Integrating machine learning and neural networks for particle image reconstruction in high-energy physics experiments

Accurate reconstruction of particle images is imperative in high-energy physics experiments for the detection and identification of new particles. This paper will introduce how to build a model which can use the data from the HCAL, EMCAL and TRACKER detectors to reconstruct the particle images. The model successfully integrates machine learning and neural network techniques, particularly multilayer perceptron (MLP) and convolutional neural network (CNN). The model has been effectively trained and evaluated on datasets simulating high-energy events. The result demonstrates the superiority of CNNs in capturing local features and spatial relationships, and also confirms the great potential of deep learning techniques in improving the accuracy and realism of particle image reconstruction.