COSMOS2020: Manifold learning to estimate physical parameters in large galaxy surveys

¹ Cosmic Dawn Center (DAWN), Denmark
² Niels Bohr Institute, University of Copenhagen, Jagtvej 128, 2200 Copenhagen N, Denmark
e-mail: [email protected]
³ Núcleo de Astronomía de la Facultad de Ingeniería y Ciencias, Universidad Diego Portales, Av. Ejército Libertador 441, Santiago, Chile
e-mail: [email protected]
⁴ Aix Marseille Univ., CNRS, CNES, LAM, Marseille, France
⁵ California Institute of Technology, Pasadena, CA 91125, USA
⁶ Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
⁷ Leiden Observatory, Leiden University, PO Box 9513 2300 RA Leiden, The Netherlands
⁸ Sorbonne Université, CNRS, UMR 7095, Institut d’Astrophysique de Paris, 98 bis Bd Arago, 75014 Paris, France
⁹ Infrared Processing and Analysis Center, California Institute of Technology, Pasadena, CA 91125, USA
¹⁰ DTU-Space, Technical University of Denmark, Elektrovej 327, 2800 Kgs. Lyngby, Denmark
¹¹ National Centre for Nuclear Research, ul. Pasteura 7, 02-093 Warszawa, Poland
¹² Physics and Astronomy Department, University of California, 900 University Avenue, Riverside, CA 92521, USA
¹³ Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA

Received: 2 February 2022
Accepted: 6 June 2022

Abstract

We present a novel method for estimating galaxy physical properties from spectral energy distributions (SEDs) as an alternative to template fitting techniques and based on self-organizing maps (SOMs) to learn the high-dimensional manifold of a photometric galaxy catalog. The method has previously been tested with hydrodynamical simulations in Davidzon et al. (2019, MNRAS, 489, 4817), however, here it is applied to real data for the first time. It is crucial for its implementation to build the SOM with a high-quality panchromatic data set, thus we selected “COSMOS2020” galaxy catalog for this purpose. After the training and calibration steps with COSMOS2020, other galaxies can be processed through SOMs to obtain an estimate of their stellar mass and star formation rate (SFR). Both quantities resulted in a good agreement with independent measurements derived from more extended photometric baseline and, in addition, their combination (i.e., the SFR vs. stellar mass diagram) shows a main sequence of star-forming galaxies that is consistent with the findings of previous studies. We discuss the advantages of this method compared to traditional SED fitting, highlighting the impact of replacing the usual synthetic templates with a collection of empirical SEDs built by the SOM in a “data-driven” way. Such an approach also allows, even for extremely large data sets, for an efficient visual inspection to identify photometric errors or peculiar galaxy types. While also considering the computational speed of this new estimator, we argue that it will play a valuable role in the analysis of oncoming large-area surveys such as Euclid of the Legacy Survey of Space and Time at the Vera C. Rubin Telescope.