Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Korol Anatolii"'
Autor:
Krause, Claudius, Giannelli, Michele Faucci, Kasieczka, Gregor, Nachman, Benjamin, Salamani, Dalila, Shih, David, Zaborowska, Anna, Amram, Oz, Borras, Kerstin, Buckley, Matthew R., Buhmann, Erik, Buss, Thorsten, Cardoso, Renato Paulo Da Costa, Caterini, Anthony L., Chernyavskaya, Nadezda, Corchia, Federico A. G., Cresswell, Jesse C., Diefenbacher, Sascha, Dreyer, Etienne, Ekambaram, Vijay, Eren, Engin, Ernst, Florian, Favaro, Luigi, Franchini, Matteo, Gaede, Frank, Gross, Eilam, Hsu, Shih-Chieh, Jaruskova, Kristina, Käch, Benno, Kalagnanam, Jayant, Kansal, Raghav, Kim, Taewoo, Kobylianskii, Dmitrii, Korol, Anatolii, Korcari, William, Krücker, Dirk, Krüger, Katja, Letizia, Marco, Li, Shu, Liu, Qibin, Liu, Xiulong, Loaiza-Ganem, Gabriel, Madula, Thandikire, McKeown, Peter, Melzer-Pellmann, Isabell-A., Mikuni, Vinicius, Nguyen, Nam, Ore, Ayodele, Schweitzer, Sofia Palacios, Pang, Ian, Pedro, Kevin, Plehn, Tilman, Pokorski, Witold, Qu, Huilin, Raikwar, Piyush, Raine, John A., Reyes-Gonzalez, Humberto, Rinaldi, Lorenzo, Ross, Brendan Leigh, Scham, Moritz A. W., Schnake, Simon, Shimmin, Chase, Shlizerman, Eli, Soybelman, Nathalie, Srivatsa, Mudhakar, Tsolaki, Kalliopi, Vallecorsa, Sofia, Yeo, Kyongmin, Zhang, Rui
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few t
Externí odkaz:
http://arxiv.org/abs/2410.21611
Autor:
Buhmann Erik, Diefenbacher Sascha, Eren Engin, Gaede Frank, Kasieczka Gregor, Korol Anatolii, Krüger Katja
Publikováno v:
EPJ Web of Conferences, Vol 251, p 03003 (2021)
Given the increasing data collection capabilities and limited computing resources of future collider experiments, interest in using generative neural networks for the fast simulation of collider events is growing. In our previous study, the Bounded I
Externí odkaz:
https://doaj.org/article/51e80560737a44ff9a9fe23520c2e72c
Autor:
Buhmann Erik, Diefenbacher Sascha, Eren Engin, Gaede Frank, Hundhausen Daniel, Kasieczka Gregor, Korcari William, Korol Anatolii, Krüger Katja, McKeown Peter, Rustige Lennart
Publikováno v:
EPJ Web of Conferences, Vol 251, p 03049 (2021)
Generative machine learning models offer a promising way to efficiently amplify classical Monte Carlo generators’ statistics for event simulation and generation in particle physics. Given the already high computational cost of simulation and the ex
Externí odkaz:
https://doaj.org/article/38f31f0fb1b24850a830ab78546748b2
Autor:
Buhmann, Erik, Gaede, Frank, Kasieczka, Gregor, Korol, Anatolii, Korcari, William, Krüger, Katja, McKeown, Peter
Fast simulation of the energy depositions in high-granular detectors is needed for future collider experiments with ever-increasing luminosities. Generative machine learning (ML) models have been shown to speed up and augment the traditional simulati
Externí odkaz:
http://arxiv.org/abs/2309.05704
Autor:
Buhmann, Erik, Diefenbacher, Sascha, Eren, Engin, Gaede, Frank, Kasieczka, Gregor, Korol, Anatolii, Korcari, William, Krüger, Katja, McKeown, Peter
Publikováno v:
JINST 18 (2023) 11, P11025
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models would enable them to augment traditio
Externí odkaz:
http://arxiv.org/abs/2305.04847
Autor:
Diefenbacher, Sascha, Eren, Engin, Gaede, Frank, Kasieczka, Gregor, Korol, Anatolii, Krüger, Katja, McKeown, Peter, Rustige, Lennart
The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools. Machine learning based generative models offer a solution that is both fast and accura
Externí odkaz:
http://arxiv.org/abs/2303.18150
Autor:
Diefenbacher, Sascha, Eren, Engin, Kasieczka, Gregor, Korol, Anatolii, Nachman, Benjamin, Shih, David
Significant advances in deep learning have led to more widely used and precise neural network-based generative models such as Generative Adversarial Networks (GANs). We introduce a post-hoc correction to deep generative models to further improve thei
Externí odkaz:
http://arxiv.org/abs/2009.03796
Autor:
Buhmann, Erik, Diefenbacher, Sascha, Eren, Engin, Gaede, Frank, Kasieczka, Gregor, Korol, Anatolii, Krüger, Katja
Publikováno v:
Computing and Software for Big Science 5, 13 (2021)
Accurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calorimeter detectors is a time consuming process and drives the computing n
Externí odkaz:
http://arxiv.org/abs/2005.05334
Akademický článek
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Autor:
McKeown, Peter, Gaede, Frank, Krüger, Katja, Eren, Engin, Korol, Anatolii, Rustige, Lennart, Bieringer, Sebastian Guido, Buhmann, Erik, Diefenbacher, Sascha Daniel, Kasieczka, Gregor, Korcari, William, Shekhzadeh, Imahn
Publikováno v:
41st International Conference on High Energy Physics, ICHEP2022, Bologna, Italy, 2022-07-06-2022-07-13
Proceedings of Science / International School for Advanced Studies (ICHEP2022), 236 (2023). doi:10.22323/1.414.0236
Proceedings of Science / International School for Advanced Studies (ICHEP2022), 236 (2023). doi:10.22323/1.414.0236
41st International Conference on High Energy Physics, ICHEP2022, Bologna, Italy, 6 Jul 2022 - 13 Jul 2022; Proceedings of Science / International School for Advanced Studies (ICHEP2022), 236 (2022). doi:10.22323/1.414.0236
While simulation is a
While simulation is a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c194562fbf9836b5898d5a73f7a1ac2
https://bib-pubdb1.desy.de/record/490323
https://bib-pubdb1.desy.de/record/490323