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Main Authors: Lin, Yu-Heng, Masters, Daniel, Faisst, Andreas L., Teplitz, Harry, Ilbert, Olivier, Bethermin, Matthieu, Hemmati, Shoubaneh, Mehta, Vihang, Rhodes, Jason D., Walth, Gregory L.
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2511.20900
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author Lin, Yu-Heng
Masters, Daniel
Faisst, Andreas L.
Teplitz, Harry
Ilbert, Olivier
Bethermin, Matthieu
Hemmati, Shoubaneh
Mehta, Vihang
Rhodes, Jason D.
Walth, Gregory L.
author_facet Lin, Yu-Heng
Masters, Daniel
Faisst, Andreas L.
Teplitz, Harry
Ilbert, Olivier
Bethermin, Matthieu
Hemmati, Shoubaneh
Mehta, Vihang
Rhodes, Jason D.
Walth, Gregory L.
contents Modern surveys present us with billions of faint galaxies for which we only have broadband images in $\sim$6-8 optical-to-near-infrared (NIR) filters. Galaxy star formation rates (SFRs) are difficult to estimate accurately without spectroscopic diagnostics or far-infrared (FIR) photometry, both of which are prohibitively expensive to obtain for large numbers of faint, high-redshift galaxies. Here we present the empirical relation between SFR and broadband optical-to-NIR colors learned from Spitzer MIPS and Herschel PACS/SPIRE imaging using an innovative stacking analysis that bins galaxies with similar optical-to-NIR spectral energy distributions using a Self-Organizing Map (SOM). Stacking based on optical-to-NIR colors ensures that our FIR stacks are built from galaxies with similar intrinsic physical properties as opposed to stacking simply by stellar mass. We train a 40$\times$40 SOM using 230,638 galaxies selected from the COSMOS field, and stack the mid-to-far infrared images from 24 micron to 500 micron. We are able to measure the median FIR luminosities from half of the SOM cells to calibrate the star formation rate. In addition to investigating the common structures of optical-to-NIR properties and FIR detections labeled on the SOM, we provide calibrated star formation rates for nearly half of the galaxies in the COSMOS fields down to $i-$band magnitude $\leq 25.5$, and present the evolution of the galaxy main sequence for low-mass galaxies to redshift $z\sim2.5$.
format Preprint
id arxiv_https___arxiv_org_abs_2511_20900
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Mapping the Galaxy Color-Star Formation Rate Relation with Manifold Learning and Infrared Image Stacking
Lin, Yu-Heng
Masters, Daniel
Faisst, Andreas L.
Teplitz, Harry
Ilbert, Olivier
Bethermin, Matthieu
Hemmati, Shoubaneh
Mehta, Vihang
Rhodes, Jason D.
Walth, Gregory L.
Astrophysics of Galaxies
Modern surveys present us with billions of faint galaxies for which we only have broadband images in $\sim$6-8 optical-to-near-infrared (NIR) filters. Galaxy star formation rates (SFRs) are difficult to estimate accurately without spectroscopic diagnostics or far-infrared (FIR) photometry, both of which are prohibitively expensive to obtain for large numbers of faint, high-redshift galaxies. Here we present the empirical relation between SFR and broadband optical-to-NIR colors learned from Spitzer MIPS and Herschel PACS/SPIRE imaging using an innovative stacking analysis that bins galaxies with similar optical-to-NIR spectral energy distributions using a Self-Organizing Map (SOM). Stacking based on optical-to-NIR colors ensures that our FIR stacks are built from galaxies with similar intrinsic physical properties as opposed to stacking simply by stellar mass. We train a 40$\times$40 SOM using 230,638 galaxies selected from the COSMOS field, and stack the mid-to-far infrared images from 24 micron to 500 micron. We are able to measure the median FIR luminosities from half of the SOM cells to calibrate the star formation rate. In addition to investigating the common structures of optical-to-NIR properties and FIR detections labeled on the SOM, we provide calibrated star formation rates for nearly half of the galaxies in the COSMOS fields down to $i-$band magnitude $\leq 25.5$, and present the evolution of the galaxy main sequence for low-mass galaxies to redshift $z\sim2.5$.
title Mapping the Galaxy Color-Star Formation Rate Relation with Manifold Learning and Infrared Image Stacking
topic Astrophysics of Galaxies
url https://arxiv.org/abs/2511.20900