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Электронный каталог: Hayrapetyan, A. - Reweighting Simulated Events Using Machine-Learning Techniques in the CMS Experiment
Hayrapetyan, A. - Reweighting Simulated Events Using Machine-Learning Techniques in the CMS Experiment
Статья
Автор: Hayrapetyan, A.
The European Physical Journal C: Reweighting Simulated Events Using Machine-Learning Techniques in the CMS Experiment
б.г.
ISBN отсутствует
Автор: Hayrapetyan, A.
The European Physical Journal C: Reweighting Simulated Events Using Machine-Learning Techniques in the CMS Experiment
б.г.
ISBN отсутствует
На полку
Статья
Hayrapetyan, A.
Reweighting Simulated Events Using Machine-Learning Techniques in the CMS Experiment / A.Hayrapetyan, S.Afanasiev, V.Alexakhin, D.Budkouski, I.Golutvin, I.Gorbunov, V.Karjavine, V.Korenkov, N.Krasnikov, A.Lanev, A.Malakhov, V.Matveev, V.Palichik, V.Perelygin, S.Polikarpov, M.Savina, V.Shalaev, S.Shmatov, S.Shulha, V.Smirnov, O.Teryaev, N.Voytishin, B.S.Yuldashev, A.Zarubin, I.Zhizhin, Z.Tsamalaidze, [CMS Collab.]. – Text : electronic // The European Physical Journal C. – 2025. – Vol. 85, No. 5. – P. 495. – URL: https://doi.org/10.1140/epjc/s10052-025-14097-x. – Bibliogr.: 90.
Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a geant-based simulation of the detectors are used to produce large samples of simulated events for analysis by the LHC experiments. These simulations come at a high computational cost, where the detector simulation and reconstruction algorithms have the largest CPU demands. This article describes how machine-learning (ML) techniques are used to reweight simulated samples obtained with a given set of parameters to samples with different parameters or samples obtained from entirely different simulation programs. The ML reweighting method avoids the need for simulating the detector response multiple times by incorporating the relevant information in a single sample through event weights. Results are presented for reweighting to model variations and higher-order calculations in simulated top quark pair production at the LHC. This ML-based reweighting is an important element of the future computing model of the CMS experiment and will facilitate precision measurements at the High-Luminosity LHC.
ОИЯИ = ОИЯИ (JINR)2025
Спец.(статьи,препринты) = Ц 840 в - Программы обработки экспериментальных данных и управление физическими установками$
Спец.(статьи,препринты) = Ц 849 - Искусственный интеллект. Теория и практика
Hayrapetyan, A.
Reweighting Simulated Events Using Machine-Learning Techniques in the CMS Experiment / A.Hayrapetyan, S.Afanasiev, V.Alexakhin, D.Budkouski, I.Golutvin, I.Gorbunov, V.Karjavine, V.Korenkov, N.Krasnikov, A.Lanev, A.Malakhov, V.Matveev, V.Palichik, V.Perelygin, S.Polikarpov, M.Savina, V.Shalaev, S.Shmatov, S.Shulha, V.Smirnov, O.Teryaev, N.Voytishin, B.S.Yuldashev, A.Zarubin, I.Zhizhin, Z.Tsamalaidze, [CMS Collab.]. – Text : electronic // The European Physical Journal C. – 2025. – Vol. 85, No. 5. – P. 495. – URL: https://doi.org/10.1140/epjc/s10052-025-14097-x. – Bibliogr.: 90.
Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a geant-based simulation of the detectors are used to produce large samples of simulated events for analysis by the LHC experiments. These simulations come at a high computational cost, where the detector simulation and reconstruction algorithms have the largest CPU demands. This article describes how machine-learning (ML) techniques are used to reweight simulated samples obtained with a given set of parameters to samples with different parameters or samples obtained from entirely different simulation programs. The ML reweighting method avoids the need for simulating the detector response multiple times by incorporating the relevant information in a single sample through event weights. Results are presented for reweighting to model variations and higher-order calculations in simulated top quark pair production at the LHC. This ML-based reweighting is an important element of the future computing model of the CMS experiment and will facilitate precision measurements at the High-Luminosity LHC.
ОИЯИ = ОИЯИ (JINR)2025
Спец.(статьи,препринты) = Ц 840 в - Программы обработки экспериментальных данных и управление физическими установками$
Спец.(статьи,препринты) = Ц 849 - Искусственный интеллект. Теория и практика
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