Zobrazeno 1 - 10
of 1 184
pro vyhledávání: '"Käch A"'
Autor:
Wohlwend, Jelena, Wipf, Oliver, Kiwic, David, Käch, Siro, Mächler, Benjamin, Haberfehlner, Georg, Spolenak, Ralph, Galinski, Henning
Plasmons can drive chemical reactions by directly exciting intramolecular transitions. However, strong coupling of plasmons to single molecules remains a challenge as ultra-small mode volumes are required. In the presented work, we propose Cu-Pd plas
Externí odkaz:
http://arxiv.org/abs/2410.22986
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
Collider data generation with machine learning has become increasingly popular in particle physics due to the high computational cost of conventional Monte Carlo simulations, particularly for future high-luminosity colliders. We propose a generative
Externí odkaz:
http://arxiv.org/abs/2408.04997
In High Energy Physics, detailed and time-consuming simulations are used for particle interactions with detectors. To bypass these simulations with a generative model, the generation of large point clouds in a short time is required, while the comple
Externí odkaz:
http://arxiv.org/abs/2311.12616
Autor:
Käch, Benno, Melzer-Pellmann, Isabell
The generation of collider data using machine learning has emerged as a prominent research topic in particle physics due to the increasing computational challenges associated with traditional Monte Carlo simulation methods, particularly for future co
Externí odkaz:
http://arxiv.org/abs/2305.15254
Autor:
Lionel Wettstein, Julia Specht, Vera Kesselring, Leif Sieben, Yanlin Pan, Daniel Käch, Dominika Baster, Frank Krumeich, Mario El Kazzi, Máté J. Bezdek
Publikováno v:
Advanced Science, Vol 11, Iss 43, Pp n/a-n/a (2024)
Abstract Sensors that can accurately assess oxygen (O2) concentrations in real time are crucial for a wide range of applications spanning personal health monitoring, environmental protection, and industrial process development. Here a high‐performa
Externí odkaz:
https://doaj.org/article/8b167bd464e74242adb384aab8150100
Autor:
Käch, Benno, Krücker, Dirk, Melzer-Pellmann, Isabell, Scham, Moritz, Schnake, Simon, Verney-Provatas, Alexi
Fast data generation based on Machine Learning has become a major research topic in particle physics. This is mainly because the Monte Carlo simulation approach is computationally challenging for future colliders, which will have a significantly high
Externí odkaz:
http://arxiv.org/abs/2211.13630
Data generation based on Machine Learning has become a major research topic in particle physics. This is due to the current Monte Carlo simulation approach being computationally challenging for future colliders, which will have a significantly higher
Externí odkaz:
http://arxiv.org/abs/2211.13623
Autor:
Gantenbein, Silvan, Colucci, Emanuele, Käch, Julian, Trachsel, Etienne, Coulter, Fergal B., Rühs, Patrick A., Masania, Kunal, Studart, André R.
Biological living materials, such as animal bones and plant stems, are able to self-heal, regenerate, adapt and make decisions under environmental pressures. Despite recent successful efforts to imbue synthetic materials with some of these remarkable
Externí odkaz:
http://arxiv.org/abs/2203.00976
Autor:
Maxwell, Matthew B., Hom-Tedla, Marianne S., Yi, Jawoon, Li, Shitian, Rivera, Samuel A., Yu, Jingting, Burns, Mannix J., McRae, Helen M., Stevenson, Braden T., Coakley, Katherine E., Ho, Josephine, Gastelum, Kameneff Bojorquez, Bell, Joshua C., Jones, Alexander C., Eskander, Ramez N., Dykhuizen, Emily C., Shadel, Gerald S., Kaech, Susan M., Hargreaves, Diana C.
Publikováno v:
In Cell 20 June 2024 187(13):3390-3408