A Machine Learning Approach to Predict Missing Flux Densities in Multi-band Galaxy Surveys

Abstract

We present a new method based on information theory to find the optimal number of bands required to measure the physical properties of galaxies with a desired accuracy. As a proof of concept, using the recently updated COSMOS catalog (COSMOS2020), we identify the most relevant wavebands for measuring the physical properties of galaxies in a Hawaii Two-0 (H20)- and UVISTA-like survey for a sample of $i<25$ AB mag galaxies. We find that with available $i$-band fluxes, $r$, $u$, IRAC/$ch2$ and $z$ bands provide most of the information regarding the redshift with importance decreasing from $r$-band to $z$-band. We also find that for the same sample, IRAC/$ch2$, $Y$, $r$ and $u$ bands are the most relevant bands in stellar mass measurements with decreasing order of importance. Investigating the inter-correlation between the bands, we train a model to predict UVISTA observations in near-IR from H20-like observations. We find that magnitudes in $YJH$ bands can be simulated/predicted with an accuracy of $1\sigma$ mag scatter $\lesssim 0.2$ for galaxies brighter than 24 AB mag in near-IR bands. One should note that these conclusions depend on the selection criteria of the sample. For any new sample of galaxies with a different selection, these results should be remeasured. Our results suggest that in the presence of a limited number of bands, a machine learning model trained over the population of observed galaxies with extensive spectral coverage outperforms template-fitting. Such a machine learning model maximally comprises the information acquired over available extensive surveys and breaks degeneracies in the parameter space of template-fitting inevitable in the presence of a few bands.

arXiv comment15 pages, 14 figures, accepted for publication in ApJ

References in corpus (23)

• Stellar population synthesis at the resolution of 2003
• Galactic Stellar and Substellar Initial Mass Function
• Measuring Reddening with SDSS Stellar Spectra and Recalibrating SFD
• The Dust Content and Opacity of Actively Star-Forming Galaxies
• Estimating Mutual Information
• The Cosmic Evolution Survey (COSMOS) -- Overview
• Accurate photometric redshifts for the CFHT Legacy Survey calibrated using the VIMOS VLT Deep Survey
• The COSMOS2015 Catalog: Exploring the 1<z<6 Universe with half a million galaxies
• Mass assembly in quiescent and star-forming galaxies since z=4 from UltraVISTA
• Measuring and Modelling the Redshift Evolution of Clustering: the Hubble Deep Field North
• UltraVISTA: a new ultra-deep near-infrared survey in COSMOS
• COSMOS2020: A panchromatic view of the Universe to $z\sim 10$ from two complementary catalogs
• Mapping the Galaxy Color-Redshift Relation: Optimal Photometric Redshift Calibration Strategies for Cosmology Surveys
• The Complete Calibration of the Color-Redshift Relation (C3R2) Survey: Survey Overview and Data Release 1
• The Complete Calibration of the Color-Redshift Relation (C3R2) Survey: Analysis and Data Release 2
• SOMz: photometric redshift PDFs with self organizing maps and random atlas
• Euclid preparation: X. The Euclid photometric-redshift challenge
• Euclid preparation: XVIII. Cosmic Dawn Survey. Spitzer observations of the Euclid deep fields and calibration fields
• A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
• Horizon-AGN virtual observatory -- 2: Template-free estimates of galaxy properties from colours
• A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning
• Comparison of Observed Galaxy Properties with Semianalytic Model Predictions using Machine Learning
• Predicting the global far-infrared SED of galaxies via machine learning techniques

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• Deep learning insights into non-universality in the halo mass function
• pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population
• Data-Space Validation of High-Dimensional Models by Comparing Sample Quantiles
• Euclid preparation: TBD. Cosmic Dawn Survey: evolution of the galaxy stellar mass function across 0.2<z<6.5 measured over 10 square degrees