Zobrazeno 1 - 10
of 334
pro vyhledávání: '"Nachman, Benjamin P."'
Autor:
Geuskens, Joep, Gite, Nishank, Krämer, Michael, Mikuni, Vinicius, Mück, Alexander, Nachman, Benjamin, Reyes-González, Humberto
Identifying the origin of high-energy hadronic jets ('jet tagging') has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders and are complex objects that serve as prototypical examples of collec
Externí odkaz:
http://arxiv.org/abs/2411.02628
Autor:
Butter, Anja, Diefenbacher, Sascha, Huetsch, Nathan, Mikuni, Vinicius, Nachman, Benjamin, Schweitzer, Sofia Palacios, Plehn, Tilman
Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph a starting simulation into the unfolded data. We show how to extend two morphing techniques, Schr\"odinger Bridges and Di
Externí odkaz:
http://arxiv.org/abs/2411.02495
Autor:
Heller, Nick, Ilten, Phil, Menzo, Tony, Mrenna, Stephen, Nachman, Benjamin, Siodmok, Andrzej, Szewc, Manuel, Youssef, Ahmed
We present an autodifferentiable rejection sampling algorithm termed Rejection Sampling with Autodifferentiation (RSA). In conjunction with reweighting, we show that RSA can be used for efficient parameter estimation and model exploration. Additional
Externí odkaz:
http://arxiv.org/abs/2411.02194
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:
Bhimji, Wahid, Calafiura, Paolo, Chakkappai, Ragansu, Chang, Po-Wen, Chou, Yuan-Tang, Diefenbacher, Sascha, Dudley, Jordan, Farrell, Steven, Ghosh, Aishik, Guyon, Isabelle, Harris, Chris, Hsu, Shih-Chieh, Khoda, Elham E, Lyscar, Rémy, Michon, Alexandre, Nachman, Benjamin, Nugent, Peter, Reymond, Mathis, Rousseau, David, Sluijter, Benjamin, Thorne, Benjamin, Ullah, Ihsan, Zhang, Yulei
The FAIR Universe -- HiggsML Uncertainty Challenge focuses on measuring the physics properties of elementary particles with imperfect simulators due to differences in modelling systematic errors. Additionally, the challenge is leveraging a large-comp
Externí odkaz:
http://arxiv.org/abs/2410.02867
Autor:
Zhu, Huanbiao, Desai, Krish, Kuusela, Mikael, Mikuni, Vinicius, Nachman, Benjamin, Wasserman, Larry
In many experimental contexts, it is necessary to statistically remove the impact of instrumental effects in order to physically interpret measurements. This task has been extensively studied in particle physics, where the deconvolution task is calle
Externí odkaz:
http://arxiv.org/abs/2409.10421
We introduce Resonant Anomaly Detection with Optimal Transport (RAD-OT), a method for generating signal templates in resonant anomaly detection searches. RAD-OT leverages the fact that the conditional probability density of the target features vary a
Externí odkaz:
http://arxiv.org/abs/2407.19818
Publikováno v:
Phys. Rev. D 110, 116013 Published 13 December, 2024
Deconvolving ("unfolding'') detector distortions is a critical step in the comparison of cross section measurements with theoretical predictions in particle and nuclear physics. However, most existing approaches require histogram binning while many t
Externí odkaz:
http://arxiv.org/abs/2407.11284
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
Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics. Higgs pair production directly probes the Higgs self-coupling and should be observed in the near future at the High-Luminosity LHC. We expl
Externí odkaz:
http://arxiv.org/abs/2405.15847