  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.
