3 years ago

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

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

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.

Publisher URL: http://arxiv.org/abs/1808.08332

DOI: arXiv:1808.08332v3

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.