1. Machine-learning techniques applied to three-year exposure of ANAIS-112
- Author
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Coarasa, I., Apilluelo, J., Amaré, J., Cebrián, S., Cintas, D., García, E., Martínez, M., Oliván, M. A., Ortigoza, Y., Ortiz de Solórzano, A., Puimedón, J., Salinas, A., Sarsa, M. L., and Villar, P.
- Subjects
History ,High Energy Physics - Experiment (hep-ex) ,FOS: Physical sciences ,Astrophysics - Instrumentation and Methods for Astrophysics ,Instrumentation and Methods for Astrophysics (astro-ph.IM) ,Computer Science Applications ,Education ,High Energy Physics - Experiment - 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., Comment: Contributed to the TAUP2021 Conference, August 2021. To be published in Journal of Physics: Conference Series
- Published
- 2021
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