Zobrazeno 1 - 10
of 2 799
pro vyhledávání: '"Kasieczka Gregor"'
Recognizing symmetries in data allows for significant boosts in neural network training, which is especially important where training data are limited. In many cases, however, the exact underlying symmetry is present only in an idealized dataset, and
Externí odkaz:
http://arxiv.org/abs/2412.18773
Autor:
Amram, Oz, Anzalone, Luca, Birk, Joschka, Faroughy, Darius A., Hallin, Anna, Kasieczka, Gregor, Krämer, Michael, Pang, Ian, Reyes-Gonzalez, Humberto, Shih, David
Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks. This work demonstrates how data collected by the CMS experiment at the Large Hadron Collid
Externí odkaz:
http://arxiv.org/abs/2412.10504
Autor:
Das, Ranit, Finke, Thorben, Hein, Marie, Kasieczka, Gregor, Krämer, Michael, Mück, Alexander, Shih, David
Resonant anomaly detection methods have great potential for enhancing the sensitivity of traditional bump hunt searches. A key component of these methods is a high quality background template used to produce an anomaly score. Using the LHC Olympics R
Externí odkaz:
http://arxiv.org/abs/2411.00085
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
Publikováno v:
2024 Mach. Learn.: Sci. Technol. 5 045044
Recently, combinations of generative and Bayesian machine learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural networks aim to quantify the uncertainty on the generated distribution ori
Externí odkaz:
http://arxiv.org/abs/2408.00838
Autor:
Hallin, Anna, Kasieczka, Gregor, Kraml, Sabine, Lessa, André, Moureaux, Louis, von Schwartz, Tore, Shih, David
We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (latent) space while conserving information about the relationship betwe
Externí odkaz:
http://arxiv.org/abs/2407.20315
Publikováno v:
2024 JINST 19 P09003
In the quest to build generative surrogate models as computationally efficient alternatives to rule-based simulations, the quality of the generated samples remains a crucial frontier. So far, normalizing flows have been among the models with the best
Externí odkaz:
http://arxiv.org/abs/2405.20407
Autor:
Li, Congqiao, Agapitos, Antonios, Drews, Jovin, Duarte, Javier, Fu, Dawei, Gao, Leyun, Kansal, Raghav, Kasieczka, Gregor, Moureaux, Louis, Qu, Huilin, Suarez, Cristina Mantilla, Li, Qiang
The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging significantly enhances the sensitivity of dedicated searches, it is limited t
Externí odkaz:
http://arxiv.org/abs/2405.12972
We propose the first-ever complete, model-agnostic search strategy based on the optimal anomaly score, for new physics on the tails of distributions. Signal sensitivity is achieved via a classifier trained on auxiliary features in a weakly-supervised
Externí odkaz:
http://arxiv.org/abs/2404.07258
Publikováno v:
Mach. Learn.: Sci. Technol. 5 035031 (2024)
Foundation models are multi-dataset and multi-task machine learning methods that once pre-trained can be fine-tuned for a large variety of downstream applications. The successful development of such general-purpose models for physics data would be a
Externí odkaz:
http://arxiv.org/abs/2403.05618