papers.md

HEPML Resources ↩

HEPML Papers

A .bib file for all papers listed is available in the tex directory.

• M. C. Romao, N. Castro, J. Milhano, R. Pedro, and T. Vale, "Use of a Generalized Energy Mover’s Distance in the Search for Rare Phenomena at Colliders," arXiv:2004.09360 [hep-ph]. (April 21, 2020)

• G. Kanwar, M. S. Albergo, D. Boyda, K. Cranmer, D. C. Hackett, S. Racanière, D. J. Rezende, and P. E. Shanahan, "Equivariant flow-based sampling for lattice gauge theory," arXiv:2003.06413 [hep-lat]. (March 13, 2020)

• J. Hollingsworth and D. Whiteson, "Resonance Searches with Machine Learned Likelihood Ratios," arXiv:2002.04699 [hep-ph]. (February 11, 2020)

• G. C. Strong, "On the impact of modern deep-learning techniques to the performance and time-requirements of classification models in experimental high-energy physics," arXiv:2002.01427 [physics.data-an]. (February 3, 2020)

• M. Romão Crispim, N. Castro, R. Pedro, and T. Vale, "Transferability of Deep Learning Models in Searches for New Physics at Colliders," Phys. Rev. D 101 (2020) no. 3, 035042, arXiv:1912.04220 [hep-ph]. (December 9, 2019)

• A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman, and J. Thaler, "OmniFold: A Method to Simultaneously Unfold All Observables," arXiv:1911.09107 [hep-ph]. (November 20, 2019)

• B. Nachman and C. Shimmin, "AI Safety for High Energy Physics," arXiv:1910.08606 [hep-ph]. (October 18, 2019)

• M. Borisyak, N. Kazeev, "Machine Learning on data with sPlot background subtraction," arXiv:1905.11719 [cs.LG]. (May 28, 2019)

• S. Caron, T. Heskes, S. Otten and B. Stienen, "Constraining the Parameters of High-Dimensional Models with Active Learning," Eur. Phys. J. C 79, 944 (2019), arXiv:1905.08628 [cs.LG]. (May 19, 2019)

• R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat, and S. Palazzo, "DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC," arXiv:1903.02433 [hep-ex]. (March 6, 2019)

• K. Datta, A. Larkoski, and B. Nachman, "Automating the Construction of Jet Observables with Machine Learning," arXiv:1902.07180 [hep-ph]. (February 19, 2019)

• MicroBooNE Collaboration, "Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber," Phys. Rev. D99 (2019) no. 9, 092001, arXiv:1808.07269 [hep-ex]. (August 22, 2018)

• M. Stoye, J. Brehmer, L. Gilles, J. Paez, and K. Cranmer, "Likelihood-free inference with an improved cross-entropy estimator," arXiv:1808.00973 [stat.ML]. (August 2, 2018)

• D. Bourgeois, C. Fitzpatrick, and S. Stahl, "Using holistic event information in the Trigger," arXiv:1808.00711 [physics.ins-det]. (August 2, 2018)

• M. Andrews, M. Paulini, S. Gleyzer, and B. Poczos, "End-to-End Physics Event Classification with the CMS Open Data: Applying Image-based Deep Learning on Detector Data to Directly Classify Collision Events at the LHC," arXiv:1807.11916 [hep-ex]. (July 31, 2018)

• J. Lin, M. Freytsis, I. Moult, and B. Nachman, "Boosting H → bb̅ with Machine Learning," arXiv:1807.10768 [hep-ph]. (July 27, 2018)

• K. Albertsson et al., "Machine Learning in High Energy Physics Community White Paper," arXiv:1807.02876 [physics.comp-ph]. (July 8, 2018)

• D. Guest, K. Cranmer, and D. Whiteson, "Deep Learning and its Application to LHC Physics," arXiv:1806.11484 [hep-ex]. (June 29, 2018)

• J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, "Constraining Effective Field Theories with Machine Learning," arXiv:1805.00013 [hep-ph]. (April 30, 2018)

• J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, "A Guide to Constraining Effective Field Theories with Machine Learning," arXiv:1805.00020 [hep-ph]. (April 30, 2018)

• CMS Collaboration, A. M. Sirunyan et al., "Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV," arXiv:1712.07158 [physics.ins-det]. (December 19, 2017)

• V. Estrade, C. Germain, I. Guyon and D. Rousseau, Adversarial learning to eliminate systematic errors: a case study in High Energy Physics, Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)

• D. Weitekamp III, T. Nguyen, D. Anderson, R. Castello, M.Pierini, M. Spiropulu and J. Vlimant, Deep topology classifiers for a more efficient trigger selection at the LHC, Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)

• L. Hertel, L. Li, P. Baldi and J. Bian, Convolutional Neural Networks for Electron Neutrino and Electron Shower Energy Reconstruction in the NOvA Detectors, Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)

• M. Stoye, J. Kieseler, M. Verzetti, H. Qu, L. Gouskos, A. Stakia and the CMS Collaboration, DeepJet: Generic physics object based jet multiclass classification for LHC experiments, Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)

• B. Hooberman, A. Farbin, G. Khattak, V. Pacela, M. Pierini, J. Vlimant, M. Spiropulu, W. Wei, M. Zhang and S. Vallecorsa, Calorimetry with Deep Learning: Particle Classification, Energy Regression, and Simulation for High-Energy Physics, Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)

• M. Paganini, L. de Oliveira and B. Nachman, Survey of Machine Learning Techniques for High Energy Electromagnetic Shower Classification, Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)

• L. de Oliveira, M. Paganini, and B. Nachman, Tips and Tricks for Training GANs with Physics Constraints, Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)

• S. Farrell, P. Calafiura, M. Mudigonda, Prabhat, D. Anderson, J. Bendavid, M. Spiropoulou, J. Vlimant, S. Zheng, G. Cerati, L. Gray, J. Kowalkowski, P. Spentzouris, A. Tsaris and D. Zurawski, Particle Track Reconstruction with Deep Learning, Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)

• I. Henrion, K. Cranmer, J. Bruna, K. Cho, J. Brehmer, G. Louppe and G. Rochette, Neural Message Passing for Jet Physics, Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)

• T. Cheng, "Recursive Neural Networks in Quark/Gluon Tagging," arXiv:1711.02633 [hep-ph]. (November 7, 2017)

• S. Chang, T. Cohen, and B. Ostdiek, "What is the Machine Learning?," arXiv:1709.10106 [hep-ph]. (September 28, 2017)

• M. Frate, K. Cranmer, S. Kalia, A. Vandenberg-Rodes, and D. Whiteson, “Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes,” arXiv:1709.05681 [physics.data-an]. (September 17, 2017)

• E. M. Metodiev, B. Nachman, and J. Thaler, “Classification without labels: Learning from mixed samples in high energy physics,” arXiv:1708.02949 [hep-ph]. (August 9, 2017)

• J. Bendavid, "Efficient Monte Carlo Integration Using Boosted Decision Trees and Generative Deep Neural Networks," arXiv:1707.00028 [hep-ph]. (June 30, 2017)

• T. Cohen, M. Freytsis, and B. Ostdiek, "(Machine) Learning to Do More with Less," arXiv:1706.09451 [hep-ph]. (June 28, 2017)

• M. Paganini, L. de Oliveira, and B. Nachman, "CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks," arXiv:1705.02355 [hep-ex]. (May 5, 2017)

• C. Shimmin, P. Sadowski, P. Baldi, E. Weik, D. Whiteson, E. Goul, and A. Sgaard, "Decorrelated Jet Substructure Tagging using Adversarial Neural Networks," arXiv:1703.03507 [hep-ex]. (March 9, 2017)

• G. Louppe, K. Cho, C. Becot, and K. Cranmer, "QCD-Aware Recursive Neural Networks for Jet Physics," arXiv:1702.00748 [hep-ph]. (February 2, 2017)

  • Lecture: QCD-Aware Neural Networks for Jet Physics, by Kyle Cranmer

• L. M. Dery, B. Nachman, F. Rubbo, and A. Schwartzman, "Weakly Supervised Classification in High Energy Physics," JHEP 05 (2017) 145, arXiv:1702.00414 [hep-ph]. (February 1, 2017)

• L. de Oliveira, M. Paganini, and B. Nachman, "Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis," arXiv:1701.05927 [stat.ML]. (January 20, 2017)

• L.-G. Pang, K. Zhou, N. Su, H. Petersen, H. Stocker, X.-N. Wang, "An equation-of-state-meter of QCD transition from deep learning," arXiv:1612.04262 [hep-ph]. (December 13, 2016)

• P. T. Komiske, E. M. Metodiev, and M. D. Schwartz, "Deep learning in color: towards automated quark/gluon jet discrimination," JHEP 01 (2017) 110, arXiv:1612.01551 [hep-ph]. (December 5, 2016)

• MicroBooNE Collaboration, R. Acciarri et al., "Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber," JINST 12 (2017) no. 03, P03011, arXiv:1611.05531 [physics.ins-det]. (November 16, 2016)

• M. Kagan, L. d. Oliveira, L. Mackey, B. Nachman, and A. Schwartzman, "Boosted Jet Tagging with Jet-Images and Deep Neural Networks," EPJ Web Conf. 127 (2016) 00009. (November 15, 2016)

• G. Bertone, M. P. Deisenroth, J. S. Kim, S. Liem, R. Ruiz de Austri, and M. Welling, "Accelerating the BSM interpretation of LHC data with machine learning," arXiv:1611.02704 [hep-ph]. (November 8, 2016)

• G. Louppe, M. Kagan, and K. Cranmer, "Learning to Pivot with Adversarial Networks," arXiv:1611.01046 [stat.ME]. (November 3, 2016)

  • Lecture: (Machine) Learning to Pivot with Adversarial Networks, by Gilles Louppe

• J. Barnard, E. N. Dawe, M. J. Dolan, and N. Rajcic, "Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks," Phys. Rev. D95 (2017) no. 1, 014018, arXiv:1609.00607 [hep-ph] (September 2, 2016)

• A. Rogozhnikov, “Reweighting with Boosted Decision Trees,” J. Phys. Conf. Ser. 762 (2016) no. 1, 012036, arXiv:1608.05806 [physics.data-an]. (August 20, 2016)

• D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban, and D. Whiteson, "Jet Flavor Classification in High-Energy Physics with Deep Neural Networks," Phys. Rev. D94 (2016) no. 11, 112002, arXiv:1607.08633 [hep-ex]. (July 28, 2016)

• S. Caron, J.S. Kim, K. Rolbiecki, R. Ruiz de Austri, B. Stienen "The BSM-AI project: SUSY-AI -- Generalizing LHC limits on supersymmetry with machine learning", EPJ C (2017) 77:257, arXiv:1605.02797 [hep-ph]. (May 9, 2016)

• A. Aurisano, A. Radovic, D. Rocco, A. Himmel, M. D. Messier, E. Niner, G. Pawloski, F. Psihas, A. Sousa, and P. Vahle, "A Convolutional Neural Network Neutrino Event Classifier," JINST 11 (2016) no. 09, P09001, arXiv:1604.01444 [hep-ex]. (April 5, 2016)

• P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, and D. Whiteson, “Parameterized neural networks for high-energy physics,” Eur. Phys. J. C76 (2016) no. 5, 235, arXiv:1601.07913 [hep-ex]. (January 28, 2016)

• L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, "Jet-images deep learning edition," JHEP 07 (2016) 069, arXiv:1511.05190 [hep-ph]. (November 16, 2015)

• A. Rogozhnikov, A. Bukva, V. Gligorov, A. Ustyuzhanin, M. Williams, "New approaches for boosting to uniformity," arXiv:1410.4140 [hep-ex]. (October 15, 2014)

• P. Baldi, P. Sadowski, and D. Whiteson, "Searching for Exotic Particles in High-Energy Physics with Deep Learning," Nature Commun. 5 (2014) 4308, arXiv:1402.4735 [hep-ph]. (February 19, 2014)

• J. Stevens, M. Williams, "uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers," arXiv:1305.7248 [nucl-ex]. (May 30, 2013)

• V. Gligorov, M. Williams, "Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree," arXiv:1210.6861 [physics]. (October 25, 2012)

• B. H. Denby, “Neural Networks and Cellular Automata in Experimental High-energy Physics,” Comput. Phys. Commun. 49 (1988) 429–448. (September 20, 1987)