arXiv · 1811.11308
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.
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B. Matusch, C. Amole, M. Ardid, I. J. Arnquist, D. M. Asner, D. Baxter, E. Behnke, M. Bressler, B. Broerman, G. Cao, C. J. Chen, U. Chowdhury, K. Clark, J. I. Collar, P. S. Cooper, C. B. Coutu, C. Cowles, M. Crisler, G. Crowder, N. A. Cruz-Venegas, C. E. Dahl, M. Das, S. Fallows, J. Farine, I. Felis, R. Filgas, F. Girard, G. Giroux, J. Hall, C. Hardy, O. Harris, T. Hillier, E. W. Hoppe, C. M. Jackson, M. Jin, L. Klopfenstein, C. B. Krauss, M. Laurin, I. Lawson, A. Leblanc, I. Levine, C. Licciardi, W. H. Lippincott, B. Loer, F. Mamedov, P. Mitra, C. Moore, T. Nania, R. Neilson, A. J. Noble, P. Oedekerk, A. Ortega, M. -C. Piro, A. Plante, R. Podviyanuk, S. Priya, A. E. Robinson, S. Sahoo, O. Scallon, S. Seth, A. Sonnenschein, N. Starinski, I. Štekl, T. Sullivan, F. Tardif, E. Vázquez-Jáuregui, N. Walkowski, E. Weima, U. Wichoski, K. Wierman, Y. Yan, V. Zacek, J. Zhang. 2018-11-27. Developing a Bubble Chamber Particle Discriminator Using Semi-Supervised Learning. https://arxiv.org/abs/1811.11308
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