Measuring Galaxy Star Formation Rates From Integrated Photometry: Insights from Color-Magnitude Diagrams of Resolved Stars
Benjamin D. Johnson, Daniel R. Weisz, Julianne J. Dalcanton, L., Clifton Johnson, Daniel A. Dale, Andrew E. Dolphin, Armando Gil de Paz,, Robert C. Kennicutt, Janice C. Lee, Evan D. Skillman, Benjamin F. Williams

TL;DR
This study uses empirical star formation histories from resolved stars to evaluate the accuracy of UV-based star formation rate indicators in dwarf galaxies, revealing significant uncertainties due to SFH fluctuations and stellar contributions.
Contribution
It demonstrates the impact of realistic, complex SFHs on UV SFR indicators and highlights limitations of current models in low-metallicity dwarf galaxies.
Findings
Modeled SEDs agree with observations from UV to optical bands.
Systematic over-prediction of IR luminosities linked to TP-AGB star treatment.
SFH fluctuations cause ~2x variation in UV luminosities, complicating SFR estimates.
Abstract
We use empirical star formation histories (SFHs), measured from HST-based resolved star color-magnitude diagrams, as input into population synthesis codes to model the broadband spectral energy distributions (SEDs) of ~50 nearby dwarf galaxies (6.5 < log M/M_* < 8.5, with metallicities ~10% solar). In the presence of realistic SFHs, we compare the modeled and observed SEDs from the ultraviolet (UV) through near-infrared (NIR) and assess the reliability of widely used UV-based star formation rate (SFR) indicators. In the FUV through i bands, we find that the observed and modeled SEDs are in excellent agreement. In the Spitzer 3.6micron and 4.5micron bands, we find that modeled SEDs systematically over-predict observed luminosities by up to ~0.2 dex, depending on treatment of the TP-AGB stars in the synthesis models. We assess the reliability of UV luminosity as a SFR indicator, in light…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
