Developing a Bubble Chamber Particle Discriminator Using Semi-Supervised Learning
2018
The identification of non-signal events is a major hurdle to overcome for
bubble chamberdark 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 architecturesare applied to the task. First, they are optimized in a
supervised learningcontext. Next, two novel
semi-supervised learningalgorithms are trained, and found to replicate the Acoustic Parameter (AP) discriminator previously used in PICO-60 with a mean of 97% accuracy.
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