The parameters tuning of event generators is a research topic characterized by complex choices: the generator response to parameter variations is difficult to obtain on a theoretical basis, and numerical methods are hardly tractable due to the long computational times required by generators. Event generator tuning has been tackled by parametrization-based techniques, with the most successful one being a polynomial parametrization. In this work, an implementation of tuning procedures based on artificial neural networks is proposed. The implementation was tested with closure testing and experimental measurements from the ATLAS experiment at the Large Hadron Collider.

Program summary

Program Title: MCNNTUNES

CPC Library link to program files: https://doi.org/10.17632/dmkydsxgd3.1

Developer’s repository link: https://github.com/N3PDF/mcnntunes

Licensing provisions: GPLv3

Programming language: Python

Nature of problem: Shower Monte Carlo generators introduce many parameters that must be tuned to reproduce the experimental measurements. The dependence of the generator output on these parameters is difficult to obtain on a theoretical basis.

Solution method: Implementation of a tuning method using supervised machine learning algorithms based on neural networks, which are universal approximators.

中文翻译：

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MCNNTUNES：通过机器学习调整淋浴蒙特卡洛发电机

事件生成器的参数调整是一个具有复杂选择特征的研究主题：生成器对参数变化的响应很难在理论基础上获得，并且由于生成器需要较长的计算时间，因此很难采用数值方法。事件生成器调整已通过基于参数化的技术解决，其中最成功的一项是多项式参数化。在这项工作中，提出了一种基于人工神经网络的调整程序的实现。该实施方案已通过大型强子对撞机的ATLAS实验的闭合测试和实验测量进行了测试。

计划摘要

节目名称： MCNNTUNES

CPC库链接到程序文件： https : //doi.org/10.17632/dmkydsxgd3.1

开发人员的资料库链接： https : //github.com/N3PDF/mcnntunes

许可条款： GPLv3

编程语言： Python

问题性质：淋浴蒙特卡罗发生器引入了许多参数，必须对其进行调整以重现实验测量结果。从理论上很难获得发电机输出对这些参数的依赖性。

解决方法：使用基于神经网络的监督机器学习算法（通用逼近器）实现调整方法。