Machine-learning techniques applied to three-year exposure of ANAIS-112
Abstract
ANAIS is a direct dark matter detection experiment aiming at the confirmation or refutation of the DAMA/LIBRA positive annual modulation signal in the low energy detection rate, using the same target and technique. ANAIS-112, located at the Canfranc Underground Laboratory in Spain, is operating an array of 3×3 ultrapure NaI(Tl) crystals with a total mass of 112.5 kg since August 2017. The trigger rate in the region of interest (1-6 keV) is dominated by non-bulk scintillation events. In order to discriminate these noise events from bulk scintillation events, robust filtering protocols have been developed. Although this filtering procedure works very well above 2 keV, the measured rate from 1 to 2 keV is about 50% higher than expected according to our background model, and we cannot discard non-bulk scintillation events as responsible of that excess. In order to improve the rejection of noise events, a Boosted Decision Tree has been developed and applied. With this new PMT-related noise rejection algorithm, the ANAIS-112 background between 1 and 2 keV is reduced by almost 30%, leading to an increase in sensitivity to the annual modulation signal. The reanalysis of the three years of ANAIS-112 data with this technique is also presented.
- Publication:
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Journal of Physics Conference Series
- Pub Date:
- December 2021
- DOI:
- 10.1088/1742-6596/2156/1/012036
- arXiv:
- arXiv:2110.10649
- Bibcode:
- 2021JPhCS2156a2036C
- Keywords:
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- Astrophysics - Instrumentation and Methods for Astrophysics;
- High Energy Physics - Experiment
- E-Print:
- Contributed to the TAUP2021 Conference, August 2021. To be published in Journal of Physics: Conference Series