论文标题
McNntunes：使用机器学习的调整淋浴蒙特卡洛发电机
MCNNTUNES: tuning Shower Monte Carlo generators with machine learning
论文作者
论文摘要
事件生成器的参数调整是一个研究主题，其特征是复杂的选择：对于参数变化的发生器响应很难以理论为基础获得，并且由于发生器所需的较长计算时间，数值方法几乎无法处理。事件发生器调整已通过基于参数化的技术来解决，其中最成功的技术是多项式参数化。在这项工作中，提出了基于人工神经网络的调整程序的实施。通过闭合测试和大型强子对撞机的ATLAS实验进行了封闭测试和实验测量测试。
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 parametrisation-based techniques, with the most successful one being a polynomial parametrisation. 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.
