Mapping the Galaxy Color-Star Formation Rate Relation with Manifold Learning and Infrared Image Stacking
Yu-Heng Lin, Daniel Masters, Andreas L. Faisst, Harry Teplitz, Olivier Ilbert, Matthieu Bethermin, Shoubaneh Hemmati, Vihang Mehta, Jason D. Rhodes, Gregory L. Walth

TL;DR
This paper introduces a novel method using manifold learning and infrared stacking to empirically relate galaxy colors to star formation rates, enabling analysis of faint, high-redshift galaxies without extensive spectroscopic data.
Contribution
It presents an innovative approach combining Self-Organizing Maps and infrared stacking to calibrate star formation rates from broadband colors for large galaxy samples.
Findings
Calibrated SFRs for nearly half of COSMOS galaxies down to i-band magnitude 25.5.
Mapped the evolution of the galaxy main sequence up to redshift 2.5.
Demonstrated the effectiveness of color-based stacking for FIR luminosity estimation.
Abstract
Modern surveys present us with billions of faint galaxies for which we only have broadband images in 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 4040 SOM…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Scientific Research and Discoveries
