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ANAIS is a direct dark matter detection experiment aiming at the confirmationor refutation of the DAMA/LIBRA positive annual modulation signal in the lowenergy detection rate, using the same target and technique. ANAIS-112, locatedat the Canfranc Underground Laboratory in Spain, is operating an array of3$\times$3 ultrapure NaI(Tl) crystals with a total mass of 112.5 kg sinceAugust 2017. The trigger rate in the region of interest (1-6 keV) is dominatedby non-bulk scintillation events. In order to discriminate these noise eventsfrom bulk scintillation events, robust filtering protocols have been developed.Although this filtering procedure works very well above 2 keV, the measuredrate from 1 to 2 keV is about 50% higher than expected according to ourbackground model, and we cannot discard non-bulk scintillation events asresponsible of that excess. In order to improve the rejection of noise events,a Boosted Decision Tree has been developed and applied. With this newPMT-related noise rejection algorithm, the ANAIS-112 background between 1 and 2keV is reduced by almost 30%, leading to an increase in sensitivity to theannual modulation signal. The reanalysis of the three years of ANAIS-112 datawith this technique is also presented.

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