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.

15 pages, 14 figures, accepted for publication in ApJ

References in corpus (7)

• The Cosmic Evolution Survey (COSMOS) -- Overview
• 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
• The Complete Calibration of the Color-Redshift Relation (C3R2) Survey: Analysis and Data Release 2
• 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
• Predicting the global far-infrared SED of galaxies via machine learning techniques

Cited by in corpus (4)

• 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