Speaker
Description
Rare decays of B mesons, such as B⁰ → K*⁰(892)μ⁺μ⁻, play a crucial role in testing the Standard Model (SM) of particle physics and probing potential signs of New Physics (NP). These flavor-changing neutral current (FCNC) processes are forbidden at tree level in the SM and occur only via higher-order loop diagrams, making them extremely sensitive to virtual contributions from beyond the SM particles.
The complexity and rarity of these events pose significant challenges in terms of signal identification and background suppression. In this context, Machine Learning (ML) techniques have emerged as powerful tools for enhancing the precision and efficiency of signal classification in high energy physics experiments.
This work aims to develop and evaluate machine learning models capable of distinguishing signal events of the rare decay B⁰ → K*⁰(892)μ⁺μ⁻ from various background processes using simulated and real experimental data.