Evaluation of Automated Fermi GBM Localizations of Gamma-ray Bursts
Adam Goldstein, Corinne Fletcher, Peter Veres, Michael S. Briggs,, William H. Cleveland, Melissa H. Gibby, C. Michelle Hui, Elisabetta Bissaldi,, Eric Burns, Rachel Hamburg, Andreas von Kienlin, Daniel Kocevski, Bagrat, Mailyan, Christian Malacaria, William S. Paciesas

TL;DR
This paper evaluates and compares automated algorithms for localizing gamma-ray bursts with Fermi GBM, demonstrating improvements in accuracy and systematic uncertainty estimation, which enhances the reliability of GRB localizations.
Contribution
The study provides a systematic comparison of RoboBA and BALROG algorithms, introduces improvements to RoboBA, and quantifies their systematic uncertainties for better GRB localization accuracy.
Findings
Updated RoboBA reduces systematic uncertainty to 1.8° for over half of GRBs.
BALROG's systematic uncertainty is underestimated, being around 2.7° for most cases.
RoboBA's localizations can be significantly more precise than BALROG when considering uncertainties.
Abstract
The capability of the Fermi Gamma-ray Burst Monitor (GBM) to localize gamma-ray bursts (GRBs) is evaluated for two different automated algorithms: the GBM Team's RoboBA algorithm and the independently developed BALROG algorithm. Through a systematic study utilizing over 500 GRBs with known locations from instruments like Swift and the Fermi LAT, we directly compare the effectiveness of, and accurately estimate the systematic uncertainty for, both algorithms. We show simple adjustments to the GBM Team's RoboBA, in operation since early 2016, yields significant improvement in the systematic uncertainty, removing the long tail identified in the systematic, and improves the overall accuracy. The systematic uncertainty for the updated RoboBA localizations is for 52% of GRBs and for the remaining 48%. Both from public reporting by BALROG and our systematic study, we…
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Taxonomy
TopicsGamma-ray bursts and supernovae
