Statistical search for angular non-stationarities of long gamma-ray burst jets using Swift data
Andor Budai, Peter Raffai, Balint Borgulya, Brian Albert Dawes, Gabor, Szeifert

TL;DR
This study investigates the potential anti-correlation between jet angles and prompt light curve variabilities in long gamma-ray bursts, using Swift data, to determine if jet non-stationarities can be statistically inferred from prompt emission features.
Contribution
It provides the first statistical analysis testing the connection between jet angles and prompt variability, suggesting non-Gaussian jet profiles and highlighting the need for larger samples for confirmation.
Findings
Weak anti-correlation observed between jet angles and variability.
Results incompatible with Gaussian jet profiles.
Over 100 GRBs needed for 3σ confirmation of the correlation.
Abstract
In Budai et al. (2020) we argued that angular non-stationarities of gamma-ray burst (GRB) jets can result in a statistical connection between the angle values deduced from jet break times and the variabilities of prompt light curves. The connection should be an anti-correlation if luminosity densities of jets follow a power-law or a uniform profile, and a correlation if they have a Gaussian profile. In this follow-up paper, we search for the connection by measuring Spearman's rank correlation coefficient in a sample of 19 long GRBs observed by the Swift satellite. Using 16 of the GRBs with well-defined angle measurements, we find and . Adding three more GRBs to the sample, each with a pair of equally possible angle values, can strengthen the anti-correlation to and . We show…
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.
