Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Aurisano Adam"'
Autor:
Hewes, V, Aurisano, Adam, Cerati, Giuseppe, Kowalkowski, Jim, Lee, Claire, Liao, Wei-keng, Grzenda, Daniel, Gumpula, Kaushal, Zhang, Xiaohe
Liquid Argon Time Projection Chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its full potential requires sophisticated automated reconstruction technique
Externí odkaz:
http://arxiv.org/abs/2403.11872
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
Quantitative cancer image analysis relies on the accurate delineation of tumours, a very specialised and time-consuming task. For this reason, methods for automated segmentation of tumours in medical imaging have been extensively developed in recent
Externí odkaz:
http://arxiv.org/abs/2212.02854
Autor:
Aurisano, Adam, Whitehead, Leigh H.
Publikováno v:
Artificial Intelligence for High Energy Physics, P. Calafiura, D. Rousseau and K. Terao, eds, pp. 313-353, World Scientific Publishing 2022
End-to-end analyses of data from high-energy physics experiments using machine and deep learning techniques have emerged in recent years. These analyses use deep learning algorithms to go directly from low-level detector information directly to high-
Externí odkaz:
http://arxiv.org/abs/2208.03285
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:
Abraham, Roshan Mammen, Alvarez-Muñiz, Jaime, Argüelles, Carlos A., Ariga, Akitaka, Ariga, Tomoko, Aurisano, Adam, Autiero, Dario, Bishai, Mary, Bostan, Nilay, Bustamante, Mauricio, Cummings, Austin, Decoene, Valentin, de Gouvêa, André, De Lellis, Giovanni, De Roeck, Albert, Denton, Peter B., Di Crescenzo, Antonia, Diwan, Milind V., Farzan, Yasaman, Fedynitch, Anatoli, Feng, Jonathan L., Fields, Laura J., Garcia, Alfonso, Garzelli, Maria Vittoria, Gehrlein, Julia, Glaser, Christian, Grzelak, Katarzyna, Hallmann, Steffen, Hewes, V, Indumathi, D., Ismail, Ahmed, Jana, Sudip, Jeong, Yu Seon, Kelly, Kevin J., Klein, Spencer R., Kling, Felix, Kosc, Thomas, Kose, Umut, Koskinen, D. Jason, Krizmanic, John, Lazar, Jeff, Li, Yichen, Martinez-Soler, Ivan, Mocioiu, Irina, Nam, Jiwoo, Niess, Valentin, Otte, Nepomuk, Patel, Sameer, Petti, Roberto, Prechelt, Remy L., Prohira, Steven, Rajaoalisoa, Miriama, Reno, Mary Hall, Safa, Ibrahim, Sarasty-Segura, Carlos, Senthil, R. Thiru, Stachurska, Juliana, Tomalak, Oleksandr, Trojanowski, Sebastian, Wendell, Roger Alexandre, Williams, Dawn, Wissel, Stephanie, Yaeggy, Barbara, Zas, Enrique, Zhelnin, Pavel, Zhu, Jing-yu
Publikováno v:
J. Phys. G: Nucl. Part. Phys. 49, 11 (2022)
Tau neutrinos are the least studied particle in the Standard Model. This whitepaper discusses the current and expected upcoming status of tau neutrino physics with attention to the broad experimental and theoretical landscape spanning long-baseline,
Externí odkaz:
http://arxiv.org/abs/2203.05591
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:
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:
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