Cosmic antihelium-3 nuclei sensitivity of the GAPS experiment
N. Saffold, T. Aramaki, R. Bird, M. Boezio, S. E. Boggs, V. Bonvicini,, D. Campana, W. W. Craig, P. von Doetinchem, E. Everson, L. Fabris, H. Fuke,, F. Gahbauer, I. Garcia, C. Gerrity, C. J. Hailey, T. Hayashi, C. Kato, A., Kawachi, S. Kobayashi, M. Kozai, A. Lenni, A. Lowell

TL;DR
The GAPS experiment aims to detect low-energy antihelium-3 nuclei in cosmic rays, providing unprecedented sensitivity to these rare particles as potential dark matter signatures, with implications for understanding fundamental physics.
Contribution
This paper presents the first detailed sensitivity analysis of GAPS for antihelium-3 detection, based on comprehensive simulations and realistic atmospheric models.
Findings
GAPS can detect antihelium-3 fluxes as low as 1.3e-6 m^{-2} sr^{-1} s^{-1} in its energy range.
GAPS's detection technique offers higher identification power for low-energy antinuclei than previous experiments.
The experiment can set new upper limits or potentially detect antihelium-3 in cosmic rays.
Abstract
The General Antiparticle Spectrometer (GAPS) is an Antarctic balloon experiment designed for low-energy (0.10.3 GeV/) cosmic antinuclei as signatures of dark matter annihilation or decay. GAPS is optimized to detect low-energy antideuterons, as well as to provide unprecedented sensitivity to low-energy antiprotons and antihelium nuclei. The novel GAPS antiparticle detection technique, based on the formation, decay, and annihilation of exotic atoms, provides greater identification power for these low-energy antinuclei than previous magnetic spectrometer experiments. This work reports the sensitivity of GAPS to detect antihelium-3 nuclei, based on full instrument simulation, event reconstruction, and realistic atmospheric influence simulations. The report of antihelium nuclei candidate events by AMS-02 has generated considerable interest in antihelium nuclei as probes of dark matter…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
