AI-Driven Particle Discovery: Deep Learning and Simulink-Based Analysis of LHC Data Using HPC and MATLAB
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
Authors
- Murali Krishna Pasupuleti (ORCID: https://orcid.org/0009-0007-1023-7035)
Publication Details
- Journal
- International Journal of Academic and Industrial Research Innovations(IJAIRI)
- Published
- 2025-05-19
- DOI
- https://doi.org/10.62311/nesx/rp05a8
- Primary Topic
- Distributed and Parallel Computing Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00