Zobrazeno 1 - 10
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pro vyhledávání: '"Andrews Michael"'
Autor:
Dong, Hao, Siddiquie, Reshma, Xiao, Xuemei, Andrews, Michael, Bergman, Brian, Hui, Chung-Yuen, Jagota, Anand
Lubricated contacts in soft materials are important in various engineering systems and natural settings. Three major lubrication regimes are boundary (BL), mixed (ML), and elasto-hydrodynamic (EHL) lubrication, where the contact region is dry, partia
Externí odkaz:
http://arxiv.org/abs/2410.09202
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