Machine-learning techniques applied to three-year exposure of ANAIS–112

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.