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

31 Aug 2021, 17:10
Talk in parallel session Dark Matter and its detection Discussion Panel Dark Matter 3


Iván Coarasa (Universidad de Zaragoza)


ANAIS (Annual modulation with NaI(Tl) Scintillators) 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 by 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 (BDT) 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. In this talk, the reanalysis of the three years of ANAIS−112 data taking with this technique will be presented.

Primary author

Iván Coarasa (Universidad de Zaragoza)


Dr. Julio Amare (CAPA-UZ) Susana Cebrian (Universidad de Zaragoza) Mr. David Cintas (CAPA-UZ) Eduardo García Abancéns (Universidad de Zaragoza) Maria Martinez (Universidad de Zaragoza) Miguel Ángel Oliván (Universidad de Zaragoza) Dr. Ysrael ortigoza (CAPA-UZ) Mr. Alfonso Ortiz de Solorzano (CAPA-UZ) Jorge Puimedon (Universidad de Zaragoza) Dr. Ana Salinas (CAPA-UZ) María Luisa Sarsa (University of Zaragoza) Patricia Villar (Universidad de Zaragoza)

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