Zobrazeno 1 - 10
of 1 310
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
Autor:
Ballestrero Alessandro, Bellan Riccardo, Biedermann Benedikt, Bittrich Carsten, Brivio Ilaria, Cardini Andrea, Gomez-Ceballos Guillelmo, Charlot Claude, Ciulli Vitaliano, Covarelli Roberto, Cuevas Javier, Denner Ansgar, Dittmaier Stefan, Di Ciaccio Lucia, Duric Senka, E. Jasper Gerard Lauwers, Farrington Sinead, Ferrari Pamela, Ferreira Silva Pedro, Finco Linda, Giljanović Duje, Glover Nigel, Gomez-Ambrosio Raquel, Gonella Giulia, Govoni Pietro, Goy Corinne, Gras Philippe, Grojean Christophe, Gross Eilam, Grossi Michele, Grunewald Martin, Helary Louis, Herrmann Tim, Herndon Matt, Hinzmann Andreas, Iltzsche Franziska, Jäger Barbara, Janssen Xavier, Kalinowski Jan, Karlberg Alexander, Kepka Oldrich, Kersevan Borut, Klute Markus, Kobel Michael, Koletsou Iro, Kordas Kostas, Lelas Damir, Lenzi Piergiulio, Li Qiang, Lohwasser Kristin, Long Kenneth, Lorenzo Martinez Narei, Lucrezia Stella Bruni, Maina Ezio, Manjarres Joany, Mariotti Chiara, Matthias Ulrich Mozer, Mildner Hannes, Mulders Martijn, Novak Jakob, Oleari Carlo, Paganoni Anna, Pellen Mathieu, Pelliccioli Giovanni, Petridou Chariclia, Pigard Philipp, Pleier Marc-Andre, Polesello Giacomo, Potamianos Karolos, Price Darren, Puljak Ivica, Rauch Michael, Rebuzzi Daniela, Reuter Jürgen, Riva Francesco, Rothe Vincent, Russo Lorenzo, Salerno Roberto, Sampsonidou Despoina, Sangalli Laura, Sauvan Emmanuel, Schumacher Markus, Schwan Christopher, Sekulla Marco, Selvaggi Michele, Siegert Frank, Slawinska Magdalena, Snoek Hella, Sommer Philip, Spannowsky Michael, Spanò Francesco, Stienemeier Pascal, Strandberg Jonas, Szleper Michał, Sznajder Andre, Todt Stefanie, Trott Michael, Tzamarias Spyros, Valsecchi Davide, Van Eijk Bob, Vicini Alessandro, Voutilainen Mikko, Vryonidou Eleni, Zanderighi Giulia, Zaro Marco, Zeppenfeld Dieter
Publikováno v:
Reviews in Physics, Vol 3, Iss , Pp 44-63 (2018)
This document summarises the talks and discussions happened during the VBSCan Split17 workshop, the first general meeting of the VBSCan COST Action network. This collaboration is aiming at a consistent and coordinated study of vector-boson scattering
Externí odkaz:
https://doaj.org/article/9c31debfd95d406fb58f54c4c56aabdb
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