Showing papers by "A. J. Noble published in 2018"

First results from the DEAP-3600 dark matter search with argon at SNOLAB

Abstract: This paper reports the first results of a direct dark matter search with the DEAP-3600 single-phase liquid argon (LAr) detector. The experiment was performed 2 km underground at SNOLAB (Sudbury, Canada) utilizing a large target mass, with the LAr target contained in a spherical acrylic vessel of 3600 kg capacity. The LAr is viewed by an array of PMTs, which would register scintillation light produced by rare nuclear recoil signals induced by dark matter particle scattering. An analysis of 4.44 live days (fidicial exposure of 9.87 tonne days) of data taken during the initial filling phase demonstrates the best electronic recoil rejection using pulse-shape discrimination in argon, with leakage <1.2X107 (90% C.L.) between 15 and 31 keVee. No candidate signal events are observed, which results in the leading limit on WIMP-nucleon spin-independent cross section on argon, <1.21044 cm2 for a 100 GeV/c2 WIMP mass (90% C.L.).

Developing a Bubble Chamber Particle Discriminator Using Semi-Supervised Learning

Abstract: The identification of non-signal events is a major hurdle to overcome for bubble chamber dark matter experiments such as PICO-60. The current practice of manually developing a discriminator function to eliminate background events is difficult when available calibration data is frequently impure and present only in small quantities. In this study, several different discriminator input/preprocessing formats and neural network architectures are applied to the task. First, they are optimized in a supervised learning context. Next, two novel semi-supervised learning algorithms are trained, and found to replicate the Acoustic Parameter (AP) discriminator previously used in PICO-60 with a mean of 97% accuracy.