Zobrazeno 1 - 10
of 1 318
pro vyhledávání: '"Gross Eilam"'
Autor:
Kakati, Nilotpal, Dreyer, Etienne, Ivina, Anna, Di Bello, Francesco Armando, Heinrich, Lukas, Kado, Marumi, Gross, Eilam
In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based on learning hypergraphs (HGPflow) has previously been explored in the
Externí odkaz:
http://arxiv.org/abs/2410.23236
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
Accurately reconstructing particles from detector data is a critical challenge in experimental particle physics, where the spatial resolution of calorimeters has a crucial impact. This study explores the integration of super-resolution techniques int
Externí odkaz:
http://arxiv.org/abs/2409.16052
The reconstruction of particle tracks from hits in tracking detectors is a computationally intensive task due to the large combinatorics of detector signals. Recent efforts have proven that ML techniques can be successfully applied to the tracking pr
Externí odkaz:
http://arxiv.org/abs/2406.16752
Autor:
Dreyer, Etienne, Gross, Eilam, Kobylianskii, Dmitrii, Mikuni, Vinicius, Nachman, Benjamin, Soybelman, Nathalie
Detector simulation and reconstruction are a significant computational bottleneck in particle physics. We develop Particle-flow Neural Assisted Simulations (Parnassus) to address this challenge. Our deep learning model takes as input a point cloud (p
Externí odkaz:
http://arxiv.org/abs/2406.01620
Autor:
Kobylianskii, Dmitrii, Soybelman, Nathalie, Kakati, Nilotpal, Dreyer, Etienne, Nachman, Benjamin, Gross, Eilam
The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the continued development of machine learning techniques to serve as efficie
Externí odkaz:
http://arxiv.org/abs/2405.10106
Denoising diffusion models have gained prominence in various generative tasks, prompting their exploration for the generation of calorimeter responses. Given the computational challenges posed by detector simulations in high-energy physics experiment
Externí odkaz:
http://arxiv.org/abs/2402.11575
Autor:
Lu, Junjian, Liu, Siwei, Kobylianski, Dmitrii, Dreyer, Etienne, Gross, Eilam, Liang, Shangsong
In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors. However, the large combinatorial space of possible tree structures
Externí odkaz:
http://arxiv.org/abs/2402.11538
Autor:
Di Bello, Francesco Armando, Charkin-Gorbulin, Anton, Cranmer, Kyle, Dreyer, Etienne, Ganguly, Sanmay, Gross, Eilam, Heinrich, Lukas, Santi, Lorenzo, Kado, Marumi, Kakati, Nilotpal, Rieck, Patrick, Tusoni, Matteo
A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator. This open-source project is aimed to support the development of machine learning algorithms in
Externí odkaz:
http://arxiv.org/abs/2303.02101
Autor:
Di Bello, Francesco Armando, Dreyer, Etienne, Ganguly, Sanmay, Gross, Eilam, Heinrich, Lukas, Ivina, Anna, Kado, Marumi, Kakati, Nilotpal, Santi, Lorenzo, Shlomi, Jonathan, Tusoni, Matteo
Publikováno v:
Eur. Phys. J. C 83 (2023) 596
The task of reconstructing particles from low-level detector response data to predict the set of final state particles in collision events represents a set-to-set prediction task requiring the use of multiple features and their correlations in the in
Externí odkaz:
http://arxiv.org/abs/2212.01328