Miguel Arratia, Anja Butter, Mario Campanelli, Vincent Croft, Aishik Ghosh, Dag Gillberg, Kristin Lohwasser, Bogdan Malaescu, Vinicius Mikuni, Benjamin Nachman,… 展开作者
Miguel Arratia, Anja Butter, Mario Campanelli, Vincent Croft, Aishik Ghosh, Dag Gillberg, Kristin Lohwasser, Bogdan Malaescu, Vinicius Mikuni, Benjamin Nachman, Juan Rojo, Jesse Thaler, Ramon Winterhalder
备注：
23 pages, 4 figures; v2: Added a missing reference; v3: Added schematic diagram and extended several discussions
Machine learning tools have empowered a qualitatively new way to perform differential cross section measurements whereby the data are unbinned, possibly in many dimensions. Unbinned measurements can enable, improve, or at least simplify comparisons between experiments and with theoretical predictions. Furthermore, many-dimensional measurements can be used to define observables after the measurement instead of before. There is currently no community standard for publishing unbinned data. While there are also essentially no measurements of this type public, unbinned measurements are expected in the near future given recent methodological advances. The purpose of this paper is to propose a scheme for presenting and using unbinned results, which can hopefully form the basis for a community standard to allow for integration into analysis workflows. This is foreseen to be the start of an evolving community dialogue, in order to accommodate future developments in this field that is rapidly evolving.
Proton Structure from Neural Simulation-Based Inference at the LHC
Marietta Blau Institute for Particle Physics of the ÖAW（奥地利科学院粒子物理马里埃塔·布劳研究所）；Nikhef Theory Group（尼赫夫理论组）；The Higgs Centre for Theoretical Physics, University of Edinburgh（爱丁堡大学希格斯理论物理中心）；Instituto de Física Corpuscular (IFIC), Universidad de Valencia-CSIC（瓦伦西亚大学-西班牙国家科研委员会粒子物理研究所）；Department of Physics and Astronomy, Vrije Universiteit Amsterdam（阿姆斯特丹自由大学物理与天文学系）；Department of Physics and ICTEA, Universidad de Oviedo（奥维耶多大学物理与ICTEA系）；DAMTP, University of Cambridge（剑桥大学应用数学与理论物理系）
Understanding and visualizing the statistical analysis of SN1987A neutrino data
SN 1987 A的中微子探测是中微子物理学中的一个重要事件，它是第一次探测到太阳系外产生的中微子，然后开创了实验中微子天文学的时代。这些数据已经在许多不同的分析中进行了大量的研究，并在不同的方面提出了一些挑战，因为在这种极端环境中超新星爆炸动力学和中微子味转换仍然有许多未知数。此外，低统计数据还需要未分组的统计方法来比较任何模型建议与数据。在本文中，我们重点讨论了最常用的统计分析解释，提出了一种教学方法来理解和可视化这种比较。
Institut für Theoretische Physik, Universität Heidelberg（海德堡大学理论物理研究所）；LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3（巴黎Cité大学LPNHE）；Physics Division, Lawrence Berkeley National Laboratory（伯克利国家实验室物理部）；National Energy Research Scientific Computing Center, Berkeley Lab（伯克利实验室国家能源研究科学计算中心）；Berkeley Institute for Data Science, University of California, Berkeley（加州大学伯克利分校数据科学研究所）；Interdisciplinary Center for Scientific Computing (IWR), Universität Heidelberg（海德堡大学跨学科科学计算中心）
Investigating the impact of the gluon saturation effects on the momentum transfer distributions for the exclusive vector meson photoproduction in hadronic collisions
