AI-Driven Particle Discovery: Deep Learning and Simulink-Based Analysis of LHC Data Using HPC and MATLAB
Year: 2025
Type: article
Abstract: : This research explores the integration of deep learning and Simulink-based modeling for particle event detection using data from the Large Hadron Collider (LHC). We apply convolutional and recurrent neural networks (CNNs and RNNs) trained on CMS data and benchmark their performance against MATLAB Simulink simulations. High-performance computing (HPC) environments with CUDA acceleration facilitate model training and analysis. Results show high accuracy and improved interpretability of particle event classification, suggesting a scalable framework for next-generation collider experiments. Keywords: AI, Deep Learning, LHC, Simulink, CNN, RNN, HPC, Particle Physics, CMS, MATLAB, Event Detection
DOI: 10.62311/nesx/rp05a8
Author: Murali Krishna Pasupuleti
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410487790