Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Shah Rukh"'
Autor:
Qasim, Shah Rukh, Chernyavskaya, Nadezda, Kieseler, Jan, Long, Kenneth, Viazlo, Oleksandr, Pierini, Maurizio, Nawaz, Raheel
Publikováno v:
Eur. Phys. J. C 82, 753 (2022)
We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a distance
Externí odkaz:
http://arxiv.org/abs/2204.01681
Autor:
Bhattacharya, Saptaparna, Chernyavskaya, Nadezda, Ghosh, Saranya, Gray, Lindsey, Kieseler, Jan, Klijnsma, Thomas, Long, Kenneth, Nawaz, Raheel, Pedro, Kevin, Pierini, Maurizio, Pradhan, Gauri, Qasim, Shah Rukh, Viazlo, Oleksander, Zehetner, Philipp
We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Neural Networks (GNNs) and directly analyzes the hits in each HGCAL endc
Externí odkaz:
http://arxiv.org/abs/2203.01189
Autor:
Iiyama, Yutaro, Cerminara, Gianluca, Gupta, Abhijay, Kieseler, Jan, Loncar, Vladimir, Pierini, Maurizio, Qasim, Shah Rukh, Rieger, Marcel, Summers, Sioni, Van Onsem, Gerrit, Wozniak, Kinga, Ngadiuba, Jennifer, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Rankin, Dylan, Jindariani, Sergo, Liu, Mia, Pedro, Kevin, Tran, Nhan, Kreinar, Edward, Wu, Zhenbin
Publikováno v:
Frontiers in Big Data 3 (2021) 44
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGP
Externí odkaz:
http://arxiv.org/abs/2008.03601
Publikováno v:
Eur. Phys. J. C, 79 7 (2019) 608
We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can exploit the full detector granularity, while natively m
Externí odkaz:
http://arxiv.org/abs/1902.07987
Autor:
Shah Rukh Qasim, Nadezda Chernyavskaya, Jan Kieseler, Kenneth Long, Oleksandr Viazlo, Maurizio Pierini, Raheel Nawaz
Publikováno v:
European Physical Journal
We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a distance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ca06924cc1a438d23eb8287bf1ed429
http://arxiv.org/abs/2204.01681
http://arxiv.org/abs/2204.01681
Autor:
Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta, Jan Kieseler, Vladimir Loncar, Maurizio Pierini, Shah Rukh Qasim, Marcel Rieger, Sioni Summers, Gerrit Van Onsem, Kinga Anna Wozniak, Jennifer Ngadiuba, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Mia Liu, Kevin Pedro, Nhan Tran, Edward Kreinar, Zhenbin Wu
Publikováno v:
Frontiers in Big Data
Frontiers in Big Data, Vol 3 (2021)
Frontiers in Big Data, Vol 3 (2021)
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGP
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 79, Iss 7, Pp 1-11 (2019)
European Physical Journal
The European Physical Journal C
European Physical Journal
The European Physical Journal C
We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can exploit the full detector granularity, while natively m