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Developing a Bubble Chamber Particle Discriminator Using Semi-Supervised Learning

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arxiv 1811.11308 v1 pith:OM2P5NTS submitted 2018-11-27 physics.comp-ph physics.ins-det

B. Matusch , C. Amole , M. Ardid , I. J. Arnquist , D. M. Asner , D. Baxter , E. Behnke , M. Bressler
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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
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classification physics.comp-phphysics.ins-det
keywords discriminatorlearningbubblechamberdevelopingeventspico-60semi-supervised
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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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