## 4 Summary and Conclusions  
The detection of GRB transients with spaceborne photon detectors requires a accurate estimate of background count rates. Our study takes a data-driven approach, using a neural network that adapts to various spaceborne X/gamma-ray photon detectors to produce a reliable estimate of background count rates. Quantile regression played a crucial role in our methodology, offering a nuanced estimation by providing a confidence range for predictions. Notably, the 90 th percentile from the quantile regression emerged as a robust estimate, enhancing the reliability of our count rate predictions. The neural network, designed for multi-output regression, showcased commendable performance across various training periods. We observed that the model achieved stability and optimal performance within a 4-month training period, with no substantial improvement observed beyond this duration. Crucially, our experiments extended beyond the conventional in-time testing. We evaluated the NN performance on out-of-time test sets, simulating scenarios where the model encountered data outside its training period. The results demonstrated a level of generalization that, while not perfect, remained satisfactory, underscoring the model adaptability and reliability. Nonetheless, this emphasizes the importance of retraining the model for each period to achieve better estimates. To enhance our understanding of the NN behavior, we employed Explainable Artificial Intelligence (XAI) techniques, including Morris sensitivity analysis and Kernel SHAP. Figure 9: Kernel SHAP feature importance for a datapoint in a step of the estimated background in Figure 8. Notably, the most influential features are those related to detector visibility, specifically whether the FoV is obscured by the Earth or not. The baseline for this method is established by using 15 datapoints before and after the datapoint to be explained. These datapoints are used for value sampling and perturbation of the datapoint during the explanation process. The global explanation through Morris sensitivity analysis unveiled the significance of various features, including the McIlwain L-parameter, geographical coordinates and detector flags indicating the Earth's Field of View (FoV) occultation. It was observed that the importance of these features could vary between different periods, emphasizing the need for a tailored neural network for each period, as demonstrated in 2014 and 2019. On the other hand, local explainability using Kernel SHAP assisted in understanding and, to some extent, debugging the neural network, leading to data fixes or suggestions, such as incorporating temporal features and investigating the omission of specific variables, to enhance the overall performance of the machine learning model. In conclusion, our developed neural network-based background estimator holds promise for the robust detection of GRBs and other transient phenomena in high-energy space telescope observations. The data-driven background estimator, featuring robust techniques like quantile regression and XAI, contributes to the creation of adaptable models for offline analysis of astronomical data. As we look to the future, XAI could pave the way for enhanced model performance and a deeper understanding of the underlying processes. Conceptualization, R.C., F.F. ; methodology, R.C. ; software, R.C. ; validation, R.C. ; formal analysis, R.C. ; investigation, R.C. ; resources, F.F. ; data curation, R.C. ; writing--original draft preparation, R.C., G.D., G.D.C. and A.V. ; writing--review and editing, R.C., G.D., G.D.C., F.F. and A.V. ; supervision, A.V. ; project administration, R.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.