Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；National Energy Research Scientific Computing Center（国家能源研究科学计算中心）；Berkeley Institute for Data Science（伯克利数据科学研究所）；University of California, Berkeley（加州大学伯克利分校）；University of California, Riverside（加州大学河滨分校）；Thomas Jefferson National Accelerator Facility（托马斯·杰斐逊国家加速器设施）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）
Fernando Torales Acosta, Vinicius Mikuni, Benjamin Nachman, Miguel Arratia, Bishnu Karki, Ryan Milton, Piyush Karande, Aaron Angerami
Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared.
A universal vision transformer for fast calorimeter simulations
Centre for Cosmology, Particle Physics（宇宙学、粒子物理与现象学研究中心）；Marietta Blau Institute for Particle Physics (MBI Vienna), Austrian Academy of Sciences (ÖAW), Austria（玛丽埃塔·布劳粒子物理研究所（MBI维也纳），奥地利科学院（ÖAW），奥地利）
Score-based Generative Models for Calorimeter Shower Simulation
National Energy Research Scientific Computing Center（国家能源研究科学计算中心）；Berkeley Lab（伯克利实验室）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Berkeley Institute for Data Science（伯克利数据科学研究所）；University of California, Berkeley（加州大学伯克利分校）
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）
Configurable calorimeter simulation for AI applications
University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；INFN and University of Genova（意大利国家核物理研究所与热那亚大学）；University of Luxembourg（卢森堡大学）；Weizmann Institue of Science（魏茨曼科学研究所）；New York University（纽约大学）；ICEPP, University of Tokyo（东京大学国际基本粒子物理研究所）；Technical University of Munich（慕尼黑工业大学）；Max Planck Institute for Physics（马克斯·普朗克物理学研究所）；INFN and Sapienza University of Rome（意大利国家核物理研究所与罗马第一大学）
GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generation
William & Mary, Department of Data Science（威廉玛丽学院数据科学系）；William & Mary, Department of Physics（威廉玛丽学院物理系）；University of Glasgow, School of Physics and Astronomy（格拉斯哥大学物理与天文学学院）；Thomas Jefferson National Accelerator Facility（泰勒·杰弗里斯国家加速器设施）
CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
School of Physics and Astronomy, University of Edinburgh（爱丁堡大学物理与天文学学院）；Department of Physics, University of Wisconsin-Madison（威斯康星大学麦迪逊分校物理系）；Fermi National Accelerator Laboratory（费米国家加速器实验室）；State Key Laboratory of Dark Matter Physics, Tsung-Dao Lee Institute & School of Physics and Astronomy, Shanghai Jiao Tong University（上海交通大学暗物质物理国家重点实验室、李政道研究所及物理与天文学学院）；Key Laboratory for Particle Astrophysics and Cosmology (MOE) & Shanghai Key Laboratory for Particle Physics and Cosmology, Shanghai Jiao Tong University（教育部粒子天体物理与宇宙学重点实验室及上海粒子物理与宇宙学重点实验室，上海交通大学）
Firmware implementation of a recurrent neural network for the computation of the energy deposited in the liquid argon calorimeter of the ATLAS experiment
Perimeter Institute for Theoretical Physics（理论物理圆周研究所）；Faculty of Mathematics, University of Waterloo（滑铁卢大学数学学院）；Department of Physics and Astronomy, University of British Columbia（不列颠哥伦比亚大学物理与天文学系）；TRIUMF（TRIUMF（加拿大国家粒子加速器中心））；Department of Physics, Simon Fraser University（西蒙菲莎大学物理系）
Contrastive Metric Learning for Point Cloud Segmentation in Highly Granular Detectors
Blackett Laboratory, Imperial College London, UK（帝国理工学院伦敦学院布莱克特实验室）；Brown Center for Theoretical Physics and Innovation (BCTPI), Brown University, USA（布朗大学理论物理与创新中心）
Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
Univ. of Chicago（芝加哥大学）；European Organization for Nuclear Research (CERN)（欧洲核子研究组织）；Univ. of Texas Arlington（德克萨斯大学阿灵顿分校）；Univ. of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；UET Peshawar（白沙瓦工程技术大学）；Fermi National Accelerator Laboratory（费米国家加速器实验室）；Univ. of California, San Diego（加利福尼亚大学圣迭戈分校）；Univ. of Helsinki（赫尔辛基大学）；California Institute of Technology（加州理工学院）
