Publications

S. Alexander, S. Gleyzer, E. McDonough, M. Toomey and E. Usai,  “Deep Learning the Morphology of Dark Matter Substructure“, arXiv:1909.07346, September 2019

 

 

M. Andrews, M. Paulini, S. Gleyzer and B. Poczos, “End-to-End Deep Learning for Particle and Event Classification”, in Proceedings of the International Conference on Computing in High Energy and Nuclear Physics, EPJ Web of Conferences 214, 06031 (2019)

 

S. Gleyzer et al., “New Machine Learning Developments in ROOT/TMVA”, in Proceedings of the International Conference on Computing in High Energy and Nuclear Physics, EPJ Web of Conferences 214, 06014 (2019)

 

D. Bourilkov et al. (including S. Gleyzer), “Machine Learning Techniques in the Search for the Higgs Boson in the di-muon Final State”, in Proceedings of the International Conference on Computing in High Energy and Nuclear Physics, EPJ Web of Conferences 214, 06002 (2019)

 

Z. Ahmed et al. (including S. Gleyzer),  “New Technologies for Discovery“, report of the 2018 DPF Coordinating Panel for Advanced Detectors, July 2019

 

 

A. Sirunyan et al. (including S. Gleyzer), “Machine Learning Auto-Categorization to Optimize the Sensitivity of High-Energy Physics Analyses”, in review, 2019

 

 

M. Andrews et al. (including S. Gleyzer), “End-to-End Identification of Quarks and Gluons with the CMS Open Data”, arXiv:1902.08276, February 2019

 

 

A. Sirunyan et al. (including S. Gleyzer), “Search for the Higgs boson production in the di-muon final state with pp collisions at √s = 13 TeV”, Physical Review Letters, 021801, 2018

 

 

A. Sirunyan et al. (including S. Gleyzer), “Observation of Higgs Boson Decay to Bottom Quarks”, Physical Review Letters, 121, 121801, 2018

 

 

S. Gleyzer et al., “The Rise of Deep Learning“, CERN Courier, 2018

 

 

 

S. Gleyzer et al., “End-To-End Deep Learning for Event Classification in CMS”, in Proceedings of Machine Learning in Science and Engineering, 2018

K. Albertsoon et al. (including S. Gleyzer), “Machine Learning in High-Energy Physics Community White Paper“, main editor, arXiv:1807.02876, 2018

 

M. Andrews, M. Paulini, S. Gleyzer and B. Poczos, “End-to-End Physics Event Classification with CMS Open Data: Applying Image-based Deep Learning on Detector Data to Directly Classify Collision Events at the LHC”, arXiv:1807.11916,  2018

 

D. Berzano et al. (including S. Gleyzer), “Community White Paper: Training, Staffing and Careers“, arXiv:1807.02875, 2018

 

 

S. Gleyzer et al., “End-To-End Deep Learning for Event Classification”, in Proceedings of XVIII International Workshop on Advanced Computing and Analysis Techniques in Physics Research, 2018

 

S. Gleyzer et al., “Machine-Learning Development in High-Energy Physics”, to appear in Proceedings of the XII Quark Confinement and the Hadron Spectrum Conference, 2018

 

J. Albrecht et al. (including S. Gleyzer), “A Roadmap for High-Energy Physics Software and Computing R&D for the 2020s“, arXiv:1712.06982, 2017

 

S. Gleyzer et al., “Machine Learning Developments n ROOT”, in Proceedings of International Conference on Computing in High Energy and Nuclear Physics, 2017

 

D. Acosta et al. (including S. Gleyzer), “Boosted Decision Trees in the CMS Level-1 Endcap Muon Trigger ”, in Proceedings of Topical Workshop in Electronics for Particle Physics, 2017

 

 

S. Gleyzer et al., “Accelerating High-Energy Physics Exploration with Deep Learning”, in Proceedings of the Practice and Experience in Advanced Research Computing, 2017

 

 

S. Gleyzer et al., “Falcon: Towards an Ultra Fast Non-Parametric Detector Simulator”, in Proceedings of the New Physics Working Group of the 2015 Les Houches Workshop, Physics at TeV Colliders, arXiv:1605.02684, 2016

 

 

S. Chatrchyan et al. (including S. Gleyzer), “Measurement of Prompt Double J/ψ production, Journal of High Energy Physics, JHEP 09 (2014) 094

 

S. Chatrchyan et al. (including S. Gleyzer), “Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC”, Phys. Lett. B 716 (2012) 30

 

S. Gleyzer and H. Prosper, “PARADIGM: Decision-Making Framework for Variable Selection and Reduction in High Energy Physics”, in Proceedings of XII International Workshop on Advanced Computing and Analysis Techniques in Physics Research, 2009

 

S. Abdullin et al. (including S. Gleyzer), “The CMS barrel calorimeter response to particle beams from 2 to 350 GeV/c”, Eur. Phys. J. C 60 (2009) 359

 

S. Gleyzer et al., “The h0 to A0A0 to bbτ+τ Signal in Vector Boson Fusion Production at the LHC”, Proceedings of the Higgs Boson Working Group of the 2007 Les Houches Workshop, Physics at TeV Colliders, arXiv:0803.1154, 2007

 

I am also an author of over 500 publications with the CMS Collaboration since 2006