HERMES (High Energy Rapid Modular Ensemble of Satellites) 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. To this purpose, the goal is to achive an overall accuracy
of a fraction of a micro-second. 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 space-born, high energy detector; a key step towards the identification of
faint astrophysical transients. We employ a Neural Network (NN) to estimate the
background lightcurves on different timescales. Subsequently, we employ a fast
change-point and anomaly detection technique to isolate 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 NN 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 and
found events, not present in Fermi/GBM database, that could be attributed to
Solar Flares, Terrestrial Gamma-ray Flashes, Gamma-Ray Bursts, Galactic X-ray
flash. Seven of these are selected and analyzed further, providing an estimate
of localisation and a tentative classification