CaltechAUTHORS
Published January 10, 2023 | Version Published
Journal Article Open

A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys

Creators

  • 1. ROR icon Carnegie Observatories
  • 2. ROR icon University of California, Irvine
  • 3. ROR icon University of California, Riverside
  • 4. ROR icon Infrared Processing and Analysis Center
  • 5. ROR icon Space Telescope Science Institute
  • 6. ROR icon University of Hawaii at Manoa
  • 7. ROR icon University of Copenhagen
  • 8. ROR icon Jet Propulsion Lab
  • 9. ROR icon Institut d'Astrophysique de Paris
  • 10. ROR icon NOIRLab
  • 11. ROR icon French National Centre for Scientific Research
  • 12. ROR icon University of Massachusetts Amherst

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 desired accuracy. As a proof of concept, using the recently updated COSMOS catalog (COSMOS2020), we identify the most relevant wave bands for measuring the physical properties of galaxies in a Hawaii Two-0- (H20) and UVISTA-like survey for a sample of i 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 intercorrelation between the bands, we train a model to predict UVISTA observations in near-IR from H20-like observations. We find that magnitudes in the YJH bands can be simulated/predicted with an accuracy of 1σ mag scatter ≲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.

Additional Information

Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. We thank the anonymous referee for providing insightful comments and suggestions that improved the quality of this work. N.C. and A.C. acknowledge support from NASA ADAP 80NSSC20K0437. I.D. has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 896225.

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Additional details

Identifiers

Eprint ID
119086
Resolver ID
CaltechAUTHORS:20230207-728273600.8

Funding

NASA
80NSSC20K0437
Marie Curie Fellowship
896225

Dates

Created
2023-03-14
Created from EPrint's datestamp field
Updated
2023-03-14
Created from EPrint's last_modified field

Caltech Custom Metadata

Caltech groups
Infrared Processing and Analysis Center (IPAC)