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.

翻译：HERMES（高能快速模块化卫星星座）探路者是轨道演示项目，由六颗携带简单但创新探测器的3U纳米卫星组成的星座构成，用于监测宇宙高能暂现源。HERMES探路者的主要目标是证明利用微型化硬件可实现对高能宇宙暂现源的精确定位。暂现源的位置是通过研究信号到达近地轨道纳米卫星上不同探测器的延迟时间获得的。为此，目标是达到微秒量级的总体精度。在此背景下，我们需要开发新型工具以充分利用HERMES探路者未来的科学数据产出。本文提出了一种新框架来评估空间高能探测器的背景计数率——这是识别微弱天体物理暂现源的关键步骤。我们采用神经网络（NN）估计不同时间尺度上的背景光变曲线，随后利用快速变点与异常检测技术，分离出观测段中计数率相对于背景估计存在统计显著超额的片段。我们在NASA费米伽马射线暴监测器（GBM）的存档数据上测试了该新软件，其收集面积和背景水平与HERMES探路者处于同一量级。分析了神经网络在太阳活动高/低周期内的性能表现。我们成功确认了费米/GBM星表中的事件，并发现未收录于费米/GBM数据库但可能归因于太阳耀斑、地球伽马射线闪、伽马射线暴及银河系X射线闪的事件。其中七个事件被挑选出来进行进一步分析，提供了定位估计和初步分类。