Search for Orphan Gamma-Ray Burst Afterglows with the Vera C. Rubin Observatory and the alert broker Fink

TL;DR

This paper develops a machine learning-based method to identify orphan gamma-ray burst afterglows in Rubin LSST data, aiding in understanding GRB physics and multi-messenger astronomy.

Contribution

It introduces a novel approach combining simulated light curves and machine learning to detect orphan afterglows in LSST data, tested within the Fink alert broker.

Findings

High accuracy in classifying orphan afterglows in simulated data

Effective discrimination of orphan events from variable objects

Validated method with ELAsTiCC and Rubin pseudo-observations

Abstract

Orphan gamma-ray burst afterglows are good candidates to learn more about the GRB physics and progenitors or for the development of multi-messenger analysis with gravitational waves. Our objective is to identify orphan afterglows in Rubin LSST data, by using the characteristic features of their light curves. In this work, we generated a population of short GRBs based on the Swift SBAT4 catalogue, and we simulated their off-axis afterglow light curves with afterglowpy. We then used the rubin_sim package to simulate observations of these orphan afterglows with Rubin LSST and proceeded with the characterisation of orphan light curves by extracting a number of parameters. The same parameters are computed for the ELAsTiCC (Extended LSST Astronomical Time-series Classification Challenge) data set, a simulated alert stream of the Rubin LSST data. We then started to develop a machine learning filter able to discriminate orphan-like events among all the variable objects. We present here the performance of our filter as implemented in the Fink broker and tested on the ELAsTiCC data set and our own Rubin pseudo-observation simulations.