How Well Can We Measure the Stellar Mass of a Galaxy: The Impact of the Assumed Star Formation History Model in SED Fitting
Sidney Lower, Desika Narayanan, Joel Leja, Benjamin D. Johnson,, Charlie Conroy, and Romeel Dav\'e

TL;DR
This study evaluates how different assumptions about galaxy star formation histories in SED modeling affect the accuracy of inferred stellar masses, showing nonparametric models significantly reduce biases and improve property recovery.
Contribution
The paper demonstrates that nonparametric star formation histories outperform traditional parametric forms in SED fitting, reducing biases and enhancing the accuracy of galaxy property estimates.
Findings
Nonparametric SFHs reduce stellar mass bias to under 0.05 dex.
Flexible SFHs improve accuracy of SFR and age estimates.
Parametric models show a bias decrease from 0.4 dex to near 0.05 dex.
Abstract
The primary method for inferring the stellar mass () of a galaxy is through spectral energy distribution (SED) modeling. However, the technique rests on assumptions such as the galaxy star formation history and dust attenuation law that can severely impact the accuracy of derived physical properties from SED modeling. Here, we examine the effect that the assumed star formation history (SFH) has on the stellar properties inferred from SED fitting by ground truthing them against mock observations of high-resolution cosmological hydrodynamic galaxy formation simulations. Classically, SFHs are modeled with simplified parameterized functional forms, but these forms are unlikely to capture the true diversity of galaxy SFHs and may impose systematic biases with under-reported uncertainties on results. We demonstrate that flexible nonparametric star formation histories outperform…
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