Search Menu
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information
Search Scholarly Publications

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

Abstract

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 physicsmore » models used for training.« less

Authors:
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; « less
Publication Date:
Research Org.:
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 Org.:
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 Org.:
MINERvA collaboration; MINERvA Collaboration
OSTI Identifier:
1484978
Alternate Identifier(s):
OSTI ID: 1487058
Report Number(s):
arXiv:1808.08332; FERMILAB-PUB-18-432-CD-ND
Journal ID: ISSN 1748-0221
Grant/Contract Number:  
AC05-00OR22725; AC02-07CH11359
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Instrumentation
Additional Journal Information:
Journal Volume: 13; Journal Issue: 11; Journal ID: ISSN 1748-0221
Publisher:
Institute of Physics (IOP)
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; 97 MATHEMATICS AND COMPUTING; Analysis and statistical methods; Pattern recognition, cluster finding, calibration and fitting methods; Neutrino detectors

Citation Formats

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., and Young, S. Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment. United States: N. p., 2018. Web. doi:10.1088/1748-0221/13/11/P11020.
Copy to clipboard
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. Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment. United States. https://doi.org/10.1088/1748-0221/13/11/P11020
Copy to clipboard
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., and Young, S. Mon . "Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment". United States. https://doi.org/10.1088/1748-0221/13/11/P11020. https://www.osti.gov/servlets/purl/1484978.
Copy to clipboard
@article{osti_1484978,
title = {Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment},
author = {Perdue, G. N. and Ghosh, A. and Wospakrik, M. and Akbar, F. and Andrade, D. A. and Ascencio, M. and Bellantoni, L. and Bercellie, A. and Betancourt, M. and Vera, G. F. R. Caceres and Cai, T. and Carneiro, M. F. and Chaves, J. and Coplowe, D. and Motta, H. da and Díaz, G. A. and Felix, J. and Fields, L. and Fine, R. and Gago, A. M. and Galindo, R. and Golan, T. and Gran, R. and Han, J. Y. and Harris, D. A. and Jena, D. and Kleykamp, J. and Kordosky, M. and Lu, X. -G. and Maher, E. and Mann, W. A. and Marshall, C. M. and McFarland, K. S. and McGowan, A. M. and Messerly, B. and Miller, J. and Nelson, J. K. and Nguyen, C. and Norrick, A. and Nuruzzaman, Nuruzzaman and Olivier, A. and Patton, R. and Ramírez, M. A. and Ransome, R. D. and Ray, H. and Ren, L. and Rimal, D. and Ruterbories, D. and Schellman, H. and Salinas, C. J. Solano and Su, H. and Upadhyay, S. and Valencia, E. and Wolcott, J. and Yaeggy, B. and Young, S.},
abstractNote = {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.},
doi = {10.1088/1748-0221/13/11/P11020},
journal = {Journal of Instrumentation},
number = 11,
volume = 13,
place = {United States},
year = {Mon Nov 26 00:00:00 EST 2018},
month = {Mon Nov 26 00:00:00 EST 2018}
}
Copy to clipboard
Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1088/1748-0221/13/11/P11020
Copyright Statement
Other availability
Search WorldCat Search WorldCat to find libraries that may hold this journal
Citation Metrics:
Cited by: 8 works
Citation information provided by
Web of Science
Save / Share:
Export Metadata
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

Works referenced in this record:

The NuMI neutrino beam
journal, January 2016

  • Adamson, P.; Anderson, K.; Andrews, M.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 806
  • DOI: 10.1016/j.nima.2015.08.063

Gradient-based learning applied to document recognition
journal, January 1998

  • Lecun, Y.; Bottou, L.; Bengio, Y.
  • Proceedings of the IEEE, Vol. 86, Issue 11
  • DOI: 10.1109/5.726791

ROOT — An object oriented data analysis framework
journal, April 1997

  • Brun, Rene; Rademakers, Fons
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 389, Issue 1-2
  • DOI: 10.1016/S0168-9002(97)00048-X

CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
conference, June 2014

  • Razavian, Ali Sharif; Azizpour, Hossein; Sullivan, Josephine
  • 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • DOI: 10.1109/CVPRW.2014.131

Deep Learning and Its Application to LHC Physics
journal, October 2018

A convolutional neural network neutrino event classifier
journal, September 2016

Learning representations by back-propagating errors
journal, October 1986

  • Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J.
  • Nature, Vol. 323, Issue 6088
  • DOI: 10.1038/323533a0

Vertex reconstruction of neutrino interactions using deep learning
conference, May 2017

  • Terwilliger, Adam M.; Perdue, Gabriel N.; Isele, David
  • 2017 International Joint Conference on Neural Networks (IJCNN)
  • DOI: 10.1109/IJCNN.2017.7966131

Caffe: Convolutional Architecture for Fast Feature Embedding
conference, January 2014

  • Jia, Yangqing; Shelhamer, Evan; Donahue, Jeff
  • Proceedings of the ACM International Conference on Multimedia - MM '14
  • DOI: 10.1145/2647868.2654889

Neutrino-Nucleus Interactions
journal, November 2011

Design, calibration, and performance of the MINERvA detector
journal, April 2014

  • Aliaga, L.; Bagby, L.; Baldin, B.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 743
  • DOI: 10.1016/j.nima.2013.12.053

The GENIE neutrino Monte Carlo generator
journal, February 2010

  • Andreopoulos, C.; Bell, A.; Bhattacharya, D.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 614, Issue 1
  • DOI: 10.1016/j.nima.2009.12.009

Measurement of Ratios of ν μ Charged-Current Cross Sections on C, Fe, and Pb to CH at Neutrino Energies 2–20 GeV
journal, June 2014

Measurement of partonic nuclear effects in deep-inelastic neutrino scattering using MINERvA
journal, April 2016

Geant4—a simulation toolkit
journal, July 2003

  • Agostinelli, S.; Allison, J.; Amako, K.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 506, Issue 3
  • DOI: 10.1016/S0168-9002(03)01368-8

MINERvA neutrino detector response measured with test beam data
journal, July 2015

  • Aliaga, L.; Altinok, O.; Araujo Del Castillo, C.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 789
  • DOI: 10.1016/j.nima.2015.04.003

A New Approach to Linear Filtering and Prediction Problems
journal, March 1960

  • Kalman, R. E.
  • Journal of Basic Engineering, Vol. 82, Issue 1
  • DOI: 10.1115/1.3662552

Neutrino flux predictions for the NuMI beam
journal, November 2016

Evolving Deep Networks Using HPC
conference, January 2017

  • Young, Steven R.; Rose, Derek C.; Johnston, Travis
  • Proceedings of the Machine Learning on HPC Environments - MLHPC'17
  • DOI: 10.1145/3146347.3146355
    Sort by:
    [ × clear filter / sort ]

    Similar Records in DOE PAGES and OSTI.GOV collections: