GRB optical afterglow and redshift selection effects: The learning curve effect at work
D.M. Coward

TL;DR
The paper demonstrates how selection effects, particularly response time to obtain spectroscopic redshifts, influence the observed distributions of GRB optical afterglows and redshifts, affecting our understanding of their true properties.
Contribution
It identifies a new redshift selection effect related to response time, showing how it biases the observed redshift distribution of GRBs.
Findings
Shorter response times correlate with lower redshift bursts.
Longer response times bias towards brighter, higher redshift bursts.
Average redshift has decreased from 2.8 to 2 between 2005 and 2008.
Abstract
We show how the observed gamma ray burst (GRB) optical afterglow (OA) and redshift distributions are changing in time from selection effects. For a subset of {\it Swift} triggered long duration bursts, we show that the mean time taken to acquire spectroscopic redshifts for a GRB OA has evolved to shorter times. We identify a strong correlation between the mean time taken to acquire a spectroscopic redshift and the measured redshift. This correlation reveals that shorter response times favour smaller redshift bursts. This is compelling evidence for a selection effect that biases longer response times with relatively brighter high redshift bursts. Conversely, for shorter response times, optically fainter bursts that are relatively closer are bright enough for spectroscopic redshifts to be acquired. This selection effect could explain why the average redshift, measured 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.
