral arms. Such objects with less-prominent spiral features provide a less
biased insight into the late evolutionary stage of spiral galaxies.

The Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP; Aihara et al. (2018);
Miyazaki et al. (2018); Furusawa et al. (2018); Kawanomoto et al. (2018);
Komiyama et al. (2018)) on the 8.2 m Subaru Telescope satisfies these
stringent requirements. The HSC-SSP surveys 1400 deg2 areas in $grizy$
broadbands approximately 3 mag deeper and with $\sim 2$ times better seeing
size compared to the SDSS. With such high-quality data, we can expect to
capture fainter spiral arms not detected in previous surveys and those with
smaller pitch angles mistaken for ringed galaxies. Many studies have
demonstrated the utility of the HSC-SSP data, such as galaxy morphological
classifications (Tadaki et al., 2020; Martin et al., 2020; Shimakawa et al.,
2021), weak lensing measurements (Oguri et al., 2018, 2021), and anomaly
detection (Storey-Fisher et al., 2021; Tanaka et al., 2021). Therefore, the
HSC-SSP data will enable us to _significantly_ expand upon existing passive
spiral samples, offering a unique opportunity to understand the transition
phase from spiral galaxies to early-type galaxies statistically.

With this motivation, this study aims to establish a large sample of passive
spiral galaxies by using the HSC-SSP PDR3 (Aihara et al., 2021). The remainder
of this paper is organized as follows. We first overview our dataset and deep
learning-based methodology to select spiral galaxies at $z=0.01{\rm-}0.3$
(section 2). Subsequently, we choose passive spiral galaxies based on the
GALEX–SDSS–WISE Legacy Catalog (GSWLC-2; Salim et al. (2018)) and describe our
passive spiral catalog (Section 3). In section 4, we compare the spectral
properties of the passive spirals with those of the entire sample, and
investigate their dynamic associations with massive galaxy clusters for the
cluster members. Finally, we summarize our results and prospects (section 5).
Following the GSWLC-2 catalog used in this study (Salim et al., 2018), the AB
magnitude system (Oke & Gunn, 1983) and a Chabrier (2003) stellar initial mass
function (IMF) are adopted. Moreover, we assume a flat WMAP7 cosmology
($H_{0}=70$ km s-1Mpc-1, $\Omega_{m}=0.27$; Komatsu et al. (2011)).

##  2 Data and methodology

In the subsections that follow, we introduce the salient features of the HSC-
SSP and the selection of 54871 main targets at $z=$ 0.01–0.3 (section 2.1).
Subsequently, we describe the color-image construction (section 2.2), and the
calibration of our deep learning classifier of spiral morphologies (section
2.3).

###  2.1 Sample selection

The HSC-SSP PDR3 provides a science-ready catalog and coadd imaging data,
which were taken through 278 nights and reduced by the dedicated pipeline
(hscPipe version 8; Bosch et al. (2018)). The survey field is split on the
database into areas of approximately $1.7\times 1.7$ deg2, termed tracts, and
further divided into approximately $12\times 12$ arcmin2 regions called
patches (Aihara et al., 2021). To exclude poor seeing data, we employ only
patch regions with seeing FWHM (full-width-half-maximum) smaller than 1.0
arcsec in all $gri$ bands, which amounts to the total survey area of 1072
deg2.

As a starting point, we use 63504 bright galaxies at $z=$ 0.01–0.3 in the HSC-
SSP PDR3 Wide layer111https://hsc.mtk.nao.ac.jp/ssp/data-release/ (Aihara et
al., 2021), all of which have spec-$z$ counterparts in the SDSS DR15 (Blanton
et al., 2017; Aguado et al., 2019; Bolton et al., 2012) and the
GALEX–SDSS–WISE Legacy Catalog (GSWLC-2; Salim et al. (2016, 2018)). The
targets are mostly based on the SDSS Main Galaxy Sample
($r_{\mathrm{petro}}<17.77$; Strauss et al. (2002)) as they select bright SDSS
sources above the Petrosian (1976) magnitude limit, $r_{\mathrm{petro}}<18$.
The GSWLC-2 has derived their physical properties, including stellar masses
and star formation rates (SFRs) with UV to mid-IR spectral energy distribution
(SED) fitting using CIGALE (Noll et al., 2009) with stellar population
synthesis models in Bruzual & Charlot (2003), based on GALEX GR6/7 (Martin et
al., 2005), SDSS DR7 (Abazajian et al., 2009), and 22 $\mu$m data from ALLWISE
source catalog (Lang et al., 2016). Based on these datasets, the GSWLC-2
provided three separate catalogs depending on the depth of the UV survey (all-
sky, A; medium, M; and deep, D). The GSWLC-A catalog is employed in this study
to homogeneously select passive spirals across the survey field, unless
otherwise noted.

All 63504 sources are detected in the $grizy$ broadband data of the HSC-SSP
PDR3 and are not influenced by nearby bright stars ($G<18$ mag; Aihara et al.
(2021)), cosmic rays, or bad pixels, by applying the following criteria in the
SQL query:
forced.isprimary=True,
inputcount_flag_noinputs=False,
sdssshape_flag_badcentroid=False,
pixelflags_edge=False,
pixelflags_crcenter=False,
pixelflags_bad=False,
psfflux_flag=False,
mask_brightstar_halo=False,
mask_brightstar_ghost=False,
mask_brightstar_blooming=False.
For details on these catalog flags, refer to Coupon et al. (2018); Aihara et
al. (2021); Bosch et al. (2018). In addition, point sources are removed based
on the threshold of i_psfflux_mag$-$i_cmodel_mag$>$0.2 (see also Strauss et
al. (2002); Baldry et al. (2010)). Consequently, we are left with 54871
galaxies at $z=$ 0.01–0.3 over 1072 deg2.

###  2.2 Image construction

For the selected targets, we constructed RGB color cutouts in the png format
with a size of $(4\times\mathrm{R90})^{2}$ arcsec2, where R90 is a radius
within which 90% of the Petrosian aperture flux in the $r$-band is included.
We adopted the R90 values of our targets from the SDSS DR15 (petroR90; Aguado
et al. (2019)) by cross-matching between our sample and the SDSS sources
within a radius of 1 arcsec. Flux scaling generally follows our previous work
(Shimakawa et al., 2021), which adopts an inverse hyperbolic sine function,
arcsinh (Lupton et al., 1999, 2004). The arcsinh normalization has been widely
employed in previous studies related to galaxy morphology classification, such
as the Galaxy Zoo project (Lintott et al., 2008, 2011; Willett et al., 2013).
Because the arcsinh stretch has also been used in the citizen astronomy
program based on the HSC imaging data (GALAXY
CRUISE222https://galaxycruise.mtk.nao.ac.jp), we can integrate this analysis
with a large training dataset provided by this citizen science program in the
near future.

After homogenizing the seeing FWHM of the images to 1.0 arcsec by the Gaussian
smoothing, we perform the arcsinh normalization with a code provided by the
HSC-SSP pipeline team333https://hsc-gitlab.mtk.nao.ac.jp/ssp-software/data-
access-tools/tree/master/pdr3/colorPostage. The normalized flux $f(x)$ can be
described as follows:

$f(x)=\frac{x}{I}\cdot\frac{\mathrm{arcsinh}(\alpha
I)}{\mathrm{arcsinh}(\alpha)}+\beta,$ (1)

where $I={\displaystyle\Sigma_{x\in\\{gri\\}}}/3$ and $x$ is the pixel value
($f_{\nu}$) on coadd image in each filter. Standardization by the mean flux
over the $gri$-band photometry ($I$) allows us to minimize color saturation.
We adopted default values of the scaling parameter of $\alpha=\exp(10)$ and
the bias parameter of $\beta=0.05$. In addition, we set the lower and upper
limits to 0 and 1, respectively. Examples of the resultant color images are
shown in figures 4, 5, and 7.

###  2.3 Deep learning model

Figure 1:  An outline of the model architecture.

To achieve an efficient classification of spiral galaxies, we apply transfer
learning (see, e.g., Pratt et al. (1993); Oquab et al. (2014)) based on a deep
learning model to the color images constructed above. Because our previous
study (section 3.1 and figure 3 in Shimakawa et al. (2021)) already
demonstrated the application of transfer learning to the galaxy images from
the HSC-SSP, we briefly summarized the approach herein. As is the case with
the spiral classification in Shimakawa et al. (2021), we attached the Xception
architecture (Chollet, 2016) of a class of convolutional neural networks (CNN;
Lecun et al. (1998, 2015)) to a two-class sigmoid classifier, which is pre-
trained for 1000 class separation with the ImageNet dataset (Russakovsky et
al., 2014). The classifier yields the probability of spiral galaxy
($\hat{y}=[0:1]$) by the sigmoid function, through the layers that consists of
a dense layer (linear transformation, $d=1024$), batch normalization (Ioffe &
Szegedy, 2015), and ReLU activation ($\equiv max(0,x)$; Glorot et al. (2011)),
as illustrated in figure 1. To implement of this model, we adopted a deep
learning model using TensorFlow (version 2.6.0; Abadi et al. (2016)) and Keras
(version 2.6.0; Chollet et al. (2015)), under a single GPU, the NVIDIA TITAN
RTX.

We prepared 6400 training data, 1600 validation data, and 2000 test data for
the model training. Through the deep learning analysis, we adopted the gray-
scale images converted from the RGB color images (figure 2) to minimize a
color–morphology dependence (e.g., de Vaucouleurs (1961); Schawinski et al.
(2014)), although we treated them as the data with three channels to tailor
the input dimension of the model architecture. For each data, half were random
non-spiral galaxies, and the other half were spiral galaxies selected by the
author’s visual inspection (RS). Because they were originally based on the
HSC-SSP PDR3 sample at $z=$ 0.01–0.3, the datasets partially overlapped with
our main targets (7239 out of 54871). Their image sizes were scaled and
homogenized to $200\times 200$ pixels (the typical image size in the sample)
from the cutout size of $(4\times\mathrm{R90})^{2}$ arcsec2 with a pixel width
of 0.168 arcsec (figure 1). In addition, the image color channels were
standardized similarly as in the pre-training with the ImageNet (Chollet,
2016) to leverage the pre-trained weight parameters.

Figure 2:  Randomly selected image samples of the training data.

Subsequently, we implemented the model training. An optimizer tunes the model
weight parameters to minimize the binary cross-entropy loss, which is defined
as:

$Loss\equiv-y\log\hat{y}-(1-y)\log(1-\hat{y}),$ (2)

where $y$ and $\hat{y}$ are the target value $\\{0,1\\}$ and the model
prediction $[0:1]$, respectively. We adopted an adaptive learning rate
optimization algorithm (Adam; Kingma & Ba (2014)) as the optimizer, with a
batch size of 32. The model training begins with a default learning rate of
$lr=1\times 10^{-3}$ until the 20th epoch, which was changed to $lr=2\times
10^{-4}$ in and after the 20th epoch. Further, we served a general data
augmentation using ImageDataGenerator in the Keras library, which performs
random image rotation ($<90$ deg) and horizontal and vertical flips.

The model learning was conducted in 80 epochs, and it took 5827 sec in our GPU
environment. The resultant training histories of the model accuracy and binary
cross-entropy loss are shown in figure 3. The model accuracy is written as:

$Accuracy\equiv\frac{TP+TN}{TP+TN+FP+FN},$ (3)

where $TP$ (or $TN$) and $FP$ (or $FN$) indicate the true positive (or
negative) and false positive (or negative) rates, respectively. Here, the
positive and negative classes predicted by the classifier are separated by
$\hat{y}=0.5$. Among the models in 80 epochs, we chose the best model with the
least cross-entropy loss in the validation data (figure 3). The selected model
provides a training accuracy of 99.5%, validation accuracy of 99.4%, and
validation loss of 0.021. We also obtained 98.9% accuracy in independent test
data consisting of 1000 spiral and 1000 non-spiral galaxies, which were
similarly selected as in the training and validation data. From the test data,
the precision ($TP/(TP+FP)$) and recall ($TP/(TP+FN)$) rates were estimated to
be 99.3% and 98.4%, respectively. Moreover, based on the end result (section
3), we noticed the test data include 65 passive spirals. Although the sample
size is small, these passive spirals show the precision rate of 96.9% and the
recall rate of 100% for the classification of spiral galaxies.

Figure 3:  Model training histories in the accuracy (upper panel) and the
binary cross-entropy loss (lower panel). The purple and yellow lines show the
histories of the training and validation data, respectively. This study adopts
the model in the 34th epoch which has the minimum validation loss as
represented by the red-filled circle.

##  3 Identifications of passive spiral galaxies

###  3.1 Selection of passive spiral galaxies

Figure 4:  Upper left: Logarithmic SFR versus stellar mass for spiral galaxies
selected by the deep learning classification. The dashed line shows the best
fit star-forming main sequence. We selected galaxies lying 1 dex or more below
this sequence (dot-dashed line) as candidates of passive spirals. Right:
Multi-colored $gri$ images of randomly selected spirals on the SFR–M⋆ plane.
Lower left: Distributions of logarithmic specific SFRs of the HSC-SSP
PDR3–GSWLC samples (gray histogram) and spiral galaxies (open dot-dashed
histogram).

First of all, we apply the well-tuned deep learning model to the main targets,
the 54871 bright galaxies at $z=$ 0.01–0.3, to choose spiral galaxies among
them. Because our final sample consists of sufficiently bright
($r_{\mathrm{petro}}<18$) and massive (M${}_{\star}>10^{10}$ M⊙) galaxies at
$z<0.3$, we assume that the redshift bias of the spiral classification is
negligible (see also Tadaki et al. (2020)). Automatic and efficient deep
learning classification enabled us to categorize such a large amount of
dataset in only 330 sec on the GPU environment. Figure 4 represents the
frequency distribution and image samples of the selected spirals on the SFR–M⋆
plane derived by the GSWLC-2 (Salim et al., 2018). This study defines as the
spiral galaxies those with model prediction values greater than $\hat{y}=0.5$
(section 2.3), which amounts to a total of 22720 sources (41% of the parent
sample). Because this study solely aims to establish a passive spiral sample,
we do not delve into more detailed shape measurements of spiral structures and
debiased values of such as the spiral fraction, which are left to future
research.

For the sample classified as spiral galaxies, we choose candidates of passive
spirals, located by an order of magnitude lower than the median-fit line for
the SFR–M⋆ distribution of star-forming galaxies with specific SFRs, SSFR
$>10^{-11}$ yr-1 (figure 4):

$\log(\mathrm{SFR/M}_{\odot}\mathrm{yr}^{-1})<0.584\times\log(\mathrm{M}_{\star}/\mathrm{M}_{\odot})-6.867.$
(4)

As a result, 2241 (9.9 percent) passive spiral candidates satisfied the SSFR
cut, which is significantly lower than a passive fraction of 50 percent in the
original sample. Within the selected spiral galaxies, we did not find a
redshift dependence of the passive fraction.

###  3.2 Visual confirmation

Next, visual inspections for further validation were performed by all authors
independently. This visual check was quite important for the passive spiral
selection because contamination by ringed S0 galaxies to the spiral sample
becomes significant in the passive population (see, e.g., Buta & Combes
(1996); Buta (2017)). In the validation process, suspicious candidates were
tagged by the authors, and then, we calculated a vote fraction for each
candidate (f_vote = {0,1}; see table 2). In conducting the visual
classifications, we first established a consensus that obvious pseudo-ringed
galaxies (Buta & Crocker, 1991; Buta & Combes, 1996; Kormendy & Kennicutt,
2004) were to be regarded as spirals, whereas we tagged visual check flags for
non-spirals, merging galaxies, or ambiguous objects that are difficult to
determine rings or pseudo-rings.

Figure 5 highlights suspicious samples that were regarded as non-spiral
galaxies in the visual validation. They mostly seem to be ringed or pseudo-
ringed galaxies, as shown in figure 5a-d. The remaining suspicious candidates
had unclear spiral features or peculiar substructures (figure 5e-g), for
example, an offset ring such as the Cartwheel Galaxy (Fosbury & Hawarden,
1977) and a polar ring (Sandage, 1961). Additionally, we found two unique
objects with diamond-shaped wings (id_hsc3 = 41033924272474055 &
41034615762212336; table 2 and figure 5h,i), which were also removed from the
passive spiral sample. They have a similar structure to local galaxies, NGC
7020 (Buta, 1990), NGC 4429 (Buta et al., 2007), and IC 4767 (Whitmore & Bell,
1988), which are reported as peculiar S0 galaxies with hexagonal or X shapes.
It would be intriguing to investigate their kinematics and accretion histories
using a modern integral field spectrograph, which will address the formation
mechanisms of such a peculiar substructure.

Figure 5:  Sample $gri$ images of the candidates judged to have non spiral
features by PI’s visual inspection. Most of these suspicious sources are
difficult to distinguish as either pseudo-ringed spirals or ringed S0 as seen
in (a–d). For the rest, some sources seem to have unique morphologies, e.g.,
(e) galaxies with an offset ring such as the Cartwheel Galaxy (Zwicky, 1941),
(f,g) polar-ring galaxies (Sandage, 1961), and (h,i) galaxies with diamond-
shaped wings surrounded by an outer ring or disk. See the text for further
explanations on visual checks and human bias.

Table 1 summarizes the results of our visual inspections. To quantify the
human bias in our visual validations, we define a concordance rate (CR) by:

$\mathrm{CR}\equiv N_{s}(A|B)=N_{s}(A\cap B)/N_{s}(B),$ (5)

where $N_{s}$($x$) is the number of suspicious objects (i.e., non passive
spirals) among the candidates ($N=2241$) judged by an author $x$; therefore,
CR indicates that the number ratio of overlapped non passive spirals in
between authors A and B ($=N_{s}$(A$\cap$B)) to that in the author B
($=N_{s}$(B)). The derived CR values between individual authors are also
summarized in table 1. The table indicates the significant variations in our
visual check results ($N_{s}=$ 570–1466) and concordance rates, i.e., human
bias, even among experts. For example, the author 5 selected the least
($N_{s}=570$) as non spiral features from the candidates, most of which ($\sim
90$ percent) were selected by the author 1, 2 and 6 as well. On the other
hand, 24–31 percents of non passive spirals deemed by the author 5 were passed
in visual validations by the author 3, 4 and 7. Meanwhile, the authors 1, 2
and 6 who selected more samples ($N>1300$) as non-spiral galaxies than the
others, with $\sim 80$ percent in agreement. Such significant variations in
our visual validations reflect difficulties in the visual classification of
passive spiral galaxies. These inconsistencies would be mostly due to the
difficulty of distinguishing pseudo-ringed spiral galaxies from ringed S0
galaxies or interacting objects with tidal tales, even with high-quality data
from the Subaru HSC. The viewing angles of galaxies in the sky would be
another factor causing the variations.

Based on the responses of our visual inspections, we calculated the vote
fraction for each object (flag_vc = [0:1]; table 2), which provides
flexibility for catalog users to make their decisions on the sample selection
from the passive spiral catalog (table 2). Higher flag_vc values indicate less
reliable passive spiral samples in our visual validations. Image samples for
each vote fractions are represented in figure 6, showing that the samples with
higher flag_vc values tend to have less clear spiral features or ring-like
morphologies. If one wants to establish the most secure passive spiral sample
from our catalog, we recommend using the sources with flag_vc = 0, which are
recognized as having spiral features by all authors of this paper. However, it
should be noted that such a conservative selection may overlook many pseudo-
ringed spiral galaxies.

Table 1:  Concordance rates ($\equiv N_{s}$(A$|$B)) among visual inspections by 7 authors (AU1–7). The 2nd row indicates the number of the candidates that are judged to have no spiral features by each co-author ($N_{s}$).  A B | AU1 | AU2 | AU3 | AU4 | AU5 | AU6 | AU7
---|---|---|---|---|---|---|---
Ns | 1357 | 1344 | 860 | 1066 | 570 | 1466 | 1071
AU1 | … | 0.81 | 0.92 | 0.77 | 0.92 | 0.77 | 0.83
AU2 | 0.81 | … | 0.87 | 0.77 | 0.90 | 0.75 | 0.77
AU3 | 0.58 | 0.56 | … | 0.53 | 0.76 | 0.50 | 0.55
AU4 | 0.61 | 0.61 | 0.66 | … | 0.74 | 0.56 | 0.65
AU5 | 0.39 | 0.38 | 0.50 | 0.39 | … | 0.35 | 0.37
AU6 | 0.83 | 0.82 | 0.85 | 0.77 | 0.91 | … | 0.84
AU7 | 0.65 | 0.61 | 0.69 | 0.65 | 0.69 | 0.61 | …
Figure 6:  The bottom histogram shows the vote fraction (flag_vc) for
suspicious sources in the passive spiral candidates selected by the deep
learning model. Color postage stamps are random examples given vote fractions:
from the top, (a) flag_vc = 0, that is, they are recognized as spirals by
every co-authors, (b) flag_vc = 0.14 or 0.29, (c) flag_vc = 0.43 or 0.57, (d)
flag_vc = 0.71, 0.86, or 1.

###  3.3 Passive spiral galaxy catalog

Our passive spiral catalog (table 2) and individual postage stamps in png
format can be downloaded on the HSC-SSP PDR3 website
(https://hsc.mtk.nao.ac.jp/ssp/data-release/). Throughout of this paper, we
employ 1100 candidates with flag_vc $<0.5$ as the passive spiral galaxies,
that is, we adopt the samples accepted by 4 or more out of 7 authors of this
paper in the last section 3.2. The passive fraction of spiral galaxies after
the visual validations is 5.1% (from $1100/(22720-(2241-1100))$)444We here
ignore the contaminants in the active spiral samples, given the high success
rate of the overall spiral classification (section 2.3)., which is the same
level as the red fraction of spirals (4–8%) in the Galaxy Zoo (Masters et al.,
2010). Whereas, we note that this consistency includes both reducing and
increasing effects from more conservative passive selection using UV-to-IR
data (Salim et al., 2018) and higher quality HSC imaging data (Aihara et al.,
2021), respectively. Example $gri$-composite color images of star-forming
(active) and passive spiral galaxies are shown in figure 7, which clearly
differ in appearance: passive spirals are redder with smoother spiral arms
than active spirals. All the color images of the selected passive spirals are
summarized by supplemental material available online. Postage stamps of the
secondary samples with flag_vc values ($>0.5$), which are not discussed
hereafter, are also available as supplemental material.

Figure 7:  Example RGB color images of active (top) and passive (bottom)
spiral galaxies at $z=$ 0.01–0.3 based on $gri$ cutouts
($4~{}\mathrm{R90}\times 4~{}\mathrm{R90}$ arcsec2) from the HSC-SSP PDR3.
Full postage stamps of our passive spiral samples are available as
supplemental material.

Moreover, we checked if they remained quiescent even when we adopted the best-
fit physical properties based on deeper GALEX data if available in the version
of the GSWLC-M or GSWLC-D (see Salim et al. (2016, 2018)). It turns out that
786 (818) and 158 (154) objects out of 1100 main (1141 secondary) samples were
covered by GSWLC-M or GSWLC-D, respectively. As noted in the literature, the
depth of the UV data influences the SFR measurement, especially for less-
active galaxies. Once we replaced their physical values with deeper UV data,
many of the passive spiral samples were located in the green valley on the
star-forming main sequence (figure 8) and approximately half of them (339 and
82 out of 786 and 158 in GSWLC-M and -D, respectively) were shifted into above
the threshold of the passive population (Eq. 4). This indicates that only deep
optical data are inadequate to select truly passive spiral galaxies and
sufficiently deep UV (and IR) data are crucial (see also Fraser-McKelvie et
al. (2016)). We note that no significant difference existed in the behavior of
between SFRs from GSWLC-M and -D to those from GSWLC-A. Consequently, our
catalog provides an additional validation flag (flag_uv), which yields “M” or
“D” if the candidates still satisfy the selection threshold even if we adopt
the catalog versions of GSWLC-M or -D, respectively. However, we should note
that this paper does not adopt flag_uv to maintain the homogeneous selection
of passive spirals over the entire survey field.

Combined with the visual check flag (flag_vc), we offer a comprehensive and
flexible source catalog (table 2) to leave further decisions up to users.
These validation flags are aimed at allowing users of our catalog to select
more (or less) secure passive spiral sources for follow-up investigations,
depending on their objectives.

Figure 8:  The left panels shows a comparison of logarithmic SFR based on
GSWLC-A with those from GSWLC-M or -D (Salim et al., 2018), and the right
panel represents revised logarithmic SFR–M⋆ based on GSWLC-M or -D from figure
4. The color map shows the distribution of all spiral galaxies available in
GSWLC-M or -D. The orange and red circles indicate the passive spiral samples
that are available in GSWLC-M or -D (but selected with GSWLC-A), respectively.
While they are mostly equal to each other, there are significant outliers.
Table 2:  Column descriptions of the passive spiral catalog. The full catalog
is available on the HSC-SSP PDR3 site (https://hsc-release.mtk.nao.ac.jp).

Column | Description
---|---
id_hsc3 | object_id in HSC-SSP PDR31
id_sdss | object_id in SDSS (GSWLC)2
ra | right ascension (deg)
dec | declination (deg)
redshift | spectroscopic redshift from SDSS DR153
y_pred | probability value by CNN classification
flag_vc | vote fraction for suspicious objects4
flag_uv | validation flag with in-depth UV data5

1 Refer to Aihara et al. (2021)
2 Cross-match within 1 arcsec to Salim et al. (2018)
3 Cross-match to Aguado et al. (2019) on the HSC-SSP database
4 See section 3.2 for details.
5 ‘M’ or ‘D’ if sources remain passive even in the SFR–M⋆ plain based on
GSWLC-M or GSWLC-D (see section 3.1).

##  4 Discussion

Based on 54871 spec-$z$ sources at $z=0.01{\rm-}0.3$ from the HSC-SSP PDR3 and
the GSWLC-2, we established a passive spiral sample, in which 1100 objects are
handled here as the main sample. This section discusses the physical
properties and environmental effects using EWHδ–Dn4000 and phase-space
diagrams, respectively.

###  4.1 EW${}_{\mathrm{H\delta_{A}}}$–Dn4000 diagram

Investigation of the stellar populations of passive spirals enables us to
understand their evolutionary stages compared to those of the other galaxies.
We examined the 4000 Å break strength (Dn4000) and the Balmer absorption line
index (H$\delta$ equivalent width, EW${}_{\mathrm{H\delta_{A}}}$; Worthey &
Ottaviani (1997)) of passive spirals, and compared them with those of spiral
galaxies and passive galaxies. The so-called
EW${}_{\mathrm{H\delta_{A}}}$–Dn4000 diagram has been widely used to
understand stellar populations of quiescent and post-starburst galaxies (Blake
et al., 2004; Yesuf et al., 2014; Wu et al., 2021). While the Dn4000 is
roughly interpreted as the ages of stellar populations, the EWHδ responds to
the time scale of star formation history, which is the strongest ($>5$ Å) in
0.1–1 Gyr after starburst (Kauffmann et al., 2003; Wu et al., 2021).

We obtained Dn4000 and EW${}_{\mathrm{H\delta_{A}}}$ values of our sample from
the MPE-JHU catalog (Kauffmann et al., 2003) by searching for their
counterparts within a 1 arcsec radius. Among the 54871 samples, 50831 objects,
including 970 passive spirals, are cross-matched with the MPE-JHU catalog. The
density distribution on the Dn4000 and EW${}_{\mathrm{H\delta_{A}}}$ diagram
and normalized distributions in each axis for spiral galaxies, passive
galaxies, and passive spirals are shown in figure 9a.

Figure 9:  (a) The top panel shows the EW${}_{\mathrm{H\delta_{A}}}$–Dn4000
diagram for our samples with counterparts in the MPE-JHU catalog within a
radius of 1 arcsec (Kauffmann et al., 2003). The black region and the red
contours show distributions of all cross-matched spirals and passive spirals,
respectively. (b) The second row indicates stellar mass distributions of
randomly selected mass-controlled passive galaxies (pink-filled) and spirals
(dashed), $N=$ 10000 for each, from the parent sample, and passive spirals
(red hatched). Panels (c–f) represent normalized distributions of the Dn4000,
EW${}_{\mathrm{H\delta_{A}}}$, Sersic index, and Petrosian concentration
index, respectively. The concentration measurements are based on the NYU-VAGC
catalog (Blanton et al., 2003, 2005). The sample in each histogram corresponds
to those in the panel (b).

As a result, we found that passive spiral galaxies have a distribution similar
to that of general passive galaxies more than active spirals on the Dn4000 and
EW${}_{\mathrm{H\delta_{A}}}$ plane (figure 9c,d), where we randomly selected
10000 mass-controlled passive galaxies and active spirals from the parent
samples to adjust the stellar mass distributions to that of passive spirals
(figure 9b). We confirmed that such a trend becomes more evident when we
employed more secure passive spirals from deeper UV data (flag_uv = M or D;
table 2). Because we selected passive galaxies with the same threshold on the
SFR–M⊙ plane (figure 4), the similar distributions between these two samples
suggest that the passive spirals have identical characteristics to the typical
quiescent galaxies in stellar populations despite the significant difference
in their morphologies. Distributions of Sersic (Sersic, 1968) and Petrosian
concentration indices (Shimasaku et al., 2001) for passive spiral galaxies in
the SDSS $r$-band are more similar to those of typical quiescent galaxies than
active spirals. These concentration measurements were taken from the NYU-VAGC
catalog (Blanton et al., 2003, 2005) by spatial cross-matching within 1 arcsec
(52166 sources, including 1009 passive spirals, were successfully matched).

The results may suggest that star formation histories and galaxy morphology
transitions are not always coupled, and the quenching timescales of spiral
galaxies are very slow ($\sim$ a few Gyr), as proposed by Schawinski et al.
(2014). However, it should be noted that the SDSS spectra mostly trace bright
central bulges of galaxies within the fibers of 3 arcsec diameter, which is
smaller than the typical Petrosian half-light radius of 1.76 arcsec in our
sample. The concentration metrics show that passive spirals tend to have bulge
structures similar to those of typical quiescent (non-spiral) galaxies; thus,
the similar stellar populations between these spectra are not surprising. An
extensive deep survey with an integral field spectrograph is needed to reach a
consensus on the star formation histories of passive spiral arms.

###  4.2 Phase-space diagram

Previous studies have reported a higher frequency of passive spirals at higher
local densities (van den Bergh, 1976; Goto et al., 2003; Masters et al.,
2010), suggesting that environmental effects may be responsible for such a
high passive fraction of spiral galaxies. This study discusses the
environmental quenching of spiral galaxies from a more practical standpoint by
leveraging a large passive spiral sample over 1072 square degrees.
Specifically, we cross-matched the main targets at $z=$0.01–0.3 (section 2.1)
with X-ray-selected clusters ($\sim 10^{14}{\rm-}10^{15}$ M⊙) by SPIDERS (the
SPectroscopic IDentification of eROSITA Sources; Clerc et al. (2016)) to
examine the dynamical associations of passive spirals with massive clusters.
Because the SPIDERS catalog offers virial radii ($R_{200}$) and velocity
dispersion ($\sigma_{v}$), we can easily test the association of passive
spirals with clusters on the phase-space diagram (Bertschinger, 1985) by
stacking all individually matched cluster members to the cluster sources. The
phase-space diagram provides a useful clue on the dynamical states of galaxy
clusters and the trajectory of galaxies infalling towards the cluster center
(Sanchis et al., 2004; Mahajan et al., 2011; Jaffé et al., 2015; Oman &
Hudson, 2016; Yoon et al., 2017). In this study, the phase-space diagram
enables us to infer where spiral galaxies are inclined to be passive during
their infall into the clusters.

We obtained 1148 cluster members associated with 123 clusters within the
scaled projected radii ($r/R_{200}<2.5$) and velocity offsets ($|\Delta
v/\sigma_{v}|<2.6$). Among the matched cluster members, 250 and 35 sources
were classified as spiral and passive spiral galaxies, respectively. The
average passive fraction of spiral galaxies in the clusters (14%) was
approximately thrice as high as that in random fields (5%). Figure 10 shows
the positions of all cluster members on the projected phase-space diagram. The
figure also indicates the escape velocity and virialized region proposed by
Jaffé et al. (2015), defining the orbital states on the projected phase-space
diagram more realistically than in previous studies.

The very center of the galaxy clusters lacks spiral galaxies (figure 10),
which is well known as the morphology–density relation (e.g., Dressler (1980);
Cappellari et al. (2011)); we also obtained a non-uniform distribution of
passive spirals to the entire spiral galaxies in the cluster environments. We
detected a substantial excess of passive spiral galaxies reaching up to $\sim
50$% on this diagram in the area of $r/R_{200}=0.3{\rm-}1$ and $|\Delta
v/\sigma_{v}|\lesssim 1$, where galaxies are expected to experience core
crossing more than once (e.g., Jaffé et al. (2015); Yoon et al. (2017)). It is
quite reasonable to see such a biased distribution owing to the truncation of
the continuous Hi gas supply due to the ram pressure stripping in the earlier
infall phase (Hamabata et al., 2019). Previous studies have reported that
spiral galaxies influenced by the ram pressure stripping were found in the 1st
infall ($|\Delta v/\sigma_{v}|=1{\rm-}2.5$ and $r/R_{200}<1.3$) and backsplash
($|\Delta v/\sigma_{v}|\leq 0.5$ and $r/R_{200}=1.2{\rm-}2$) areas (see, e.g.,
(Yoon et al., 2017, figure 5)). Thus, our results provide strong independent
support for the conclusions presented in previous studies that ram pressure
stripping enhances the fraction of passive spirals in cluster environments
(Bothun & Sullivan, 1980; Giovanelli & Haynes, 1983; Cayatte et al., 1994;
Bravo-Alfaro et al., 2001; Elmegreen et al., 2002; Boselli et al., 2006). We
also observed some passive spirals in cluster outskirts ($r>R_{200}$), which
may have been formed through the pre-processing in falling subclusters
(Fujita, 2004; Rhee et al., 2020).

Finally, the environmental effects would not be the only causal factors that
transform star-forming spirals into passive spirals because passive spirals
are observed in low-density environments and in the vicinity of galaxy
clusters and groups (see also Masters et al. (2010)). Nevertheless, our
results suggest that the environmental effects can extend the passive spiral
phase and/or help spiral galaxies to be quiescent.

Figure 10:  The phase-space diagram for our samples (gray dots) associated
with X-ray clusters ($>10^{14}$ M⊙; Clerc et al. (2016)). The blue circles and
red squares are spirals and passive spiral galaxies, respectively. The purple
contours indicate peak areas of the passive fraction of spiral galaxies ($\geq
40$%) on the diagram, reaching up to $\sim 50$%. The dashed curves and gray
regions are the escape velocity and virialized area, respectively (Jaffé et
al., 2015).

##  5 Conclusion

Based on the deep learning morphology classification for the HSC-SSP PDR3
imaging data, we constructed 1100 passive spiral samples at $z<0.3$ over 1072
deg2, which are in a quiescent phase as inferred from their UV to mid-IR SEDs
by the GSWLC-2 catalog (Salim et al., 2016, 2018). As a result, we obtained
1100 relatively reliable passive spirals and 1141 secondary samples consisting
of ringed S0, pseudo-ringed galaxies, and false detection or peculiar types
through visual inspection. Validation flags based on visual inspections by
authors and selections with deeper UV data will allow users to flexibly select
more (or less) secure passive spirals. All data related to this study are
publicly available on the data release site of the HSC-SSP PDR3
(https://hsc.mtk.nao.ac.jp/ssp/data-release/). Our large passive galaxy sample
will help ones to delve into more detailed properties of passive spirals in
individuals to unveil the late evolutionary phase of spiral galaxies. Some
secondary samples have unique structures, such as polar rings and diamond
wings. While these special morphological types are beyond the scope of this
study, they would provide us with further insight into the formation
mechanisms of galaxy morphologies.

We also found that the characteristics of stellar populations in passive
spirals inferred from the EW${}_{\mathrm{H\delta_{A}}}$ and Dn4000 indices
were similar to those of typical quiescent galaxies. Further research is
required to understand the basis for such morphological discrepancies between
general quiescent galaxies and passive spirals; for instance, IFU observations
in the optical regime (Ishigaki et al., 2007) and high-resolution cold gas
observation with the Atacama Large Millimeter/submillimeter Array (ALMA) will
be helpful in resolving the detailed kinematics and consumption processes of
cold gas. In addition, we investigated the kinematic positions of passive
spirals associated with X-ray clusters on the phase-space diagram. We then
detected the peak fraction of the passive spirals over spiral galaxies around
the midterm to late infall phase on the phase-space plane, supporting the ram
pressure scenario, which has been widely suggested in previous studies (Bothun
& Sullivan, 1980; Giovanelli & Haynes, 1983; Cayatte et al., 1994; Bravo-
Alfaro et al., 2001; Elmegreen et al., 2002; Boselli et al., 2006).

The current passive spiral sample still relies highly on visual inspection,
especially to divide passive spirals with ringed S0 galaxies. Such a time-
consuming procedure should be automated when the large next-gen datasets are
delivered by such as the LSST project on the Vera C. Rubin Observatory (Ivezić
et al., 2019) and the High Latitude Wide Area Survey with the Nancy Grace
Roman Space Telescope (Spergel et al., 2015). To achieve a more effective
classification, the citizen science project dedicated to the HSC data, the
GALAXY CRUISE program, will be quite useful for the creation of the training
data, which provides us with not only the spiral classification but also the
presence or absence of the ring feature. Thus, future updates combined with
the GALAXY CRUISE data will enable us to conduct more effective and
comprehensive classifications.

Based on data collected at the Subaru Telescope and retrieved from the HSC
data archive system, which is operated by Subaru Telescope and Astronomy Data
Center at National Astronomical Observatory of Japan. We are honored and
grateful for the opportunity of observing the Universe from Maunakea, which
has the cultural, historical and natural significance in Hawaii.

The Hyper Suprime-Cam (HSC) collaboration includes the astronomical
communities of Japan and Taiwan, and Princeton University. The HSC
instrumentation and software were developed by the National Astronomical
Observatory of Japan (NAOJ), the Kavli Institute for the Physics and
Mathematics of the Universe (Kavli IPMU), the University of Tokyo, the High
Energy Accelerator Research Organization (KEK), the Academia Sinica Institute
for Astronomy and Astrophysics in Taiwan (ASIAA), and Princeton University.
Funding was contributed by the FIRST program from Japanese Cabinet Office, the
Ministry of Education, Culture, Sports, Science and Technology (MEXT), the
Japan Society for the Promotion of Science (JSPS), Japan Science and
Technology Agency (JST), the Toray Science Foundation, NAOJ, Kavli IPMU, KEK,
ASIAA, and Princeton University. This paper makes use of software developed
for the Large Synoptic Survey Telescope. We thank the LSST Project for making
their code available as free software at http://dm.lsst.org.

The Pan-STARRS1 Surveys (PS1) have been made possible through contributions of
the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project
Office, the Max-Planck Society and its participating institutes, the Max
Planck Institute for Astronomy, Heidelberg and the Max Planck Institute for
Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham
University, the University of Edinburgh, Queen’s University Belfast, the
Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory
Global Telescope Network Incorporated, the National Central University of
Taiwan, the Space Telescope Science Institute, the National Aeronautics and
Space Administration under Grant No. NNX08AR22G issued through the Planetary
Science Division of the NASA Science Mission Directorate, the National Science
Foundation under Grant No. AST-1238877, the University of Maryland, and Eotvos
Lorand University (ELTE) and the Los Alamos National Laboratory.

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.sdss.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.

We thank anonymous referee for helpful feedback. CB gratefully acknowledges
the support of a post-doctoral scholarship from the Natural Sciences and
Engineering Research Council of Canada. PFW acknowledges the support of the
fellowship from the East Asian Core Observatories Association. We would like
to thank Editage (www.editage.com) for English language editing.

This work made extensive use of the following tools, NumPy (Harris et al.,
2020), Matplotlib (Hunter, 2007), the Tool for OPerations on Catalogues And
Tables, TOPCAT (Taylor, 2005), a community-developed core Python package for
Astronomy, Astopy (Astropy Collaboration et al., 2013), and Python Data
Analysis Library pandas (Wes McKinney, 2010).

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Figure 11:  The main samples.  Figure 12:  The secondary samples.