Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Buhmann Erik"'
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
We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizing flow (CNF) trained with the flow matching technique. It is conditio
Externí odkaz:
http://arxiv.org/abs/2312.00123
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, Ewen, Cedric, Kasieczka, Gregor, Mikuni, Vinicius, Nachman, Benjamin, Shih, David
Publikováno v:
Phys. Rev. D 109, 055015 (2024)
Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strategies for resonant anomaly detection have been developed, where sideband
Externí odkaz:
http://arxiv.org/abs/2310.06897
Autor:
Buhmann, Erik, Ewen, Cedric, Faroughy, Darius A., Golling, Tobias, Kasieczka, Gregor, Leigh, Matthew, Quétant, Guillaume, Raine, John Andrew, Sengupta, Debajyoti, Shih, David
Jets at the LHC, typically consisting of a large number of highly correlated particles, are a fascinating laboratory for deep generative modeling. In this paper, we present two novel methods that generate LHC jets as point clouds efficiently and accu
Externí odkaz:
http://arxiv.org/abs/2310.00049
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
Publikováno v:
SciPost Phys. 15, 130 (2023)
With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far th
Externí odkaz:
http://arxiv.org/abs/2301.08128
Autor:
Buhmann, Erik, Diefenbacher, Sascha, Eren, Engin, Gaede, Frank, Hundhausen, Daniel, Kasieczka, Gregor, Korcari, William, Krüger, Katja, McKeown, Peter, Rustige, Lennart
Motivated by the computational limitations of simulating interactions of particles in highly-granular detectors, there exists a concerted effort to build fast and exact machine-learning-based shower simulators. This work reports progress on two impor
Externí odkaz:
http://arxiv.org/abs/2112.09709