Quantification of The Age Dependence of Mid-Infrared Star Formation Rate Indicators
Daniela Calzetti (1), Robert C. Kennicutt (2,3), Angela Adamo (4), Karin Sandstrom (5), Daniel A. Dale (6), Bruce Elmegreen (7), John S. Gallagher (8,9), Benjamin Gregg (1), Varun Bajaj (10), Torsten Boker (11), Giacomo Bortolini (4), Martha Boyer (10), Matteo Correnti (12,13)

TL;DR
This study uses JWST and other data to analyze how mid-infrared star formation rate indicators depend on galaxy age, revealing that older stellar populations significantly influence IR emission and calibrations.
Contribution
It provides a new calibration for infrared SFR across a wide luminosity range and quantifies the impact of stellar populations older than 5-6 Myr on IR emission.
Findings
The L(24)-L(Pa-alpha) correlation exponent exceeds 1 across six decades.
The hybrid 24 micron+H-alpha SFR indicator's scaling constant is ~4.4 times higher for HII regions.
Older stellar populations contribute significantly to IR emission, affecting SFR calibrations.
Abstract
We combine James Webb Space Telescope images of the nearby galaxy NGC 5194 in the hydrogen recombination line Pa-alpha (lambda=1.8756 micron) from the Cycle 1 program JWST-FEAST with 21 micron dust continuum images from the Cycle 2 Treasury program JWGT to quantify the difference in the calibration of mid-infrared star formation rates (SFR) between HII regions and galaxies. We use the archival HST H-alpha image to correct the Pa-alpha emission for the effects of dust attenuation. Our data confirm previous results that the dust-corrected Pa-alpha flux is tightly correlated with the 21 micron emission at the scales of HII regions. When combined with published JWST data for the HII regions of the galaxy NGC 628 and Spitzer 24 micron data for whole galaxies and for kpc-size galaxy regions, we show that the L(24)-L(Pa-alpha) correlation has exponent >1 across six decades in luminosity. In…
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.
