Rodríguez Cahuantzi, MarioFernández Téllez, ArturoRODRIGUEZ CAHUANTZI, MARIO; 209077FERNANDEZ TELLEZ, ARTURO; 10068Aguirre Polo, Andrea2025-05-212025-05-212024-11https://hdl.handle.net/20.500.12371/28264"In high-energy proton-proton collisions, understanding the impact parameter is crucial for unraveling the underlying dynamics of particle interactions. This work explores the ex traction of the impact parameter using machine learning techniques in the context of the ALICE experiment at the Large Hadron Collider (LHC). We apply ML models (DNN and decision Trees) to predict the multiplicity of charged particles in relation to the impact param eter, leveraging simulated data generated by the PYTHIA 8.3 event generator. Our findings demonstrate the efficacy of machine learning algorithms in providing accurate estimates of the impact parameter, offering a new avenue for exploring particle collisions and expanding our understanding of quantum chromodynamics (QCD) in high-energy physics".pdfengCIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRAFísica--Física nuclear y de partículas--Física de partículas elementalesFísica--Física nuclear y de partículas--Interacciones nucleares--Temas especiales--ColisionesPartículas (Física nuclear)--ExperimentosInteracciones protón-protón--InvestigaciónTesis de maestríaopenAccess