Publications

Recent Papers: MicroBooNE/Deep Learning on LArTPCs

  • Abratenko, K. Mason, J. Mills, R. Sharankova, T.M. Wongjirad with the MicroBooNE collaboration. “Search for an anomalous excess of charged-current quasi-elastic ╬Że interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction.” arXiv:2110.14080 (2021).
  • K. Mason with the MicroBooNE Collaboration. “Electromagnetic Shower Reconstruction and Energy Validation with Michel and $\pi^0$ Samples for the MicroBooNE Deep-Learning-Based Low Energy Excess Analysis.” arxiv:2110.11874
  • Abratenko, K. Mason, J. Mills, R. Sharankova, T.M. Wongjirad with the MicroBooNE collaboration, “Semantic Segmentation with a Sparse Convolutional Neural Network for Event Reconstruction in MicroBooNE” arXiv:2012.08513, accepted to PRD (2020)
  • P. Lutkus, S. Aeron, and T. Wongjirad. “Towards Designing and Exploiting Generative Networks for Neutrino Physics Experiments using Liquid Argon Time Projection Chambers.” International Conference on Learning Representations (ICLR) 2021 workshop on Deep Learning and Simulations
  • R. Sharankoa and T. Wongjirad. Particle ID in Neutrino Experiments. Chapter in book on “AI in Particle Physics.” To be published by IJMPA (2021)
  • P. Abratenko, K. Mason, J. Mills, R. Sharankova, T.M. Wongjirad with the MicroBooNE collaboration, “A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber.” arXiv:2010.08653, accepted to PRD (2020)
  • C. Adams, K. Terao, T. Wongjirad. “PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics.” arXiv:2006.01993 (2020).
  • T. Wongjirad with others. Snowmass LOI: Scalable, End-to-End Optimizable Data Reconstruction and Physics Inference Techniques for Large-scale Particle Imaging Neutrino Detectors (2020).
  • R. Sharankova, T.M. Wongjirad et al. (The MicroBooNE Collaboration), Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber. Physical Review D 99 (9), 092001
  • A. Radovic, M. Williams, D. Rousseau, M. Kagan, D. Bonacorsi, A. Himmel, A. Aurisano, K. Terao, T. Wongjirad, “Machine learning at the energy and intensity frontiers of particle physics.” Nature 560 (7716) (2018).
  • MicroBooNE Collaboration. “Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber.” JINST 12 (03) P03011 (2017)