Abstract Monte Carlo generation of high energy particle collisions is a critical tool for both theoretical and experimental particle physics, connecting perturbative calculations to phenomenological models, and theory predictions to full detector simulation. The generation of minimum bias events can be particularly computationally expensive, where non-perturbative effects play an important role and specific processes and fiducial regions can no longer be well defined. In particular scenarios, particle guns can be used to quickly sample kinematics for single particles produced in minimum bias events. CIMBA (Cubic Interpolation for Minimum Bias Approximation) provides a comprehensive package to smoothly sample predefined kinematic grids, from any general purpose Monte Carlo generator, for all particles produced in minimum bias events. These grids are provided for a number of beam configurations including those of the Large Hadron Collider. Program summary Program title: CIMBA (Cubic Interpolation for Minimum Bias Approximation) CPC Library link to program files: http://dx.doi.org/10.17632/49m44md4ph.1 Licensing provisions: GPL version 2 or later Programming language: Python, C++ Nature of problem: generation of simulated events in high energy particle physics is quickly becoming a bottleneck in analysis development for collaborations on the Large Hadron Collider (LHC). With the expected long-term continuation of the high luminosity LHC, this problem must be solved in the near future. Significant progress has been made in designing new ways to perform detector simulation, including parametric detector models and machine learning techniques, e.g. calorimeter shower evolution with generative adversarial networks. Consequently, the efficiency of generating physics events using general purpose Monte Carlo event generators, rather than just detector simulation, needs to be improved. Solution method: in many cases, single particle generation from pre-sampled phase-space distributions can be used as a fast alternative to full event generation. Phase-space distributions sampled in particle pseudorapidity and transverse momentum are sampled from large, once-off, minimum bias samples generated with Pythia 8. A novel smooth sampling of these distributions is performed using piecewise cubic Hermite interpolating polynomials. Distributions are created for all generated particles, as well as particles produced directly from hadronisation. Interpolation grid libraries are provided for a number of common collider configurations, and code is provided which can produce custom interpolation grid libraries. Restrictions: Single particle generation

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CIMBA：使用三次插值快速生成蒙特卡罗

摘要 高能粒子碰撞的蒙特卡罗生成是理论和实验粒子物理学的关键工具，它将微扰计算与现象学模型联系起来，并将理论预测与完整的探测器模拟联系起来。最小偏差事件的生成在计算上可能特别昂贵，其中非微扰效应起着重要作用，并且无法再明确定义特定过程和基准区域。在特定情况下，粒子枪可用于对最小偏差事件中产生的单个粒子的运动学进行快速采样。CIMBA（最小偏差近似的三次插值）提供了一个综合包，可以从任何通用蒙特卡洛发生器中为最小偏差事件中产生的所有粒子平滑地采样预定义的运动网格。这些网格用于许多光束配置，包括大型强子对撞机的配置。程序摘要 程序名称：CIMBA（最小偏差近似的三次插值）CPC 库程序文件链接：http://dx.doi.org/10.17632/49m44md4ph.1 许可条款：GPL 版本 2 或更高版本 编程语言：Python、C++问题性质：高能粒子物理学中模拟事件的生成正迅速成为大型强子对撞机 (LHC) 合作分析开发的瓶颈。随着高光度 LHC 的长期延续，这个问题必须在不久的将来得到解决。在设计执行探测器模拟的新方法方面取得了重大进展，包括参数探测器模型和机器学习技术，例如 具有生成对抗网络的量热仪淋浴进化。因此，需要提高使用通用蒙特卡罗事件生成器（而不仅仅是检测器模拟）生成物理事件的效率。解决方法：在许多情况下，从预采样的相空间分布中生成单个粒子可用作完整事件生成的快速替代方法。在粒子赝快速度和横向动量中采样的相空间分布是从使用 Pythia 8 生成的大型一次性最小偏差样本中采样的。使用分段三次 Hermite 插值多项式对这些分布进行新的平滑采样。为所有生成的粒子以及直接从强子化产生的粒子创建分布。为许多常见的对撞机配置提供了插值网格库，并提供了可以生成自定义插值网格库的代码。限制：单粒子生成