Dust extinction for an unbiased sample of GRB afterglows
S. Covino, A. Melandri, R. Salvaterra, S. Campana, S. D. Vergani, M.G., Bernardini, P. D'Avanzo, V. D'Elia, D. Fugazza, G. Ghirlanda, G. Ghisellini,, A. Gomboc, Z.P. Jin, T. Kruehler, D. Malesani, L. Nava, B. Sbarufatti, G., Tagliaferri

TL;DR
This study measures rest-frame dust extinction in a complete, unbiased sample of gamma-ray burst afterglows, revealing diverse extinction levels and no clear evolution with redshift, with implications for understanding GRB environments.
Contribution
It provides the first unbiased assessment of dust extinction in GRB afterglows, showing a wide distribution and highlighting the presence of highly obscured events.
Findings
87% of afterglows have less than 2 mag extinction
50% of afterglows suffer less than 0.3-0.4 mag extinction
High extinction events may be underrepresented due to redshift measurement biases
Abstract
In this paper we compute rest-frame extinctions for the afterglows of a sample of gamma-ray bursts complete in redshift. The selection criteria of the sample are based on observational high-energy parameters of the prompt emission and therefore our sample should not be biased against dusty sight-lines. It is therefore expected that our inferences hold for the general population of gamma-ray bursts. Our main result is that the optical/near-infrared extinction of gamma-ray burst afterglows in our sample does not follow a single distribution. 87% of the events are absorbed by less than 2 mag, and 50% suffer from less than 0.3-0.4 mag extinction. The remaining 13% of the afterglows are highly absorbed. The true percentage of gamma-ray burst afterglows showing high absorption could be even higher since a fair fraction of the events without reliable redshift measurement are probably part of…
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.
