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$\times$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.

arXiv commentContributed to the TAUP2021 Conference, August 2021. To be published in Journal of Physics: Conference Series

References in corpus (6)

• Direct Detection of WIMP Dark Matter: Concepts and Status
• Annual modulation results from three-year exposure of ANAIS-112
• Analysis of backgrounds for the ANAIS-112 dark matter experiment
• Performance of ANAIS-112 experiment after the first year of data taking
• ANAIS-112 sensitivity in the search for dark matter annual modulation
• Light yield determination in large sodium iodide detectors applied in the search for dark matter

Cited by in corpus (4)

• Improving ANAIS-112 sensitivity to DAMA/LIBRA signal with machine learning techniques
• Insights into Dark Matter Direct Detection Experiments: Decision Trees versus Deep Learning
• Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming
• Bayesian technique to combine independently-trained Machine-Learning models applied to direct dark matter detection