标题： 从事件集合中进行参数推断和顶夸克质量 显示英文标题

标题： Parameter Inference from Event Ensembles and the Top-Quark Mass

摘要： 任何粒子对撞机的关键任务之一是测量。 在实践中，这通常是通过将数据拟合到模拟中来完成的，而模拟依赖于许多参数。 有时，当改变不同参数的效果高度相关时，可能需要大量数据集来解决参数空间的简并性。 一个重要的例子是测量顶夸克质量，在拟合顶夸克质量参数时，模拟中的其他物理和非物理参数必须被边缘化。 我们比较了三种不同的顶夸克质量测量方法：一种经典的直方图拟合程序，类似于实验中常用的方法，可选地结合软落 jets 剥离；一种称为 DCTR 的机器学习方法；以及一种线性回归方法，可以使用最小二乘拟合或带有密集线性激活神经网络的方法。 尽管单个事件完全不相关，但我们发现当输入按质量排序的事件集合时，线性回归方法最有效，而不是在单个事件上进行训练。 尽管所有方法都能稳健地提取顶夸克质量参数，但线性网络表现略胜一筹，并且非常简单。 对于顶夸克研究，我们得出结论，使用基于排序事件集合训练的网络，可以显著减少当前从 LHC 数据中提取顶夸克质量的蒙特卡洛不确定性（可能减少两倍）。 更一般地说，从集合中进行机器学习用于参数估计在对撞机物理测量中具有广泛潜力。 显示英文摘要

摘要： One of the key tasks of any particle collider is measurement. In practice, this is often done by fitting data to a simulation, which depends on many parameters. Sometimes, when the effects of varying different parameters are highly correlated, a large ensemble of data may be needed to resolve parameter-space degeneracies. An important example is measuring the top-quark mass, where other physical and unphysical parameters in the simulation must be marginalized over when fitting the top-quark mass parameter. We compare three different methodologies for top-quark mass measurement: a classical histogram fitting procedure, similar to one commonly used in experiment optionally augmented with soft-drop jet grooming; a machine-learning method called DCTR; and a linear regression approach, either using a least-squares fit or with a dense linearly-activated neural network. Despite the fact that individual events are totally uncorrelated, we find that the linear regression methods work most effectively when we input an ensemble of events sorted by mass, rather than training them on individual events. Although all methods provide robust extraction of the top-quark mass parameter, the linear network does marginally best and is remarkably simple. For the top study, we conclude that the Monte-Carlo-based uncertainty on current extractions of the top-quark mass from LHC data can be reduced significantly (by perhaps a factor of 2) using networks trained on sorted event ensembles. More generally, machine learning from ensembles for parameter estimation has broad potential for collider physics measurements.