Particle identification using semi-supervised learning in the PICO-60 dark matter detector

Abstract

Astrophysical observations have demonstrated the existence of dark matter over the past decades. Experimental efforts in the search for dark matter are largely focused on the well-motivated weakly interacting massive particles (WIMPs) as a dark matter candidate. Current experiments in direct detection are producing increasingly competitive limits on the cross section of WIMP-nucleon scattering. The main experimental challenge for all direct detection experiments is the presence of background signals. These backgrounds need to be either eliminated by providing sufficient shielding or discriminated from WIMP signals. In this work, semi-supervised learning techniques are developed to discriminate alpha recoils from nuclear recoils induced by WIMPs in the PICO-60 detector. The two semi-supervised learning techniques, gravitational differentiation and iterative cluster nucleation, maximize the effect of the most confidently predicted data samples on subsequent training iterations. Classifications using both techniques can reproduce the traditional acoustic parameter with accuracies over 98%. The best model yields an accuracy of 99.2% and a class-wise standard deviation value of 0.11. These techniques can reliably serve as an intermediate verification tool before the acoustic parameter is constructed in future detectors.