Cosmic Background Removal with Deep Neural Networks in SBND
暂无分享,去创建一个
Los Alamos National Laboratory | University College London | Enrico Fermi Institute | Colorado State University | C. | Ñ. | R. | U. Pennsylvania | U. Florida | U. Michigan | C. University | B. N. Laboratory | F. N. Laboratory | G. | W. | I.. | Á. | D. | U. Sheffield | G. Brandt | A. Ereditato | J. Pater | R. University | F. Marinho | A. Roeck | M. Tripathi | M. Malek | M. Mooney | S. Gollapinni | L. Bagby | J. Zennamo | D. Torretta | V. D. Benedetto | M. | L. Astiz | D. Franco | H. Chen | A. Nowak | M. Tutto | R. Guenette | L. Mora | U. Manchester | U. Sussex | K. Mistry | O. Palamara | J. Yu | I. Gil-Botella | J. Spitz | M. Toups | M. Worcester | P. Guzowski | L. University | U. Liverpool | Y. Chen | E. Worcester | A. Holin | K. Mavrokoridis | M. Soderberg | M. Stancari | A. Szelc | S. Soldner-Rembold | Universidade Estadual de Campinas | S. University | J. Evans | S. Tufanli | D. Brailsford | C. Backhouse | F. Psihas | B. Zamorano | I. Lepetic | W. Badgett | W. Tang | Cern | N. McConkey | P. Green | E. Raguzin | V. Basque | E. Cristaldo | C. Cuesta | S. Gao | D. Garcia-Gamez | O. Goodwin | C. Griffith | L. Kashur | A. Mastbaum | L. Paulucci | M. Reggiani-Guzzo | D. Rivera | A. Scarff | E. Tyley | A. Bhanderi | A. Bhat | V. Meddage | T. Mettler | A. Navrer-Agasson | M. Ross-Lonergan | G. Scanavini | W. Foreman | V. Pandey | A. N. Laboratory | Universidade Federal do Abc | C. Adams | W. Schmitz | V. Stenico | Universitat Bern | U. Granada | G. Chisnall | J. Larkin | J. T. Vidal | Ciemat | U. Carlos | C. Louis | V. | B. | G. Putnam | C. Spooner | Harvard University | U. T. Arlington | I. O. Technology | S. Fitzpatrick | J. | H. Frandini | T. Ham | D. Mardsen | S. C. R. Acciarri | Q. Bazetto | F. Carneiro | I. Crespo-Anad'on | C. Ezeribe | T. Fleming | D.Kalra | J. Kim | A. Kudryavtsev | R. Littlejohn | P. M'endez | A. Moura | L. Pimentel | A. Valdiviesso | Center for Information Technology Renato Archer Campinas | Universidade Federal de Alfenas | Fiuna Facultad de Ingenier'ia | University of Tennessee, Knoxville | W. Laboratory | M. Małek | U. Bern
[1] J. I. Crespo-Anad'on,et al. Construction of precision wire readout planes for the Short-Baseline Near Detector (SBND) , 2020, Journal of Instrumentation.
[2] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[3] D. A. Wickremasinghe,et al. First Measurement of Inclusive Muon Neutrino Charged Current Differential Cross Sections on Argon at E_{ν}∼0.8 GeV with the MicroBooNE Detector. , 2019, Physical review letters.
[4] Kazuhiro Terao,et al. Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data , 2019, ArXiv.
[5] MicroBooNE collaboration C. Adams,et al. Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber , 2018, Physical Review D.
[6] B. Abi. The DUNE Far Detector Interim Design Report, Volume 2: Single-Phase Module , 2018 .
[7] D. P. Méndez,et al. The DUNE Far Detector Interim Design Report, Volume 3: Dual-Phase Module , 2018, 1807.10340.
[8] D. P. Méndez,et al. The DUNE Far Detector Interim Design Report Volume 1: Physics, Technology and Strategies , 2018 .
[9] C. D. Moore,et al. Ionization electron signal processing in single phase LArTPCs. Part II. Data/simulation comparison and performance in MicroBooNE , 2018, Journal of Instrumentation.
[10] Chao Zhang,et al. Three-dimensional imaging for large LArTPCs , 2018, Journal of Instrumentation.
[11] C. D. Moore,et al. Ionization electron signal processing in single phase LArTPCs. Part I. Algorithm Description and quantitative evaluation with MicroBooNE simulation , 2018, Journal of Instrumentation.
[12] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[13] T. V. Vieira,et al. The Single-Phase ProtoDUNE Technical Design Report , 2017, 1706.07081.
[14] E. L. Snider,et al. LArSoft: toolkit for simulation, reconstruction and analysis of liquid argon TPC neutrino detectors , 2017 .
[15] D. A. Wickremasinghe,et al. Design and construction of the MicroBooNE detector , 2016, 1612.05824.
[16] B. Baller,et al. First observation of low energy electron neutrinos in a liquid argon time projection chamber , 2016, 1610.04102.
[17] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[19] A. Rappoldi,et al. Operation and performance of the ICARUS T600 cryogenic plant at Gran Sasso underground Laboratory , 2015, 1504.01556.
[20] A. Rappoldi,et al. A Proposal for a Three Detector Short-Baseline Neutrino Oscillation Program in the Fermilab Booster Neutrino Beam , 2015, 1503.01520.
[21] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[22] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[23] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[24] C. Bromberg,et al. The ArgoNeuT Detector in the NuMI Low-Energy beam line at Fermilab , 2012, 1205.6747.
[25] A. Dabrowska,et al. Underground operation of the ICARUS T600 LAr-TPC: first results , 2011, 1106.0975.
[26] R. Hatcher,et al. The GENIE * Neutrino Monte Carlo Generator , 2009, 0905.2517.
[27] J. Knapp,et al. CORSIKA: A Monte Carlo code to simulate extensive air showers , 1998 .
[28] Andrew L. Maas. Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .
[29] A. Dell'Acqua,et al. Geant4 - A simulation toolkit , 2003 .