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We describe how a novel online changepoint detection algorithm, calledPoisson-FOCuS, can be used to optimally detect gamma-ray bursts within thecomputational constraints imposed by miniaturized satellites such as theupcoming HERMES-Pathfinder constellation. Poisson-FOCuS enables testing forgamma-ray burst onset at all intervals in a count time series, across alltimescales and offsets, in real-time and at a fraction of the computationalcost of conventional strategies. We validate an implementation with automaticbackground assessment through exponential smoothing, using archival data fromFermi-GBM. Through simulations of lightcurves modeled after real short and longgamma-ray bursts, we demonstrate that the same implementation has higherdetection power than algorithms designed to emulate the logic of Fermi-GBM andCompton-BATSE, reaching the performances of a brute-force benchmark with oracleinformation on the true background rate, when not hindered by automaticbackground assessment. Finally, using simulated data with different lengths andmeans, we show that Poisson-FOCuS can analyze data twice as fast as a similarlyimplemented benchmark emulator for the historic Fermi-GBM on-board triggeralgorithms.

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