Galaxy Merger Rates up to z $\sim$ 3 using a Bayesian Deep Learning Model $-$ A Major-Merger classifier using IllustrisTNG Simulation data

Abstract

Merging is potentially the dominate 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 $\sim$ 3 in all five CANDELS fields (UDS, EGS, GOODS-S, GOODS-N, COSMOS) using deep learning convolutional neural networks (CNNs) trained with simulated galaxies from the IllustrisTNG cosmological simulation. The deep learning architecture used is objectively selected by a Bayesian Optmization 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. (2015), and show that they are broadly consistent. We finish by demonstrating that our model is capable of measuring galaxy merger rates, $\mathcal{R}$, that are consistent with results found for CANDELS galaxies using close pairs statistics, with $\mathcal{R}(z)=0.02\pm 0.004\times (1+z{)}^{2.76\pm 0.21}$. This is the first general agreement between major mergers measured using pairs and structure at z < 3.

arXiv comment22 pages, 13 figures, 3 tables, accepted for publication in ApJ

References in corpus (39)

• Cosmic Star Formation History
• Binary companions of evolved stars in APOGEE DR14: Search method and catalog of ~5,000 companions
• The EAGLE project: Simulating the evolution and assembly of galaxies and their environments
• Introducing the Illustris Project: Simulating the coevolution of dark and visible matter in the Universe
• CANDELS: The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey
• CANDELS: The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey - The Hubble Space Telescope Observations, Imaging Data Products and Mosaics
• The propagation of uncertainties in stellar population synthesis modeling I: The relevance of uncertain aspects of stellar evolution and the IMF to the derived physical properties of galaxies
• First results from the IllustrisTNG simulations: the galaxy color bimodality
• The propagation of uncertainties in stellar population synthesis modeling III: model calibration, comparison, and evaluation
• The Relationship Between Stellar Light Distributions of Galaxies and their Formation Histories
• A New Non-Parametric Approach to Galaxy Morphological Classification
• The merger rate of galaxies in the Illustris Simulation: a comparison with observations and semi-empirical models
• A Direct Measurement of Major Galaxy Mergers at z < 3
• The Evolution of Galaxy Structure over Cosmic Time
• The optical morphologies of galaxies in the IllustrisTNG simulation: a comparison to Pan-STARRS observations
• Galaxy Merger Morphologies and Time-Scales from Simulations of Equal-Mass Gas-Rich Disc Mergers
• The Fundamental Properties of Galaxies and a New Galaxy Classification System
• Star formation sustained by gas accretion
• COSMOS-DASH: The Evolution of the Galaxy Size-Mass Relation Since z~3 from new Wide Field WFC3 Imaging Combined with CANDELS/3DHST
• Galaxy Zoo: The properties of merging galaxies in the nearby Universe - local environments, colours, masses, star-formation rates and AGN activity
• Observational constraints on the merger history of galaxies since $z\approx 6$: Probabilistic galaxy pair counts in the CANDELS fields
• Evolution of Disk Galaxies in the GOODS-South Field: Number Densities and Size Distribution
• CANDELS Visual Classifications: Scheme, Data Release, and First Results
• Using transfer learning to detect galaxy mergers
• Resolving the Discrepancy of Galaxy Merger Fraction Measurements at z ~ 0 - 3
• A consistent measure of the merger histories of massive galaxies using close-pair statistics I: Major mergers at $z<3.5$
• Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging
• Deep learning predictions of galaxy merger stage and the importance of observational realism
• Mass assembly and morphological transformations since $z\sim 3$ from CANDELS
• Massive Close Pairs Measure Rapid Galaxy Assembly in Mergers at High Redshift
• The Hubble Sequence at $z\sim 0$ in the IllustrisTNG simulation with deep learning
• Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range
• Identifying Galaxy Mergers in Observations and Simulations with Deep Learning
• Galaxy morphological classification in deep-wide surveys via unsupervised machine learning
• Automated Distant Galaxy Merger Classifications from Space Telescope Images using the Illustris Simulation
• FERENGI: Redshifting galaxies from SDSS to GEMS, STAGES and COSMOS
• Deblending galaxy superpositions with branched generative adversarial networks
• Galaxy Formation as a Cosmological Tool. I: The Galaxy Merger History as a Measure of Cosmological Parameters
• The Structures of Distant Galaxies - IV: A New Empirical Measurement of the Time-Scale for Galaxy Mergers - Implications for the Merger History

Cited by in corpus (62)

• Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
• EPOCHS VI: The Size and Shape Evolution of Galaxies since z ~ 8 with JWST Observations
• The DAWES review 10: The impact of deep learning for the analysis of galaxy surveys
• Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG
• Galaxy mergers can rapidly shut down star formation
• Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks
• IllustrisTNG in the HSC-SSP: image data release and the major role of mini mergers as drivers of asymmetry and star formation
• The merger fraction of post-starburst galaxies in UNIONS
• A direct measurement of galaxy major and minor merger rates and stellar mass accretion histories at $z<3$ using galaxy pairs in the REFINE survey
• Galaxy Evolution in all Five CANDELS Fields and IllustrisTNG: Morphological, Structural, and the Major Merger Evolution to $z\sim 3$
• Characterization of Low Surface Brightness structures in annotated deep images
• Galaxy Mergers in the Epoch of Reionization I: A JWST Study of Pair Fractions, Merger Rates, and Stellar Mass Accretion Rates at $z=4.5-11.5$
• DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains
• ERGO-ML I: Inferring the assembly histories of IllustrisTNG galaxies from integral observable properties via invertible neural networks
• Star formation characteristics of CNN-identified post-mergers in the Ultraviolet Near Infrared Optical Northern Survey (UNIONS)
• Quantifying Non-parametric Structure of High-redshift Galaxies with Deep Learning
• The combined and respective roles of imaging and stellar kinematics in identifying galaxy merger remnants
• North Ecliptic Pole merging galaxy catalogue
• Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models
• Star Formation Suppresion by Tidal Removal of Cold Molecular Gas from an Intermediate-Redshift Massive Post-Starburst Galaxy
• Identifying Galaxy Mergers in Simulated CEERS NIRCam Images using Random Forests
• Towards a consistent framework of comparing galaxy mergers in observations and simulations
• AGN in post-mergers from the Ultraviolet Near Infrared Optical Northern Survey
• Towards robust determination of non-parametric morphologies in marginal astronomical data: resolving uncertainties with cosmological hydrodynamical simulations
• A re-assessment of strong line metallicity conversions in the machine learning era
• From haloes to galaxies -- II. The fundamental relations in star formation and quenching
• The observability of galaxy merger signatures in nearby gas-rich spirals
• A Robust Study of High-Redshift Galaxies: Unsupervised Machine Learning for Characterising morphology with JWST up to z ~ 8
• OH Megamasers in HI Surveys: Forecasts and a Machine Learning Approach to Separating Disks from Mergers
• Galaxy Morphologies Revealed with Subaru HSC and Super-Resolution Techniques I: Major Merger Fractions of L_UV~3-15 L_UV* Dropout Galaxies at z~4-7
• Dust and Power: Unravelling the merger - active galactic nucleus connection in the second half of cosmic history
• The Connection between Mergers and AGN Activity in Simulated and Observed Massive Galaxies
• Galaxy merger challenge: A comparison study between machine learning-based detection methods
• Do galaxy mergers prefer under-dense environments?
• ERGO-ML: Towards a robust machine learning model for inferring the fraction of accreted stars in galaxies from integral-field spectroscopic maps
• Deep learning for nano-photonic materials -- The solution to everything!?
• Lessons Learned from the Two Largest Galaxy Morphological Classification Catalogues built by Convolutional Neural Networks
• De-noising of galaxy optical spectra with autoencoders
• A Comparison of Star-Forming Clumps and Tidal Tails in Local Mergers and High Redshift Galaxies
• Mock Galaxy Surveys for HST and JWST from the IllustrisTNG Simulations
• An astronomical image content-based recommendation system using combined deep learning models in a fully unsupervised mode
• Uncovering Tidal Treasures: Automated Classification of Faint Tidal Features in DECaLS Data
• SDSS-IV MaNGA: Unveiling Galaxy Interaction by Merger Stages with Machine Learning
• Merger identification through photometric bands, colours, and their errors
• Inferring redshift and galaxy properties via a multi-task neural net with probabilistic outputs: An application to simulated MOONS spectra
• Critical Stellar Central Densities Drive Galaxy Quenching in the Nearby Universe
• DeepGalaxy: Deducing the Properties of Galaxy Mergers from Images Using Deep Neural Networks
• Determining the time before or after a galaxy merger event
• Galaxy Zoo JWST: Up to 75% of discs are featureless at $3<z<7$
• Astraeus VII: The environmental-dependent assembly of galaxies in the Epoch of Reionization
• The Ly$\alpha$ and Continuum Origins Survey. III. Investigating the Link between Galaxy Morphology, Merger Properties and LyC Escape
• AGN -- host galaxy photometric decomposition using a fast, accurate and precise deep learning approach
• Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies
• Harvesting the Lyα forest with convolutional neural networks
• GalaxyGenius: Mock galaxy image generator for various telescopes from hydrodynamical simulations
• The spatially-resolved effect of mergers on the stellar mass assembly of MaNGA galaxies
• Enhanced AGN Activity in Overdense Galactic Environments at $2<z<4$
• Classifying merger stages with adaptive deep learning and cosmological hydrodynamical simulations
• Describing the Nonuniversal Galaxy Merger Timescales in IllustrisTNG: Effects of Host Halo Mass, Baryons, and Sample Selection
• Are all post-starbursts mergers? HST reveals hidden disturbances in the majority of PSBs
• The major merger-active galactic nucleus connection up to the cosmic noon
• A Simulation Driven Deep Learning Approach for Separating Mergers and Star Forming Galaxies: The Formation Histories of Clumpy Galaxies in all the CANDELS Fields