## 1 Introduction  
The rapid identification of various transient types, such as Gamma-Ray Bursts (GRBs), is crucial, especially when considering multiple observations related to multi-wavelength event analysis. For instance, it plays a key role in alerting ground telescopes for pointing direction and subsequent afterglow observations, facilitating follow-up studies. GRBs are distinguished by increases in detector count rates, indicating activity that appears unusual and cannot be explained by background or known sources. Real-time modeling of the physical background is inherently difficult, so algorithms dedicated to \"online\" GRB detection can extrapolate the background from recent observations, such as from the onboard detector systems Fermi GBM [1], Compton-BATSE [2] and BeppoSAX-GRBM [3].  
In \"offline\" analysis, researchers search archival data for GRB events that may have gone unnoticed. Examples of this methodology can be found in studies such as [PERSON] et al. (1999) [4], which uses the BATSE catalog, or in [PERSON] et al. (2018) [5] and [PERSON] et al. (2017) [6], where the search focuses on faint, short GRBs that occur concurrently with known gravitational wave events.  
[PERSON] et al. (2020) [7] introduce a novel approach that provides comprehensive estimation by incorporating detailed models catching various background factors anticipated for the Gamma-Ray Burst Monitor (GBM). These factors encompass detector response, cosmic-ray background, solar activity, geomagnetic environment, Earth albedo and the visibility of X and gamma-ray point sources. The resulting background estimation closely replicates Fermi GBM observations, particularly the long temporal variations (e.g., 10 min), suggesting potential applicability in detecting challenging GRBs, such as long weak eventswith slow rise times. However, because this technique is specifically designed for Fermi GBM observations, its application to other experiments is limited.  
The use of deep learning approaches provides a promising solution for improving the accuracy and efficiency of detection of GRBs or other X/gamma-ray astrophysical transients.  
[PERSON] (2019) [8] introduces an alternative method for background prediction based on a recurrent neural network (RNN). This RNN, which was trained on existing burst observation catalogs to identify GRB events, raises concerns about the potential transfer of detection biases from traditional GRB detection strategies. This disadvantage may result in overlooking events that previous search methodologies had missed.  
In a related context, [PERSON] et al. [9; 10] describes a feedforward neural network (FFNN) for estimating X/gamma-ray backgrounds for space telescope detectors. This method, which is accompanied by an efficient changepoint detection technique [11; 12], takes advantage of all available satellite information (variables), as employed in [7]. Importantly, because the learning is data-driven, it can be applied to any X/gamma-ray photon detector on a space satellite and does not require event exclusion during training. The following section describes two experiments designed to test the robustness of this method, identify its limitations and improve the interpretation of its output, taking into account the neural network inherent \"black box\" nature.  
The neural network was tested using Fermi GBM data, but the problem formulation is intended to be adaptable to other satellites, including upcoming ventures such as the technological and scientific pathfinders HERMES-TP (funded by ASI) and HERMES-SP [13; 14; 15; 16; 17] (funded by the European Commission, collectively known as HERMES Pathfinder). The primary goal of HERMES Pathfinder is to demonstrate the feasibility of detecting and localizing GRBs using miniaturized instrumentation hosted by nanosatellites. The first six HERMES Pathfinder spacecraft are set to launch into low-Earth, near-equatorial orbits in 2024/2025.