TY - JOUR
T1 - Galaxy Merger Rates up to z ~ 3 using a Bayesian Deep Learning Model: A Major-Merger classifier using IllustrisTNG Simulation data
AU - Ferreira, Leonardo
AU - Conselice, Christopher J.
AU - Duncan, Kenneth
AU - Cheng, Ting-Yun
AU - Griffiths, Alex
AU - Whitney, Amy
N1 - 22 pages, 13 figures, 3 tables, accepted for publication in ApJ
PY - 2020/6/3
Y1 - 2020/6/3
N2 - Merging is potentially the dominant process in galaxy formation, yet
there is still debate about its history over cosmic time. To address
this, we classify major mergers and measure galaxy merger rates up to z ~ 3
in all five CANDELS fields (UDS, EGS, GOODS-S, GOODS-N, COSMOS) using
deep learning convolutional neural networks trained with simulated
galaxies from the IllustrisTNG cosmological simulation. The deep
learning architecture used is objectively selected by a Bayesian
optimization process over the range of possible hyperparameters. We show
that our model can achieve 90% accuracy when classifying mergers from
the simulation and has the additional feature of separating mergers
before the infall of stellar masses from post-mergers. We compare our
machine-learning classifications on CANDELS galaxies and compare with
visual merger classifications from Kartaltepe et al., and show that they
are broadly consistent. We finish by demonstrating that our model is
capable of measuring galaxy merger rates, , that are consistent with results found for CANDELS galaxies using close pairs statistics, with . This is the first general agreement between major mergers measured using pairs and structure at z < 3.
AB - Merging is potentially the dominant process in galaxy formation, yet
there is still debate about its history over cosmic time. To address
this, we classify major mergers and measure galaxy merger rates up to z ~ 3
in all five CANDELS fields (UDS, EGS, GOODS-S, GOODS-N, COSMOS) using
deep learning convolutional neural networks trained with simulated
galaxies from the IllustrisTNG cosmological simulation. The deep
learning architecture used is objectively selected by a Bayesian
optimization process over the range of possible hyperparameters. We show
that our model can achieve 90% accuracy when classifying mergers from
the simulation and has the additional feature of separating mergers
before the infall of stellar masses from post-mergers. We compare our
machine-learning classifications on CANDELS galaxies and compare with
visual merger classifications from Kartaltepe et al., and show that they
are broadly consistent. We finish by demonstrating that our model is
capable of measuring galaxy merger rates, , that are consistent with results found for CANDELS galaxies using close pairs statistics, with . This is the first general agreement between major mergers measured using pairs and structure at z < 3.
KW - astro-ph.GA
KW - astro-ph.IM
U2 - 10.3847/1538-4357/ab8f9b
DO - 10.3847/1538-4357/ab8f9b
M3 - Article
SN - 0004-637X
VL - 895
JO - Astrophysical Journal
JF - Astrophysical Journal
IS - 2
ER -