Rapidly applying the effects of detector response to physics objects (e.g. electrons, muons, showers of particles) is essential in high energy physics. Currently available tools for the transformation from truth-level physics objects to reconstructed detector-level physics objects involve manually defining resolution functions. These resolution functions are typically derived in bins of variables that are correlated with the resolution (e.g. pseudorapidity and transverse momentum). This process is time consuming, requires manual updates when detector conditions change, and can miss important correlations. Machine learning offers a way to automate the process of building these truth-to-reconstructed object transformations and can capture complex correlation for any given set of input variables. Such machine learning algorithms, with sufficient optimization, could have a wide range of applications: improving phenomenological studies by using a better detector representation, allowing for more efficient production of Geant4 simulation by only simulating events within an interesting part of phase space, and studies on future experimental sensitivity to new physics.

中文翻译：

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使用机器学习的自动探测器模拟和重建参数化

快速将探测器响应的影响应用于物理对象（例如电子、μ 子、粒子簇射）在高能物理学中是必不可少的。当前可用的用于从真实级物理对象转换为重建探测器级物理对象的工具涉及手动定义分辨率函数。这些分辨率函数通常是在与分辨率相关的变量箱中导出的（例如，赝速度和横向动量）。此过程非常耗时，需要在检测器条件发生变化时手动更新，并且可能会错过重要的相关性。机器学习提供了一种自动化构建这些真实到重建对象转换过程的方法，并且可以捕获任何给定输入变量集的复杂相关性。这样的机器学习算法，