Promotionsvortrag Micol Olocco

This thesis investigates the role of automation and machine-learning techniques as a methodological bridge between high-energy physics and industry, within the framework of the SMARTHEP European Training Network. The core scientific contribution of this thesis is the development of flavour-tagging algorithms for the LHCb Upgrade I. The performance of the developed algorithms is evaluated using data collected by the LHCb experiment in 2024 and demonstrates stable behaviour in the upgraded detector environment. These algorithms constitute the first flavour-tagging implementation specifically developed for Run 3, enabling time-dependent measurements of neutral B-meson mixing and CP violation. Automation principles are further applied to the development of an infrastructure for the deployment and validation of Trigger Configuration Keys at LHCb, designed to support Run 3 data-taking and operations. Beyond the core LHCb physics programme, this thesis presents the design of an open-source framework for the automated generation of synthetic financial transaction data using large language models. Such synthetic datasets provide a practical alternative for fraud-detection research, where access to real financial data is constrained by privacy and regulatory requirements.