{"paper":{"title":"Developing a Bubble Chamber Particle Discriminator Using Semi-Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.ins-det"],"primary_cat":"physics.comp-ph","authors_text":"A. E. Robinson, A. J. Noble, A. Leblanc, A. Ortega, A. Plante, A. Sonnenschein, B. Broerman, B. Loer, B. Matusch, C. Amole, C. B. Coutu, C. B. Krauss, C. Cowles, C. E. Dahl, C. Hardy, C. J. Chen, C. Licciardi, C. M. Jackson, C. Moore, D. Baxter, D. M. Asner, E. Behnke, E. V\\'azquez-J\\'auregui, E. Weima, E. W. Hoppe, F. Girard, F. Mamedov, F. Tardif, G. Cao, G. Crowder, G. Giroux, I. Felis, I. J. Arnquist, I. Lawson, I. Levine, I. \\v{S}tekl, J. Farine, J. Hall, J. I. Collar, J. Zhang, K. Clark, K. Wierman, L. Klopfenstein, M. Ardid, M. Bressler, M.-C. Piro, M. Crisler, M. Das, M. Jin, M. Laurin, N. A. Cruz-Venegas, N. Starinski, N. Walkowski, O. Harris, O. Scallon, P. Mitra, P. Oedekerk, P. S. Cooper, R. Filgas, R. Neilson, R. Podviyanuk, S. Fallows, S. Priya, S. Sahoo, S. Seth, T. Hillier, T. Nania, T. Sullivan, U. Chowdhury, U. Wichoski, V. Zacek, W. H. Lippincott, Y. Yan","submitted_at":"2018-11-27T23:18:29Z","abstract_excerpt":"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 foun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.11308","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}