Title: Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning Show Chinese title

Title: 五维淋浴分离用于高颗粒度量热器的机器学习

Abstract: To achieve state-of-the-art jet energy resolution for Particle Flow, sophisticated energy clustering algorithms must be developed that can fully exploit available information to separate energy deposits from charged and neutral particles. Three published neural network-based shower separation models were applied to simulation and experimental data to measure the performance of the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL) technological prototype in distinguishing the energy deposited by a single charged and single neutral hadron for Particle Flow. The performance of models trained using only standard spatial and energy and charged track position information from an event was compared to models trained using timing information available from AHCAL, which is expected to improve sensitivity to shower development and, therefore, aid in clustering. Both simulation and experimental data were used to train and test the models and their performances were compared. The best-performing neural network achieved significantly superior event reconstruction when timing information was utilised in training for the case where the charged hadron had more energy than the neutral one, motivating temporally sensitive calorimeters. All models under test were observed to tend to allocate energy deposited by the more energetic of the two showers to the less energetic one. Similar shower reconstruction performance was observed for a model trained on simulation and applied to data and a model trained and applied to data. Show Chinese abstract

Abstract: 为了实现粒子流的最先进喷注能量分辨率，必须开发复杂的能量聚类算法，这些算法能够充分利用可用信息来分离带电粒子和中性粒子的能量沉积。 应用了三种已发表的基于神经网络的簇射分离模型，对模拟和实验数据进行测量，以评估高度颗粒化的CALICE模拟强子量热计（AHCAL）技术原型在区分单个带电粒子和单个中性强子所沉积能量方面的性能。 将仅使用事件中标准空间和能量以及带电轨迹位置信息训练的模型性能，与使用AHCAL提供的时间信息训练的模型性能进行了比较，预计时间信息可以提高对簇射发展的敏感度，从而有助于聚类。 使用模拟和实验数据对模型进行训练和测试，并比较了它们的性能。 当带电强子的能量大于中性强子时，利用时间信息进行训练的最优神经网络实现了显著优越的事件重建，这促使了对时间敏感的量热计的发展。 所有测试模型都被观察到倾向于将两个簇射中能量较高的那个分配给能量较低的那个。 对于一个在模拟上训练并应用于数据的模型和一个在数据上训练并应用的模型，观察到了相似的簇射重建性能。