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pro vyhledávání: '"Paulini Manfred"'
A real-time autoencoder-based anomaly detection system using semi-supervised machine learning has been developed for the online Data Quality Monitoring system of the electromagnetic calorimeter of the CMS detector at the CERN LHC. A novel method is i
Externí odkaz:
http://arxiv.org/abs/2407.20278
Autor:
Andrews Michael, Burkle Bjorn, Chaudhari Shravan, Di Croce Davide, Gleyzer Sergei, Heintz Ulrich, Narain Meenakshi, Paulini Manfred, Usai Emanuele
Publikováno v:
EPJ Web of Conferences, Vol 251, p 03057 (2021)
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 ben
Externí odkaz:
https://doaj.org/article/45d5fcb893f94bdaa449c946063df085
Autor:
Andrews Michael, Burkle Bjorn, Chaudhari Shravan, DiCroce Davide, Gleyzer Sergei, Heintz Ulrich, Narain Meenakshi, Paulini Manfred, Usai Emanuele
Publikováno v:
EPJ Web of Conferences, Vol 251, p 04030 (2021)
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 combine
Externí odkaz:
https://doaj.org/article/68f4b42e2190438b8f61ecf2484f33d6
The online Data Quality Monitoring system (DQM) of the CMS electromagnetic calorimeter (ECAL) is a crucial operational tool that allows ECAL experts to quickly identify, localize, and diagnose a broad range of detector issues that would otherwise hin
Externí odkaz:
http://arxiv.org/abs/2308.16659
Publikováno v:
EPJ Web of Conferences, Vol 214, p 06031 (2019)
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
Externí odkaz:
https://doaj.org/article/9383ceee798d4e46ab9a2a40eda3d06e
We show that the Laser Interferometer Gravitational Wave Observatory (LIGO) is a powerful instrument in the Search for Extraterrestrial Intelligence (SETI). LIGO's ability to detect gravitational waves (GWs) from astrophysical sources, such as binary
Externí odkaz:
http://arxiv.org/abs/2212.02065
Autor:
Andrews, Michael, Paulini, Manfred, Sellers, Luke, Bobrick, Alexey, Martire, Gianni, Vestal, Haydn
All scientific claims of gravitational wave discovery to date rely on the offline statistical analysis of candidate observations in order to quantify significance relative to background processes. The current foundation in such offline detection pipe
Externí odkaz:
http://arxiv.org/abs/2207.04749
Autor:
Andrews, Michael, Burkle, Bjorn, Chen, Yi-fan, DiCroce, Davide, Gleyzer, Sergei, Heintz, Ulrich, Narain, Meenakshi, Paulini, Manfred, Pervan, Nikolas, Shafi, Yusef, Sun, Wei, Usai, Emanuele, Yang, Kun
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 combine
Externí odkaz:
http://arxiv.org/abs/2104.14659
Autor:
Alison, John, An, Sitong, Andrews, Michael, Bryant, Patrick, Burkle, Bjorn, Gleyzer, Sergei, Heintz, Ulrich, Narain, Meenakshi, Paulini, Manfred, Poczos, Barnabas, Usai, Emanuele
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). We present an innovative end-to-end deep learning approach for
Externí odkaz:
http://arxiv.org/abs/1910.07029
Autor:
Andrews, Michael, Alison, John, An, Sitong, Bryant, Patrick, Burkle, Bjorn, Gleyzer, Sergei, Narain, Meenakshi, Paulini, Manfred, Poczos, Barnabas, Usai, Emanuele
Publikováno v:
Nucl. Instrum. Methods Phys. Res. A 977, 164304 (2020)
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. We highlight the importance of precise spatial inf
Externí odkaz:
http://arxiv.org/abs/1902.08276