The General Antiparticle Spectrometer (GAPS) is a balloon-borne experiment, scheduled for a first flight in the austral summer 2022. It is designed to measure low energy (< 0.25 GeV/n) cosmic antinuclei. A particular focus is on antideuterons, which are predicted to have an ultra-low astrophysical background as compared to signals from dark matter annihilation or decay in the Galactic halo. GAPS uses a novel technique for particle identification based on the formation and decay of exotic atoms. To achieve sufficient rejection power for particle identification, an accurate determination of several fundamental quantities is needed. The precise reconstruction of the energy deposition pattern on the primary track is a particularly intricate problem and we developed a strategy devised to solve this using modern machine learning techniques. In the future, this approach can also be used for particle identification. Here, we present preliminary results of these efforts obtained from simulations.

(2022). Neural Networks approach to event reconstruction for the GAPS experiment . In POS PROCEEDINGS OF SCIENCE. Retrieved from https://hdl.handle.net/10446/235013

Neural Networks approach to event reconstruction for the GAPS experiment

Manghisoni, M.;Re, V.;Riceputi, E.;
2022-01-01

Abstract

The General Antiparticle Spectrometer (GAPS) is a balloon-borne experiment, scheduled for a first flight in the austral summer 2022. It is designed to measure low energy (< 0.25 GeV/n) cosmic antinuclei. A particular focus is on antideuterons, which are predicted to have an ultra-low astrophysical background as compared to signals from dark matter annihilation or decay in the Galactic halo. GAPS uses a novel technique for particle identification based on the formation and decay of exotic atoms. To achieve sufficient rejection power for particle identification, an accurate determination of several fundamental quantities is needed. The precise reconstruction of the energy deposition pattern on the primary track is a particularly intricate problem and we developed a strategy devised to solve this using modern machine learning techniques. In the future, this approach can also be used for particle identification. Here, we present preliminary results of these efforts obtained from simulations.
Marcelli, N.; Aramaki, T.; Bird, R.; Boezio, M.; Boggs, S. E.; Bonvicini, V.; Campana, D.; Craig, W. W.; Everson, E.; Fabris, L.; Fuke, H.; Gahbauer, F.; Garcia, I.; Gerrity, C.; Hailey, C. J.; Hayashi, T.; Kato, C.; Kawachi, A.; Kobayashi, S.; Kozai, M.; Lenni, A.; Lowell, A.; Manghisoni, Massimo; Marcelli, N.; Mochizuki, B.; Mognet, S. A. I.; Munakata, K.; Munini, R.; Nakagami, Y.; Olson, J.; Ong, R. A.; Osteria, G.; Perez, K.; Quinn, S.; Re, Valerio; Riceputi, Elisa; Roach, B.; Rogers, F.; Ryan, J. A.; Saffold, N.; Scotti, V.; Shimizu, Y.; Sonzogni, M.; Sparvoli, R.; Stoessl, A.; Tiberio, A.; Vannuccini, E.; von Doetinchem, P.; Wada, T.; Xiao, M.; Yamatami, M.; Yoshida, A.; Yoshida, T.; Zampa, G.; Zweerink, J.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/235013
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