Towards universal hybrid star formation rate estimators
M. Boquien, R. Kennicutt, D. Calzetti, D. Dale, M. Galametz, M., Sauvage, K. Croxall, B. Draine, A. Kirkpatrick, N. Kumari, L. Hunt, I. De, Looze, E. Pellegrini, M. Relano, J.-D. Smith, and F. Tabatabaei

TL;DR
This paper investigates the physical factors influencing hybrid UV+IR estimators for galaxy star formation rates, proposing new models that adapt to galaxy properties for more accurate dust attenuation correction.
Contribution
It identifies key physical drivers of estimator variability and introduces new universal hybrid estimators based on galaxy colors and luminosity densities.
Findings
Hybrid estimators depend strongly on stellar mass surface density and sSFR.
IR scaling coefficients can vary by nearly an order of magnitude.
New estimators outperform classical methods in estimating dust-corrected UV emission.
Abstract
To compute the SFR of galaxies from the rest-frame UV it is essential to take into account the obscuration by dust. To do so, one of the most popular methods consists in combining the UV with the emission from the dust itself in the IR. Yet, different studies have derived different estimators, showing that no such hybrid estimator is truly universal. In this paper we aim at understanding and quantifying what physical processes drive the variations between different hybrid estimators. Doing so, we aim at deriving new universal UV+IR hybrid estimators to correct the UV for dust attenuation, taking into account the intrinsic physical properties of galaxies. We use the CIGALE code to model the spatially-resolved FUV to FIR SED of eight nearby star-forming galaxies drawn from the KINGFISH sample. This allows us to determine their local physical properties, and in particular their UV…
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.
