Zobrazeno 1 - 10
of 1 817
pro vyhledávání: '"Klijnsma Thomas"'
Autor:
Hewes Jeremy, Aurisano Adam, Cerati Giuseppe, Kowalkowski Jim, Lee Claire, Liao Wei-keng, Day Alexandra, Agrawal Ankit, Spiropulu Maria, Vlimant Jean-Roch, Gray Lindsey, Klijnsma Thomas, Calafiura Paolo, Conlon Sean, Farrell Steve, Ju Xiangyang, Murnane Daniel
Publikováno v:
EPJ Web of Conferences, Vol 251, p 03054 (2021)
This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar
Externí odkaz:
https://doaj.org/article/c67ce5e63cd34f69b730ef8fededbcd9
Autor:
Wang, Chun-Yi, Ju, Xiangyang, Hsu, Shih-Chieh, Murnane, Daniel, Calafiura, Paolo, Farrell, Steven, Spiropulu, Maria, Vlimant, Jean-Roch, Aurisano, Adam, Hewes, V, Cerati, Giuseppe, Gray, Lindsey, Klijnsma, Thomas, Kowalkowski, Jim, Atkinson, Markus, Neubauer, Mark, DeZoort, Gage, Thais, Savannah, Ballow, Alexandra, Lazar, Alina, Caillou, Sylvain, Rougier, Charline, Stark, Jan, Vallier, Alexis, Sardain, Jad
Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalman Filter, achieve excellent performance in reconstructing the prompt
Externí odkaz:
http://arxiv.org/abs/2203.08800
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:
Lazar, Alina, Ju, Xiangyang, Murnane, Daniel, Calafiura, Paolo, Farrell, Steven, Xu, Yaoyuan, Spiropulu, Maria, Vlimant, Jean-Roch, Cerati, Giuseppe, Gray, Lindsey, Klijnsma, Thomas, Kowalkowski, Jim, Atkinson, Markus, Neubauer, Mark, DeZoort, Gage, Thais, Savannah, Hsu, Shih-Chieh, Aurisano, Adam, Hewes, V, Ballow, Alexandra, Acharya, Nirajan, Wang, Chun-yi, Liu, Emma, Lucas, Alberto
Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconstruction problems in high energy physics, including particle tracking. The Exa.TrkX pipeline based on GNNs demonstrated promising performance in recons
Externí odkaz:
http://arxiv.org/abs/2202.06929
Autor:
Deiana, Allison McCarn, Tran, Nhan, Agar, Joshua, Blott, Michaela, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Hauck, Scott, Liu, Mia, Neubauer, Mark S., Ngadiuba, Jennifer, Ogrenci-Memik, Seda, Pierini, Maurizio, Aarrestad, Thea, Bahr, Steffen, Becker, Jurgen, Berthold, Anne-Sophie, Bonventre, Richard J., Bravo, Tomas E. Muller, Diefenthaler, Markus, Dong, Zhen, Fritzsche, Nick, Gholami, Amir, Govorkova, Ekaterina, Hazelwood, Kyle J, Herwig, Christian, Khan, Babar, Kim, Sehoon, Klijnsma, Thomas, Liu, Yaling, Lo, Kin Ho, Nguyen, Tri, Pezzullo, Gianantonio, Rasoulinezhad, Seyedramin, Rivera, Ryan A., Scholberg, Kate, Selig, Justin, Sen, Sougata, Strukov, Dmitri, Tang, William, Thais, Savannah, Unger, Kai Lukas, Vilalta, Ricardo, Krosigk, Belinavon, Warburton, Thomas K., Flechas, Maria Acosta, Aportela, Anthony, Calvet, Thomas, Cristella, Leonardo, Diaz, Daniel, Doglioni, Caterina, Galati, Maria Domenica, Khoda, Elham E, Fahim, Farah, Giri, Davide, Hawks, Benjamin, Hoang, Duc, Holzman, Burt, Hsu, Shih-Chieh, Jindariani, Sergo, Johnson, Iris, Kansal, Raghav, Kastner, Ryan, Katsavounidis, Erik, Krupa, Jeffrey, Li, Pan, Madireddy, Sandeep, Marx, Ethan, McCormack, Patrick, Meza, Andres, Mitrevski, Jovan, Mohammed, Mohammed Attia, Mokhtar, Farouk, Moreno, Eric, Nagu, Srishti, Narayan, Rohin, Palladino, Noah, Que, Zhiqiang, Park, Sang Eon, Ramamoorthy, Subramanian, Rankin, Dylan, Rothman, Simon, Sharma, Ashish, Summers, Sioni, Vischia, Pietro, Vlimant, Jean-Roch, Weng, Olivia
Publikováno v:
Front. Big Data 5, 787421 (2022)
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
Externí odkaz:
http://arxiv.org/abs/2110.13041
Autor:
Ju, Xiangyang, Murnane, Daniel, Calafiura, Paolo, Choma, Nicholas, Conlon, Sean, Farrell, Steve, Xu, Yaoyuan, Spiropulu, Maria, Vlimant, Jean-Roch, Aurisano, Adam, Hewes, V, Cerati, Giuseppe, Gray, Lindsey, Klijnsma, Thomas, Kowalkowski, Jim, Atkinson, Markus, Neubauer, Mark, DeZoort, Gage, Thais, Savannah, Chauhan, Aditi, Schuy, Alex, Hsu, Shih-Chieh, Ballow, Alex, Lazar, and Alina
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline
Externí odkaz:
http://arxiv.org/abs/2103.06995
Autor:
Rankin, Dylan Sheldon, Krupa, Jeffrey, Harris, Philip, Flechas, Maria Acosta, Holzman, Burt, Klijnsma, Thomas, Pedro, Kevin, Tran, Nhan, Hauck, Scott, Hsu, Shih-Chieh, Trahms, Matthew, Lin, Kelvin, Lou, Yu, Ho, Ta-Wei, Duarte, Javier, Liu, Mia
Publikováno v:
2020 IEEE/ACM International Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC), 2020, pp. 38-47
Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically as-a-service computing, has the potential for significant gains over tr
Externí odkaz:
http://arxiv.org/abs/2010.08556
Autor:
Krupa, Jeffrey, Lin, Kelvin, Flechas, Maria Acosta, Dinsmore, Jack, Duarte, Javier, Harris, Philip, Hauck, Scott, Holzman, Burt, Hsu, Shih-Chieh, Klijnsma, Thomas, Liu, Mia, Pedro, Kevin, Rankin, Dylan, Suaysom, Natchanon, Trahms, Matt, Tran, Nhan
Publikováno v:
Mach. Learn.: Sci. Technol. 2 (2021) 035005
In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limited. At the CERN Large Hadron Collider (LHC), these two issues will co
Externí odkaz:
http://arxiv.org/abs/2007.10359
Autor:
Choma, Nicholas, Murnane, Daniel, Ju, Xiangyang, Calafiura, Paolo, Conlon, Sean, Farrell, Steven, Prabhat, Cerati, Giuseppe, Gray, Lindsey, Klijnsma, Thomas, Kowalkowski, Jim, Spentzouris, Panagiotis, Vlimant, Jean-Roch, Spiropulu, Maria, Aurisano, Adam, Hewes, V, Tsaris, Aristeidis, Terao, Kazuhiro, Usher, Tracy
To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event
Externí odkaz:
http://arxiv.org/abs/2007.00149
Autor:
Ju, Xiangyang, Farrell, Steven, Calafiura, Paolo, Murnane, Daniel, Prabhat, Gray, Lindsey, Klijnsma, Thomas, Pedro, Kevin, Cerati, Giuseppe, Kowalkowski, Jim, Perdue, Gabriel, Spentzouris, Panagiotis, Tran, Nhan, Vlimant, Jean-Roch, Zlokapa, Alexander, Pata, Joosep, Spiropulu, Maria, An, Sitong, Aurisano, Adam, Hewes, V, Tsaris, Aristeidis, Terao, Kazuhiro, Usher, Tracy
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy
Externí odkaz:
http://arxiv.org/abs/2003.11603