Beware the recent past: a bias in spectral energy distribution modelling due to bursty star formation
P. Haskell, S. Das, D.J.B. Smith, R.K. Cochrane, C.C. Hayward, D., Angl\'es-Alc\'azar

TL;DR
This paper reveals a significant bias in spectral energy distribution modeling of galaxies caused by bursty star formation, highlighting the importance of non-parametric SFHs for accurate star formation rate recovery, especially in high-redshift galaxies.
Contribution
It demonstrates that parametric SFHs lead to underestimated uncertainties and biases in SFR estimates, and shows that non-parametric SFHs avoid this issue, improving modeling accuracy.
Findings
Magphys SFRs can differ from true values by up to 1 dex due to bursty SFH biases.
Prospector with non-parametric SFHs does not show this bias.
Parametric SFHs underestimate SFR uncertainties by up to 5 times.
Abstract
We investigate how the recovery of galaxy star formation rates (SFRs) using energy-balance spectral energy distribution (SED) fitting codes depends on their recent star formation histories (SFHs). We use the Magphys and Prospector codes to fit 6,706 synthetic spectral energy distributions of simulated massive galaxies at from the Feedback in Realistic Environments (FIRE) project. We identify a previously-unknown systematic error in the Magphys results due to bursty star formation: the derived SFRs can differ from the truth by as much as 1 dex, at large statistical significance (), depending on the details of their recent SFH. SFRs inferred using Prospector with non-parametric SFHs do not exhibit this trend. We show that using parametric SFHs (pSFHs) causes SFR uncertainties to be underestimated by a factor of up to . Although this undoubtedly contributes…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Stellar, planetary, and galactic studies · Gamma-ray bursts and supernovae
