TRAINING DEEP NEURAL NETWORKS FOR AUTOMATED ANALYSIS OF PHYSICAL EXPERIMENT DATA

Abstract

In modern physics, experimental setups produce vast volumes of complex data, the processing of which is becoming increasingly labor-intensive for traditional analytical methods. This is particularly relevant for tasks involving the analysis of multidimensional signals, images, and time series, where the speed and accuracy of interpretation play a critical role. The purpose of this work is to investigate the capabilities of deep neural networks for the automated analysis of experimental physical data. Within the scope of this study, the application of various deep learning architectures is considered, including convolutional and recurrent networks, as well as transformer-based models. Significant attention is paid to the preparation of input data – specifically its normalization, annotation, and the generation of simulated samples (Baldi, P., Sadowski, P., & Whiteson, D., 2014). The conducted modeling has demonstrated that the proposed approaches allow for high-accuracy identification of experimental event types, detection of hidden patterns, and prediction of physical system behavior (Goodfellow, I., Bengio, Y., & Courville, A. 2016). The results obtained showcase the promise of using deep learning to increase the efficiency of processing physical research results, reduce manual labor, and improve the quality of interpretation. Future research directions include integration with explainable AI models and application in real-time environments (Agostinelli, S., Allison, J., Amako, K., et al. 2003).