J/A+A/679/A142 Mergers in the HSC-SSP (Omori+, 2023)
Galaxy mergers in Subaru HSC-SSP: A deep representation learning approach for
identification, and the role of environment on merger incidence.
Omori K.C., Bottrell C., Walmsley M., Yesuf H.M., Goulding A.D., Ding X.,
Popping G., Silverman J.D., Takeuchi T.T., Toba Y.
<Astron. Astrophys. 679, A142 (2023)>
=2023A&A...679A.142O 2023A&A...679A.142O (SIMBAD/NED BibCode)
ADC_Keywords: Galaxies, optical ; Galaxies, interacting ; Redshifts
Keywords: galaxies: evolution - galaxies: interactions -
methods: data analysis - galaxies: abundances - galaxies: statistics
Abstract:
Galaxy mergers and interactions are an important process within the
context of galaxy evolution, however, there is still no definitive
method which identifies pure and complete merger samples is still not
definitive. A method for creating such a merger sample is required so
that studies can be conducted to deepen our understanding of the
merger process and its impact on galaxy evolution.
In this work, we take a deep learning-based approach for galaxy merger
identification in Subaru HSC-SSP, specifically through the use of deep
representation learning and fine-tuning, with the aim of creating a
pure and complete merger sample within the HSC-SSP survey. We can use
this merger sample to conduct studies on how mergers affect galaxy
evolution.
We use Zoobot, a deep learning representation learning
model pre-trained on citizen science votes on Galaxy Zoo DeCALS
images. We fine-tune Zoobot for the purpose of merger classification
of images of SDSS and GAMA galaxies in HSC-SSP PDR 3. Fine-tuning is
done using 1200 synthetic HSC-SSP images of galaxies from the TNG
simulation. We then find merger probabilities on observed HSC images
using the fine-tuned model. Using our merger probabilities, we examine
the relationship between merger activity and environment.
We find that our fine-tuned model returns an accuracy on the synthetic
validation data of 76%. This number is comparable to those of previous
studies where convolutional neural networks were trained with
simulation images, but with our work requiring a far smaller number of
training samples. For our synthetic data, our model is able to achieve
completeness and precision values of 80%. In addition, our model is
able to correctly classify both mergers and non-mergers of diverse
morphologies and structures, including those at various stages and
mass ratios, while distinguishing between projections and merger
pairs. For the relation between galaxy mergers and environment, we
find two distinct trends. Using stellar mass overdensity estimates for
TNG simulations and observations using SDSS and GAMA, we find that
galaxies with higher merger scores favor lower density environments on
scales of 0.5 to 8h-1Mpc. However, below these scales in the
simulations, we find that galaxies with higher merger scores favor
higher density environments.
We fine-tuned a citizen-science trained deep representation learning
model for purpose of merger galaxy classification in HSC-SSP, and make
our merger probability catalog available to the public. Using our
morphology-based catalog, we find that mergers are more prevalent in
lower density environments on scales of 0.5-8h-1Mpc.
Description:
Merger probability catalog for galaxies in HSC-SSP PDR3 cross matched
with SDSS and GAMA. For each galaxy identification and coordinates,
spectroscopic redshift, stellar mass, effective radii and merger
probability are available.
File Summary:
--------------------------------------------------------------------------------
FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
table3.dat 96 136130 Merger probabilities for ∼130000 HSC Galaxies
--------------------------------------------------------------------------------
Byte-by-byte Description of file: table3.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 17 I17 --- ObjID HSC PDR3 object ID (object_id)
19- 29 F11.7 deg RAdeg Right ascension (J2000) (ra)
31- 41 F11.8 deg DEdeg Declination (J2000) (dec)
43- 50 F8.6 --- z Spectroscopic redshift (z)
52- 60 F9.6 [Msun] logM ?=- log stellar mass (logM)
62- 74 F13.7 arcsec Reff ?=-9999 Effective radius (R_eff)
76- 96 F21.19 --- Pmerg Merger probability (prediction)
--------------------------------------------------------------------------------
Acknowledgements:
Kiyoaki Christopher Omori,
omori.kiyoaki.christopher.v3(at)s.mail.nagoya-u.ac.jp
(End) Patricia Vannier [CDS] 02-Oct-2023