{"paper":{"title":"Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.data-an","authors_text":"A. Bercellie, A. Ghosh, A. M. Gago, A. M. McGowan, A. Norrick, A. Olivier, B. Messerly, B. Yaeggy, C. J. Solano Salinas, C. M. Marshall, C. Nguyen, D. A. Andrade, D. A. Harris, D. Coplowe, D. Jena, D. Rimal, D. Ruterbories, E. Maher, E. Valencia, F. Akbar, G. A. D\\'iaz, G. F. R. Caceres Vera, G. N. Perdue, H. da Motta, H. Ray, H. Schellman, H. Su, J. Chaves, J. Felix, J. Kleykamp, J. K. Nelson, J. Miller, J. Wolcott, J. Y. Han, K. S. McFarland, L. Bellantoni, L. Fields, L. Ren, M. A. Ram\\'irez, M. Ascencio, M. Betancourt, M. F. Carneiro, M. Kordosky, M. Wospakrik, Nuruzzaman, R. D. Ransome, R. Fine, R. Galindo, R. Gran, R. Patton, S. Upadhyay, S. Young, T. Cai, T. Golan, W. A. Mann, X. G. Lu","submitted_at":"2018-08-24T23:13:51Z","abstract_excerpt":"We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.08332","kind":"arxiv","version":4},"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"}