Developing a Bubble Chamber Particle Discriminator Using Semi-Supervised Learning

Understand

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.