A Fast-cadenced Search for Gamma-Ray Burst Orphan Afterglows with the Deeper, Wider, Faster Programme
James Freeburn, Jeff Cooke, Anais M\"oller, Dougal Dobie, Jielai, Zhang, Om Sharan Salafia, Karelle Siellez, Katie Auchettl, Simon Goode,, Timothy M. C. Abbott, Igor Andreoni, Rebecca Allen, Natasha Van Bemmel and, Sara Webb

TL;DR
This study searches for gamma-ray burst orphan afterglows using high-cadence, deep optical data, introducing a machine learning method to identify candidates and setting upper limits on their occurrence rate and jet structure parameters.
Contribution
It presents a novel fast-cadence search method with machine learning for detecting orphan afterglows in archival data, constraining their rates and jet geometries.
Findings
Recovered 51 OA candidates, mostly stellar flares.
Nine candidates are likely Galactic stellar events.
Set upper limits on OA rate and jet opening angles.
Abstract
The relativistic outflows that produce Long GRBs (LGRBs) can be described by a structured jet model where prompt -ray emission is restricted to a narrow region in the jet's core. Viewing the jet off-axis from the core, a population of afterglows without an associated GRB detection can be predicted. In this work, we conduct an archival search for these `orphan' afterglows (OAs) with minute-cadence, deep () data from the Dark Energy Camera (DECam) taken as part of the Deeper, Wider, Faster programme (DWF). We introduce a method to select fast-evolving OA candidates within DWF data that comprises a machine learning model, based on a realistic synthetic population of OAs. Using this classifier, we recover 51 OA candidates. Of these candidates, 42 are likely flare events from M-class stars. The remaining nine possess quiescent, coincident sources in archival data with…
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Taxonomy
TopicsGamma-ray bursts and supernovae
