Real-time Likelihood Methods for Improved Gamma-ray Transient Detection and Localization

TL;DR

This paper introduces a real-time maximum likelihood algorithm for gamma-ray transient detection that significantly improves sensitivity and localization accuracy over existing methods, enabling earlier and more precise identification of gamma-ray bursts.

Contribution

The paper presents a fast, sensitive ML algorithm for real-time gamma-ray transient detection and localization, validated with simulations and archival data, outperforming traditional excess count methods.

Findings

ML method is nearly twice as sensitive as excess count algorithms.

Detected 240-280% more short gamma-ray bursts in simulations.

Would have detected GRB 170817A even if it was four times fainter.

Abstract

We present a maximum likelihood (ML) algorithm that is fast enough to detect gamma-ray transients in real time on low-performance processors often used for space applications. We validate the routine with simulations and find that, relative to algorithms based on excess counts, the ML method is nearly twice as sensitive, allowing detection of 240-280% more short gamma-ray bursts. We characterize a reference implementation of the code, estimating its computational complexity and benchmarking it on a range of processors. We exercise the reference implementation on archival data from the Fermi Gamma-ray Burst Monitor (GBM), verifying the sensitivity improvements. In particular, we show that the ML algorithm would have detected GRB 170817A even if it had been nearly four times fainter. We present an ad hoc but effective scheme for discriminating transients associated with background variations. We show that the on-board localizations generated by ML are accurate, but that refined off-line localizations require a detector response matrix with about ten times finer resolution than is current practice. Increasing the resolution of the GBM response matrix could substantially reduce the few-degree systematic uncertainty observed in the localizations of bright bursts.