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This work proposes a domain-informed neural network architecture for experimental particle physics, using particle interaction localization with the time-projection chamber (TPC) technology for dark matter research as an example application.
Double Beta Decay APPEC Committee Report
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Convolutional networks for images, speech, and time-series
LeCun, Y. and Bengio, Y. (1995) · 1995
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Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998) · 1998
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The Nature of Statistical Learning Theory (New York, NY: Springer New York), vol. 8
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The Star time projection chamber: A Unique tool for studying high multiplicity events at RHIC
Anderson, M., Berkovitz, J., Betts, W., Bossingham, R., Bieser, F., Brown, R., et al. (2003) · 2003
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Measurement of single electron emission in two-phase xenon
Edwards, B., Araújo, H., Chepel, V., Cline, D., Durkin, T., Gao, J., et al. (2008) · 2008
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The ALICE TPC, a large 3-dimensional tracking device with fast readout for ultra-high multiplicity events
Alme, J., Andres, Y., Appelshäuser, H., Bablok, S., Bialas, N., Bolgen, R., et al. (2010) · 2010
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The Large Underground Xenon (LUX) Experiment
Akerib, D., Bai, X., Bedikian, S., Bernard, E., Bernstein, A., Bolozdynya, A., et al. (2013) · 2012
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Position Reconstruction in a Dual Phase Xenon Scintillation Detector
Solovov, V. N., Belov, V. A., Akimov, D. Y., Araujo, H. M., Barnes, E. J., Burenkov, A. A., et al. (2012) · 2012
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Observation and applications of single-electron charge signals in the XENON100 experiment
Aprile, E., Alfonsi, M., Arisaka, K., Arneodo, F., Balan, C., Baudis, L., et al. (2014) · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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Light propagation and reflection off teflon in liquid xenon detectors for the XENON100 and XENON1T dark matter experiments
Levy, C. (2014) · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems
[Dataset] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., et al. (2015) · 2015
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Analytical methods for squaring the disc
Fong, C. (2015) · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. (2015) · 2015
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A Convolutional Neural Network Neutrino Event Classifier
Aurisano, A., Radovic, A., Rocco, D., Himmel, A., Messier, M. D., Niner, E., et al. (2016) · 2016
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Deep learning (Cambridge, MA: MIT press)
Goodfellow, I., Bengio, Y., and Courville, A. (2016) · 2016
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Mappings between sphere, disc, and square
Lambers, M. (2016) · 2016
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The XENON1T Dark Matter Experiment
Aprile, E., Aalbers, J., Agostini, F., Alfonsi, M., Amaro, F. D., Anthony, M., et al. (2017) · 2017
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Dark Matter Results From 54-Ton-Day Exposure of PandaX-II Experiment
Cui, X., Abdukerim, A., Chen, W., Chen, X., Chen, Y., Dong, B., et al. (2017) · 2017
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Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
de Oliveira, L., Paganini, M., and Nachman, B. (2017) · 2017
Direct Detection of WIMP Dark Matter: Concepts and Status
Schumann, M. (2019) · 2019
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Review of deep learning algorithms and architectures
Shrestha, A. and Mahmood, A. (2019) · 2019
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Machine Learning Accelerated Likelihood-Free Event Reconstruction in Dark Matter Direct Detection
Simola, U., Pelssers, B., Barge, D., Conrad, J., and Corander, J. (2019) · 2019
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Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume IV: Far Detector Single-phase Technology
Abi, B., Acciarri, R., Acero, M. A., Adamov, G., Adams, D., Adinolfi, M., et al. (2020) · 2020
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End-to-end object detection with transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020) · 2020
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The frontier of simulation-based inference
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Raissi, M., Perdikaris, P., and Karniadakis, G. E. (2017) · 2017
Cited alongside, same era.
Position Reconstruction in LUX
Akerib, D. S., Alsum, S., Araú jo, H. M., Bai, X., Bailey, A. J., Balajthy, J., et al. (2018) · 2018
Cited alongside, same era.
Sensitivity and Discovery Potential of nEXO to Neutrinoless Double Beta Decay
Albert, J. B., Anton, G., Arnquist, I. J., Badhrees, I., Barbeau, P., Beck, D., et al. (2018) · 2018
Cited alongside, same era.
Machine Learning in High Energy Physics Community White Paper
Albertsson, K., Altoe, P., Anderson, D., Anderson, J., Andrews, M., Espinosa, J. P. A., et al. (2018) · 2018
Cited alongside, same era.
Deep Neural Networks for Energy and Position Reconstruction in EXO-200
Delaquis, S., Jewell, M., Ostrovskiy, I., Weber, M., Ziegler, T., Dalmasson, J., et al. (2018) · 2018
Cited alongside, same era.
Hoogeboom, E., Peters, J. W., Cohen, T. S., and Welling, M. (2018) · 2018
Cited alongside, same era.
Cranmer, K., Brehmer, J., and Louppe, G. (2020) · 2020
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Deep Neural Networks for Position Reconstruction in XENON1T
de Vries, L. (2020) · 2020
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Convolutional neural network approach to event position reconstruction in DarkSide-50 experiment
Grobov, A. and Ilyasov, A. (2020) · 2020
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Enhancing Direct Searches for Dark Matter: Spatial-Temporal Modeling and Explicit Likelihoods
Pelssers, B. E. J. (2020) · 2020
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Detection prospects for the second-order weak decays of 124
Wittweg, C., Lenardo, B., Fieguth, A., and Weinheimer, C. (2020) · 2020
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A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory
Abbasi, R., Ackermann, M., Adams, J., Aguilar, J., Ahlers, M., Ahrens, M., et al. (2021) · 2021
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Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE
Abratenko, P., Alrashed, M., An, R., Anthony, J., Asaadi, J., Ashkenazi, A., et al. (2021) · 2021
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Enforcing analytic constraints in neural networks emulating physical systems
Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., and Gentine, P. (2021) · 2021
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Domain-informed neural networks
[Dataset] Liang, S. and Tunnell, C. (2021) · 2021
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Horizontal position reconstruction in PandaX-II
Zhang, D., Tan, A., Abdukerim, A., Chen, W., Chen, X., Chen, Y., et al. (2021) · 2021
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Direct detection of dark matter—APPEC committee report
Billard, J., Boulay, M., Cebrián, S., Covi, L., Fiorillo, G., Green, A., et al. (2022) · 2022
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