Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

Perdue, G. N.; Ghosh, A.; Wospakrik, M.; Akbar, F.; Andrade, D. A.; Ascencio, M.; Bellantoni, L.; Bercellie, A.; Betancourt, M.; Vera, G. F.  R.  Caceres; Cai, T.; Carneiro, M. F.; Chaves, J.; Coplowe, D.; Motta, H. da; Díaz, G. A.; Felix, J.; Fields, L.; Fine, R.; Gago, A. M.; Galindo, R.; Golan, T.; Gran, R.; Han, J. Y.; Harris, D. A.; Jena, D.; Kleykamp, J.; Kordosky, M.; Lu, X. -G.; Maher, E.; Mann, W. A.; Marshall, C. M.; McFarland, K. S.; McGowan, A. M.; Messerly, B.; Miller, J.; Nelson, J. K.; Nguyen, C.; Norrick, A.; Nuruzzaman, Nuruzzaman; Olivier, A.; Patton, R.; Ramírez, M. A.; Ransome, R. D.; Ray, H.; Ren, L.; Rimal, D.; Ruterbories, D.; Schellman, H.; Salinas, C. J.  Solano; Su, H.; Upadhyay, S.; Valencia, E.; Wolcott, J.; Yaeggy, B.; Young, S.

doi:10.1088/1748-0221/13/11/P11020

Title: Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

Journal Article · Mon Nov 26 00:00:00 EST 2018 · Journal of Instrumentation

DOI:https://doi.org/10.1088/1748-0221/13/11/P11020· OSTI ID:1484978

Perdue, G. N.; Ghosh, A.; Wospakrik, M.; Akbar, F.; Andrade, D. A.; Ascencio, M.; Bellantoni, L.; Bercellie, A.; Betancourt, M.; Vera, G. F. R. Caceres; Cai, T.; Carneiro, M. F.; Chaves, J.; Coplowe, D.; Motta, H. da; Díaz, G. A.; Felix, J.; Fields, L.; Fine, R.; Gago, A. M. more »

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 at Fermilab. A-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. We find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.

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Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)

Sponsoring Organization:: National Science Foundation (NSF); CNPq (Brazil); CoNaCyT (Mexico); IDI/IGI-UNI (Peru); Latin American Center for Physics (CLAF); Russian Ministry of Education and Science (Russia); National Science Centre of Poland; USDOE Office of Science (SC), High Energy Physics (HEP)

Contributing Organization:: MINERvA collaboration; MINERvA Collaboration

Grant/Contract Number:: AC05-00OR22725; AC02-07CH11359

OSTI ID:: 1484978

Alternate ID(s):: OSTI ID: 1487058

Report Number(s):: arXiv:1808.08332; FERMILAB-PUB-18-432-CD-ND

Journal Information:: Journal of Instrumentation, Vol. 13, Issue 11; ISSN 1748-0221

Publisher:: Institute of Physics (IOP)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 8 works

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References (19)

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