Abstract
We investigate the potential of using deep learning techniques to identify possible background events in search of Dark Matter with directional time projection chamber. The difference in the topological features can be learned by the deep learning models by training over thousand of events. These networks can be further used for the discrimination of background and signal events. The networks trained in this study performs better than the conventional approach (applying selection on variable) used for the classification of backgrounds and signal events. There is potential to further improve the performance of these models to improve the background rejection capabilities.
CYGNO Collaboration (F. D. Amaro, L. Benussi, S. Bianco, C. Capoccia, M. Caponero, D. S. Cardoso, G. Cavoto, A. Cortez, I. A. Costa, G. D’Imperio, E. Dané, G. Dho, F. Di Giambattista, E. Di Marco, F. Iacoangeli, H. P. Lima Junior, G. S. P. Lopes, G. Maccarrone, R. D. P. Mano , R. R. Marcelo Gregorio, D. J. G. Marques, G. Mazzitelli, A. G. McLean, A. Messina, C. M. B. Monteiro, R. A. Nobrega, I. F. Pains, E. Paoletti, L. Passamonti, S. Pelosi, F. Petrucci, S. Piacentini, D. Piccolo, D. Pierluigi, D. Pinci, F. Renga, R. J. D. C. Roque, F. Rosatelli, A. Russo, G. Saviano, N. J. C. Spooner, R. Tesauro, S. Tomassini, S. Torelli, and J. M. F. dos Santos)
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References
Bertone, G., Hooper, D., Silk, J., Particle dark matter: evidence, candidates and constraints. Phys. Rep. 405(5–6), 279–390 (2005). https://doi.org/10.1016/j.physrep.2004.08.031
Marafini, M., et al.: ORANGE: a high sensitivity particle tracker based on optically read out GEM. Nucl. Instrum. Meth. A 845, 285–288 (2017). https://doi.org/10.1016/j.nima.2016.04.014
Amaro, F. D., Baracchini, E., et al.: The CYGNO experiment, MPDI (2022). https://doi.org/10.3390/instruments6010006
Baracchini, E., et al.: Identification of low energy nuclear recoils in a gas time projection chamber with optical readout. Meas. Sci. Technol 32, 025902 (2020)
Baracchini, E., et al.: A density-based clustering algorithm for the CYGNO data analysis. J. Instrum. 15. (2020). https://doi.org/10.1088/1748-0221/15/12/T12003
Ghrear, M., Vahsen, S.E. & Deaconu, C.: Observables for recoil identification in high-definition gas time projection chambers. J. Cosmol. Astropart. Phys. (2020) https://doi.org/10.1088/1475-7516/2021/10/005
Acknowledgements
This project has received fundings under the European Union’s Horizon 2020 research and innovation programme from the European Research Council (ERC) grant agreement No 818744.
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Prajapati, A., Baracchini, E. (2023). Reconstruction and Particle Identification with CYGNO Experiment. In: Bufano, F., Riggi, S., Sciacca, E., Schilliro, F. (eds) Machine Learning for Astrophysics. ML4Astro 2022. Astrophysics and Space Science Proceedings, vol 60. Springer, Cham. https://doi.org/10.1007/978-3-031-34167-0_10
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