Convolutional Autoencoder Enhances Pulse Reconstruction in High-Energy Physics Experiments with xAI Analysis

On April 22, 2025, researchers Kalina Dimitrova, Venelin Kozhuharov, and Peicho Petkov published Applicability Evaluation of Selected xAI Methods for Machine Learning Algorithms for Signal Parameters Extraction, detailing their investigation into advanced AI techniques to enhance signal analysis in high-energy physics experiments using a modified convolutional autoencoder model.

A modified convolutional autoencoder was employed to identify and reconstruct pulses in scintillating crystals within high energy physics experiments. Four xAI methods were applied to analyze the model’s reconstruction mechanism, providing deeper insights into its functionality. The findings underscore the importance of xAI for enhancing algorithmic understanding and performance.

In recent years, machine learning (ML) has emerged as a transformative tool in the field of high-energy physics (HEP), revolutionizing how scientists analyze and interpret data from particle experiments. By employing advanced algorithms, researchers are now able to uncover intricate patterns within vast datasets, enhancing our understanding of fundamental particles and their interactions.

One of the most significant applications of ML in HEP is its use in particle detection systems. Traditional methods often struggle with the sheer volume and complexity of data generated by particle collisions. Neural networks have proven particularly effective in reconstructing particle trajectories and identifying their positions within detectors. For instance, researchers at the PADME experiment have employed neural networks to analyze signals from liquid argon detectors. These models excel at processing high-dimensional data, enabling precise reconstruction of particle paths and improving the accuracy of collision event analysis.

Another critical area where ML has made an impact is in image processing. Convolutional autoencoders, a type of neural network, have been used to enhance the quality and resolution of images derived from particle collisions. These models work by encoding input data into a compressed format and then decoding it back to its original form, effectively denoising and reconstructing images with remarkable accuracy. This approach has proven particularly useful in experiments where high-resolution imaging is essential for identifying subtle patterns or anomalies.

While ML models are highly effective at processing complex data, their black box nature can sometimes hinder interpretation. To address this challenge, researchers have turned to explainable AI (XAI) methods that provide insights into how these models make decisions. Techniques such as SHAP (SHapley Additive exPlanations) and saliency maps are being widely adopted in HEP research. These tools not only enhance transparency but also enable scientists to validate their models and refine their hypotheses.

Recent studies have demonstrated the significant impact of ML in HEP. For example, researchers at CERN have used machine learning algorithms to analyze data from the Large Hadron Collider (LHC), leading to new insights into particle physics. Additionally, experiments like PADME have shown how neural networks can improve the accuracy of particle detection and tracking.

Machine learning is increasingly important in high-energy physics research, enabling scientists to uncover new insights into the fundamental nature of matter and energy. By leveraging advanced algorithms and techniques, researchers are able to process and analyze complex datasets with greater precision and efficiency, paving the way for future discoveries in this exciting field.