EVENT GENERATION USING MADGRAPH AND AI

Abstract

In this project, we study the simulation of high-energy particle collisions using MadGraph, a widely used Monte Carlo event generator in theoretical and experimental particle physics. We begin by installing and setting up the software environment, including the necessary dependencies such as Python, Fortran compiler, and Pythia. Using MadGraph, we simulate a fundamental process—proton-proton (pp) collisions leading to top-anti top quark pair (t t̄) production. The simulation allows us to generate and analyze various subprocesses and visualize Feynman diagrams corresponding to s-channel, t-channel, and u-channel interactions. We further explore the modification of key parameters like beam energies and particle masses through the run-card and param-card configuration files. By launching the simulation we observe how MadGraph predicts cross-sections and processes consistent with real collider experiments. This study demonstrates MadGraph’s effectiveness in computing and analyzing tree-level and loop-level processes, making it a valuable tool for modern particle physics research. Additionally, we investigate the emerging integration of artificial intelligence and machine learning techniques with MadGraph, exploring how AI can enhance event generation, parameter optimization, and anomaly detection in beyond Standard Model physics.This study demonstrates MadGraph's effectiveness in computing and analyzing tree-level and loop-level processes, making it a valuable tool for modern particle physics research, particularly as the field increasingly adopts AI-driven approaches for complex theoretical computations and data analysis.