## 7 Discussion and Conclusions
Future work could focus on further refining these parameters and exploring their potential interactions with other features to uncover additional performance gains. It is worth noting that in this study, the temporal parameters were tested only with the Random Forest model. However, our future work aims to investigate the application of more advanced ensemble learning models, such as those presented in [4], to the temporal parameters. This will allow us to explore the potential benefits of combining advanced machine learning techniques with the temporal parameters, building on the promising results already obtained with morphological and stereo parameters. In addition to exploring more advanced ensemble learning models, we plan to investigate deep learning approaches for gamma/hadron discrimination using temporal parameters. While Random Forests provide interpretable results and good performance with manageable computational costs, deep neural networks--particularly convolutional or recurrent architectures--might capture more complex temporal patterns in the shower development. Such networks could potentially extract hierarchical features directly from the raw time and intensity data without requiring manual feature engineering. However, this would need to be balanced against the increased computational demands and reduced interpretability inherent to deep learning models. A comparative study between our Random Forest approach and various deep learning architectures would provide valuable insights for the IACT community. Conceptualization, V.L.P., G.C., S.L., A.L.B., A.T. and A.P. ; Methodology, V.L.P., G.C., S.L., A.A.C., A.L.B., A.T. and A.P. ; Software, V.L.P., S.L., A.A.C., A.T. and A.P. ; Validation, V.L.P., S.L., A.A.C., A.T. and A.P. ; Formal analysis, V.L.P., S.L. and A.P. ; Investigation, V.L.P., S.L. and A.P. ; Resources, V.L.P. ; Data curation, V.L.P., S.L. and A.P. ; Writing--original draft, V.L.P., S.L. and A.P. ; Writing--review & editing, V.L.P., S.L. and A.P. ; Visualization, V.L.P. and A.P. ; Supervision, V.L.P., S.L., A.L.B. and A.P. ; Funding acquisition, V.L.P., S.L. and A.P. All authors have read and agreed to the published version of the manuscript. This work is partially supported by ICSC--Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, funded by European Union--NextGenerationEU. We acknowledge financial support from the ASI-INAF agreement n. 2022-14-HH0. Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ASTRI experiment simulation data policy. This work was conducted in the context of the ASTRI Project. We gratefully acknowledge support from the people, agencies, and organisations listed here: [[http://www.astri.inaf.it/en/library/](http://www.astri.inaf.it/en/library/)]([http://www.astri.inaf.it/en/library/](http://www.astri.inaf.it/en/library/)) accessed on 1 March 2025. This work is partially supported by ICSC--Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, funded by European Union--NextGenerationEU. We acknowledge financial support from the ASI-INAF agreement n. 2022-14-HH.0. This paper went through the internal ASTRI review process. The authors declare no conflicts of interest.