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CERN Document Server Pronađeno je 2,021 zapisa  1 - 10slijedećikraj  idi na zapis: Pretraživanje je potrajalo 1.15 sekundi 
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End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data / Andrews, Michael (Carnegie Mellon U.) ; Burkle, Bjorn (Brown U.) ; Chen, Yi-fan (Digital Pathways, Mtn. View) ; DiCroce, Davide (Alabama U.) ; Gleyzer, Sergei (Alabama U.) ; Heintz, Ulrich (Brown U.) ; Narain, Meenakshi (Brown U.) ; Paulini, Manfred (Carnegie Mellon U.) ; Pervan, Nikolas (Brown U.) ; Shafi, Yusef (Google Inc. ; Digital Pathways, Mtn. View) et al.
We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation of the high-energy collision event. [...]
arXiv:2104.14659.- 2021 - 9 p. - Published in : EPJ Web Conf.: 251 (2021) , pp. 04030
- Published in : Phys. Rev. D Fulltext: document - PDF; 2104.14659 - PDF;
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021, pp.04030
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End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data / Burkle, Bjorn (speaker) (Brown University (US))
We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation of the high-energy collision event. [...]
2021 - 1067. Conferences; 25th International Conference on Computing in High Energy & Nuclear Physics External links: Talk details; Event details In : 25th International Conference on Computing in High Energy & Nuclear Physics
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End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data / Andrews, M. (Carnegie Mellon U.) ; Alison, J. (Carnegie Mellon U.) ; An, S. (Carnegie Mellon U. ; CERN) ; Bryant, Patrick ; Burkle, B. (Brown U.) ; Gleyzer, S. (Alabama U.) ; Narain, M. (Brown U.) ; Paulini, M. (Carnegie Mellon U.) ; Poczos, B. (Carnegie Mellon U.) ; Usai, E. (Brown U.)
We describe the construction of end-to-end jet image classifiers based on simulated low-level detector data to discriminate quark- vs. gluon-initiated jets with high-fidelity simulated CMS Open Data. [...]
arXiv:1902.08276.- 2020-10-11 - 8 p. - Published in : Nucl. Instrum. Methods Phys. Res., A 977 (2020) 164304 Fulltext: PDF; Fulltext from publisher: PDF;
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Accelerating End-to-End Deep Learning for Particle Reconstruction using CMS open data / Di Croce, Davide (speaker) (University of Alabama (US))
Machine learning algorithms are gaining ground in high energy physics for applications in particle and event identification, physics analysis, detector reconstruction, simulation and trigger. Currently, most data-analysis tasks at LHC experiments benefit from the use of machine learning. [...]
2021 - 702. Conferences; 25th International Conference on Computing in High Energy & Nuclear Physics External links: Talk details; Event details In : 25th International Conference on Computing in High Energy & Nuclear Physics
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End-to-end Deep Learning Inference in CMS software framework /CMS Collaboration
Deep learning techniques have been proven to provide excellent performance for a variety of high energy physics applications, such as particle identification, event reconstruction and trigger operations. Using low-level detector information in end-to-end deep learning approach allows to probe the poorly explored regions for dark matter search. [...]
CMS-DP-2023-036; CERN-CMS-DP-2023-036.- Geneva : CERN, 2023 - 15 p. Fulltext: PDF;
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End-to-end particle and event identification at the Large Hadron Collider with CMS Open Data / Alison, John (Carnegie Mellon U.) ; An, Sitong (CERN) ; Bryant, Patrick (Carnegie Mellon U.) ; Burkle, Bjorn (Carnegie Mellon U. ; Brown U.) ; Gleyzer, Sergei (Alabama U.) ; Narain, Meenakshi (Brown U.) ; Paulini, Manfred (Carnegie Mellon U.) ; Poczos, Barnabas (Carnegie Mellon U. ; Northeastern U.) ; Usai, Emanuele (Brown U.)
From particle identification to the discovery of the Higgs boson, deep learning algorithms have become an increasingly important tool for data analysis at the Large Hadron Collider (LHC). [...]
arXiv:1910.07029. - 5 p.
Fulltext
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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 / Andrews, M. (Carnegie Mellon U.) ; Paulini, M. (Carnegie Mellon U.) ; Gleyzer, S. (Florida U.) ; Poczos, B. (Carnegie Mellon U.)
This paper describes the construction of novel end-to-end image-based classifiers that directly leverage low-level simulated detector data to discriminate signal and background processes in pp collision events at the Large Hadron Collider at CERN. To better understand what end-to-end classifiers are capable of learning from the data and to address a number of associated challenges, we distinguish the decay of the standard model Higgs boson into two photons from its leading background sources using high-fidelity simulated CMS Open Data. [...]
arXiv:1807.11916.- 2020-03-11 - 14 p. - Published in : Comput. Softw. Big Sci. 4 (2020) 6
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Exploring End-to-end Deep Learning Applications for Event Classification at CMS / Andrews, Michael Benjamin (Carnegie Mellon U.) ; Paulini, Manfred (Carnegie Mellon U.) ; Gleyzer, Sergei (Florida U.) ; Barnabas Poczos /CMS Collaboration
An essential part of new physics searches at the Large Hadron Collider (LHC) at CERN involves event classification, or distinguishing potential signal events from those coming from background processes. Current machine learning techniques accomplish this using traditional hand-engineered features like particle 4-momenta, motivated by our understanding of particle decay phenomenology. [...]
CMS-CR-2018-379.- Geneva : CERN, 2019 - 9 p. - Published in : EPJ Web Conf. 214 (2019) 06031 Fulltext: PDF; Published fulltext: PDF;
In : 23rd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2018, Sofia, Bulgaria, 9 - 13 Jul 2018, pp.06031
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End-to-end deep learning inference with CMSSW via ONNX using Docker / Chaudhari, Purva (Alabama U.) ; Chaudhari, Shravan (Alabama U.) ; Chudasama, Ruchi (Alabama U.) ; Gleyzer, Sergei (Alabama U.) /CMS Collaboration
Deep learning techniques have been proven to provide excellent performance for a variety of high energy physics applications, such as particle identification, event reconstruction and trigger operations. Recently, we developed an end-to-end deep learning approach to identify various particles using low-level detector information from high energy collisions. [...]
arXiv:2309.14254; CMS-CR-2023-161.- Geneva : CERN, 2024 - 9 p.
- Published in : EPJ Web Conf.: 295 (2024) , pp. 09015 Fulltext: document - PDF; 2309.14254 - PDF; CR2023_161 - PDF;
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.09015
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Strange quark as a probe for new physics in the Higgs sector / Albert, Alexander (Cornell U., CLASSE) ; Basso, Matthew J. (Toronto U. ; SLAC ; Stanford U., Phys. Dept.) ; Bright-Thonney, Samuel K. (Cornell U., CLASSE) ; Cairo, Valentina M.M. (CERN) ; Damerell, Chris (Rutherford) ; Egana-Ugrinovic, Daniel (Perimeter Inst. Theor. Phys.) ; Einhaus, Ulrich (DESY) ; Heintz, Ulrich (Brown U.) ; Homiller, Samuel (Harvard U.) ; Kawada, Shin-ichi (DESY) et al.
This paper describes a novel algorithm for tagging jets originating from the hadronisation of strange quarks (strange-tagging) with the future International Large Detector (ILD) at the International Linear Collider (ILC). [...]
arXiv:2203.07535 ; Report-no: ILD-PHYS-PROC-2022-001. - 69 p.
eConf - Fulltext
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