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SUMMARY:Machine-learning techniques applied to three-year exposure of ANAI
 S−112
DTSTART;VALUE=DATE-TIME:20210831T154500Z
DTEND;VALUE=DATE-TIME:20210831T160000Z
DTSTAMP;VALUE=DATE-TIME:20260511T001340Z
UID:indico-contribution-15858@indico.ific.uv.es
DESCRIPTION:Speakers: Iván Coarasa (Universidad de Zaragoza)\nANAIS (Annu
 al modulation with NaI(Tl) Scintillators) is a direct dark matter detectio
 n experiment aiming at the confirmation or refutation of the DAMA/LIBRA po
 sitive annual modulation signal in the low energy detection rate\, using t
 he same target and technique. ANAIS−112\, located at the Canfranc Underg
 round 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 scintill
 ation events. In order to discriminate these noise events from bulk scinti
 llation events\, robust filtering protocols have been developed. Although 
 this filtering procedure works very well above 2 keV\, the measured rate f
 rom 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 tha
 t excess. In order to improve the rejection of noise events\, a Boosted De
 cision Tree (BDT) has been developed and applied. With this new PMT-relate
 d noise rejection algorithm\, the ANAIS−112 background between 1 and 2 k
 eV is reduced by almost 30%\, leading to an increase in sensitivity to the
  annual modulation signal. In this talk\, the reanalysis of the three year
 s of ANAIS−112 data taking with this technique will be presented.\n\nhtt
 ps://indico.ific.uv.es/event/6178/contributions/15858/
LOCATION:
URL:https://indico.ific.uv.es/event/6178/contributions/15858/
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