HERMES Pathfinder is an in-orbit demonstration consisting of a constellation of six 3U nano-satellites hosting simple but innovative detectors for the monitoring of cosmic high-energy transients. The main objective of HERMES Pathfinder is to prove that accurate position of high-energy cosmic transients can be obtained using miniaturized hardware. The transient position is obtained by studying the delay time of arrival of the signal to different detectors hosted by nano-satellites on low-Earth orbits. In this context, we need to develop novel tools to fully exploit the future scientific data output of HERMES Pathfinder. In this paper, we introduce a new framework to assess the background count rate of a spaceborne, high energy detector; a key step towards the identification of faint astrophysical transients. We employ a neural network to estimate the background lightcurves on different timescales. Subsequently, we employ a fast change-point and anomaly detection technique called Poisson-FOCuS to identify observation segments where statistically significant excesses in the observed count rate relative to the background estimate exist. We test the new software on archival data from the NASA Fermi Gamma-ray Burst Monitor (GBM), which has a collecting area and background level of the same order of magnitude to those of HERMES Pathfinder. The neural network performances are discussed and analyzed over period of both high and low solar activity. We were able to confirm events in the Fermi-GBM catalog, both solar flares and gamma-ray bursts, and found events, not present in Fermi-GBM database, that could be attributed to solar flares, terrestrial gamma-ray flashes, gamma-ray bursts and galactic X-ray flashes. Seven of these are selected and further analyzed, providing an estimate of localisation and a tentative classification.
