标题： 基于不确定性感知神经网络的ATLAS实验中量能器信号的精度校准 显示英文标题

标题： Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network

摘要： ATLAS实验在大型强子对撞机上探索使用现代神经网络对其由拓扑连接单元簇（拓扑簇）定义的 calorimeter 信号进行多维校准。 贝叶斯神经网络（BNN）方法不仅产生了一个连续且平滑的校准函数，相对于标准校准提高了性能，还为每个拓扑簇的校准能量提供了不确定性。 使用训练好的 BNN 得到的结果与标准局部强子校准以及通过训练深度神经网络提供的校准进行了比较。 BNN 预测的不确定性是在训练校准的系统不确定性部分贡献的背景下进行解释的。 它们还与使用排斥集合的替代估计器获得的不确定性预测进行了比较。 显示英文摘要

摘要： The ATLAS experiment at the Large Hadron Collider explores the use of modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of topologically connected cells (topo-clusters). The Bayesian neural network (BNN) approach not only yields a continuous and smooth calibration function that improves performance relative to the standard calibration but also provides uncertainties on the calibrated energies for each topo-cluster. The results obtained by using a trained BNN are compared to the standard local hadronic calibration and to a calibration provided by training a deep neural network. The uncertainties predicted by the BNN are interpreted in the context of a fractional contribution to the systematic uncertainties of the trained calibration. They are also compared to uncertainty predictions obtained from an alternative estimator employing repulsive ensembles.