Probing unexplored spin-dependent dark matter-proton coupling with few-photoelectron threshold in COSINE-100
W. K. Kim, N. Carlin, J. Y. Cho, S. J. Cho, S. Choi, A. C. Ezeribe, L. E. Fran\c{c}a, R. F. Muhdi, O. Gileva, C. Ha, I. S. Hahn, E. J. Jeon, H. W. Joo, W. G. Kang, M. Kauer, B. H. Kim, D. Y. Kim, H. J. Kim, J. Kim, K. W. Kim, S. H. Kim, S. K. Kim, Y. D. Kim, Y. H. Kim, B. R. Ko

TL;DR
This paper presents new, more sensitive constraints on low-mass spin-dependent dark matter-proton interactions using a novel low-threshold detection method in the COSINE-100 experiment, including the first search in the few-photoelectron regime.
Contribution
It introduces a specialized event selection and noise mitigation technique enabling detection thresholds of 3-4 photoelectrons, extending sensitivity to sub-GeV dark matter masses.
Findings
Established the most stringent limits for 1.75-2.25 GeV/c$^2$ dark matter.
Extended sensitivity to 15-58 MeV/c$^2$ dark matter using the Migdal effect.
No significant annual modulation signal was observed.
Abstract
We report new constraints on the spin-dependent scattering cross section between low-mass dark matter and protons using data collected by the COSINE-100 experiment. By implementing a specialized event selection process using a multi-layer perceptron and robust noise mitigation, this analysis pioneers a detection threshold of 3 and 4 isolated peaks, corresponding to the reconstructed photoelectrons, which is significantly lower than the 8 photoelectron threshold used in previous analyses. In this unstudied few-photoelectron regime, where PMT-induced noise and phosphorescence are prevalent, we utilize a phenomenological background model to search for the annual modulation signal expected from the Standard Halo Model. No statistically significant annual modulation is observed in our data. We derive new 90% confidence level upper limits for the spin-dependent DM-proton cross section,…
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Taxonomy
TopicsDark Matter and Cosmic Phenomena · Particle physics theoretical and experimental studies · Computational Physics and Python Applications
