Gamma-ray burst detection with Poisson-FOCuS and other trigger algorithms

TL;DR

Poisson-FOCuS is a novel online changepoint detection algorithm optimized for real-time gamma-ray burst detection on resource-constrained satellites, outperforming traditional methods in detection power and speed.

Contribution

The paper introduces Poisson-FOCuS, a new algorithm for real-time gamma-ray burst detection that is computationally efficient and more effective than existing methods.

Findings

Poisson-FOCuS detects gamma-ray bursts with higher power than existing algorithms.

The method operates twice as fast as benchmark algorithms on simulated Fermi-GBM data.

Validation with archival and simulated data demonstrates improved detection and speed.

Abstract

We describe how a novel online changepoint detection algorithm, called Poisson-FOCuS, can be used to optimally detect gamma-ray bursts within the computational constraints imposed by miniaturized satellites such as the upcoming HERMES-Pathfinder constellation. Poisson-FOCuS enables testing for gamma-ray burst onset at all intervals in a count time series, across all timescales and offsets, in real-time and at a fraction of the computational cost of conventional strategies. We validate an implementation with automatic background assessment through exponential smoothing, using archival data from Fermi-GBM. Through simulations of lightcurves modeled after real short and long gamma-ray bursts, we demonstrate that the same implementation has higher detection power than algorithms designed to emulate the logic of Fermi-GBM and Compton-BATSE, reaching the performances of a brute-force benchmark with oracle information on the true background rate, when not hindered by automatic background assessment. Finally, using simulated data with different lengths and means, we show that Poisson-FOCuS can analyze data twice as fast as a similarly implemented benchmark emulator for the historic Fermi-GBM on-board trigger algorithms.