In particle physics the simulation of particle transport through detectors requires an enormous amount of computational resources, utilizing more than 50% of the resources of the CERN Worldwide Large Hadron Collider Grid. This challenge has motivated the investigation of different, faster approaches for replacing the standard Monte Carlo simulations. Deep Learning Generative Adversarial Networks are among the most promising alternatives. Previous studies showed that they achieve the necessary level of accuracy while decreasing the simulation time by orders of magnitudes. In this paper we present a newly developed neural network architecture which reproduces a three-dimensional problem employing 2D convolutional layers and we compare its performance with an earlier architecture consisting of 3D convolutional layers. The performance evaluation relies on direct comparison to Monte Carlo simulations, in terms of different physics quantities usually employed to quantify the detector response. We prove that our new neural network architecture reaches a higher level of accuracy with respect to the 3D convolutional GAN while reducing the necessary computational resources. Calorimeters are among the most expensive detectors in terms of simulation time. Therefore we focus our study on an electromagnetic calorimeter prototype with a regular highly granular geometry, as an example of future calorimeters.
HybridMC: a fast detailed simulation framework for optical calorimeter technologies at the LHCb ECAL Upgrade II
Università degli Studi di Milano-Bicocca（米兰比可卡大学）；CERN（欧洲核子研究中心）；Peking University（北京大学）；INFN Sezione di Bologna（意大利国家核物理研究所博洛尼部分所）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
CsI(Tl) Pulse Shape Discrimination with the Belle II Electromagnetic Calorimeter as a Novel Method to Improve Particle Identification at Electron-Positron Colliders
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Institute of High Energy Physics (HEPHY), Austrian Academy of Sciences (OeAW)（高能物理研究所（HEPHY），奥地利科学院）；Department of Microtechnology and Nanoscience（微电子与纳米科学系）；Lawrence Berkeley National Laboratory（伯克利国家实验室）；CERN（欧洲核子研究中心）；NHETC, Department of Physics and Astronomy（NHETC，物理与天文学系）；Fermi National Accelerator Laboratory（费米国家加速器实验室）；III. Physikalisches Institut A, RWTH Aachen University（RWTH亚琛大学第三物理研究所）；Department of Physics and Astronomy, Alma Mater Studiorum - University of Bologna（博洛尼亚大学物理与天文学系）；Weizmann Institute of Science（魏茨曼科学研究所）；IBM Research, India（印度IBM研究院）；University of Washington（华盛顿大学）；IBM T. J. Watson Research Center（IBM T.J. Watson研究中）；California Institute of Technology（加州理工学院）；MaLGa--DIBRIS, University of Genova（热那亚大学MaLGa-DIBRIS）；Tsung-Dao Lee Institute (TDLI), Shanghai Jiao Tong University（李政道研究所（TDLI），上海交通大学）；Key Laboratory for Particle Astrophysics and Cosmology (MOE), Shanghai Key Laboratory for Particle Physics and Cosmology (SKLPPC), Shanghai Jiao Tong University（粒子天体物理与宇宙学重点实验室（MOE），上海粒子物理与宇宙学重点实验室（SKLPPC），上海交通大学）；Institute of Nuclear and Particle Physics, School of Physics and Astronomy, Shanghai Jiao Tong University（核物理与粒子物理研究所，上海交通大学物理与天文学院）；University College London (UCL)（伦敦大学学院（UCL））；Département de Physique Nucléaire et Corpusculaire, University of Geneva（日内瓦大学核物理与粒子物理系）；Department of Physics, University of Genova（热那亚大学物理系）；Yale University（耶鲁大学）
Machine Learning for Particle Flow Reconstruction at CMS
NICPB（国家化学物理研究所）；University of California San Diego（加州大学圣迭戈分校）；European Center for Nuclear Research (CERN)（欧洲核子研究中心）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；California Institute of Technology（加州理工学院）
