Zobrazeno 1 - 10
of 1 546
pro vyhledávání: '"Chernyavskaya Nadezda"'
Autor:
Raikwar Piyush, Cardoso Renato, Chernyavskaya Nadezda, Jaruskova Kristina, Pokorski Witold, Salamani Dalila, Srivatsa Mudhakar, Tsolaki Kalliopi, Vallecorsa Sofia, Zaborowska Anna
Publikováno v:
EPJ Web of Conferences, Vol 295, p 09039 (2024)
Recently, transformer-based foundation models have proven to be a generalized architecture applicable to various data modalities, ranging from text to audio and even a combination of multiple modalities. Transformers by design should accurately model
Externí odkaz:
https://doaj.org/article/38aa2902bcb54c9d8df76ee9dcd98398
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:
Orzari, Breno, Chernyavskaya, Nadezda, Cobe, Raphael, Duarte, Javier, Fialho, Jefferson, Gunopulos, Dimitrios, Kansal, Raghav, Pierini, Maurizio, Tomei, Thiago, Touranakou, Mary
Publikováno v:
Mach. Learn.: Sci. Technol. 4 045023 (2023)
In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. However, becaus
Externí odkaz:
http://arxiv.org/abs/2310.13138
Autor:
Pol, Adrian Alan, Govorkova, Ekaterina, Gronroos, Sonja, Chernyavskaya, Nadezda, Harris, Philip, Pierini, Maurizio, Ojalvo, Isobel, Elmer, Peter
Unsupervised deep learning techniques are widely used to identify anomalous behaviour. The performance of such methods is a product of the amount of training data and the model size. However, the size is often a limiting factor for the deployment on
Externí odkaz:
http://arxiv.org/abs/2310.06047
Autor:
Chhibra, Simranjit Singh, Chernyavskaya, Nadezda, Maier, Benedikt, Pierini, Maurzio, Hasan, Syed
Publikováno v:
Eur. Phys. J. Plus 139, 281 (2024)
Confining dark sectors with pseudo-conformal dynamics can produce Soft Unclustered Energy Patterns (SUEP), at the Large Hadron Collider: the production of dark quarks in proton-proton collisions leading to a dark shower and the high-multiplicity prod
Externí odkaz:
http://arxiv.org/abs/2306.13595
Autor:
Anzalone, Luca, Chhibra, Simranjit Singh, Maier, Benedikt, Chernyavskaya, Nadezda, Pierini, Maurizio
Publikováno v:
Mach. Learn.: Sci. Technol. 5 (2024) 035064
We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types of particles and interactions, are considered in our study as anomalie
Externí odkaz:
http://arxiv.org/abs/2306.12955
More than a thousand 8" silicon sensors will be visually inspected to look for anomalies on their surface during the quality control preceding assembly into the High-Granularity Calorimeter for the CMS experiment at CERN. A deep learning-based algori
Externí odkaz:
http://arxiv.org/abs/2303.15319
Autor:
Chernyavskaya, Nadezda
An overview of the recent searches for Higgs boson pair production at the LHC was presented. The searches were based on approximately 140 $\mathrm{fb}^{-1}$ of data collected by the ATLAS and CMS experiments in proton-proton collisions at $\sqrt{s}$
Externí odkaz:
http://arxiv.org/abs/2302.12631
Publikováno v:
Eur. Phys. J. C 83, 485 (2023)
There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection. Often these models are adapted from those designed for datasets in c
Externí odkaz:
http://arxiv.org/abs/2212.07347
Autor:
Kansal, Raghav, Li, Anni, Duarte, Javier, Chernyavskaya, Nadezda, Pierini, Maurizio, Orzari, Breno, Tomei, Thiago
Publikováno v:
Phys. Rev. D 107, 076017 (2023)
There has been a recent explosion in research into machine-learning-based generative modeling to tackle computational challenges for simulations in high energy physics (HEP). In order to use such alternative simulators in practice, we need well-defin
Externí odkaz:
http://arxiv.org/abs/2211.10295