Valerio Bertacchi is a particle physics researcher with nine years of experience, currently a permanent researcher at INFN Pisa working within the Belle II collaboration. His work spans precision B-decay measurements, flavor-changing neutral current searches (B→K(*)νν̄), tau-involving final states, and tracking/HLT optimization, reflecting a rare mix of analysis and detector/trigger expertise. He has held Humboldt- and Argelander-funded postdocs in Bonn and Marseille and served as Belle II skim manager, coordinating data reduction crucial for large-scale analyses. During his PhD at Scuola Normale Superiore he contributed to W-mass studies in CMS and applied neural-network techniques to high-energy jet tracking, and he has continued to bridge ML and tracking in open-source CMS software contributions. Based in Tuscany, he combines hands-on detector production experience from the Silicon Vertex Detector with advanced analysis techniques, making him effective at turning reconstruction improvements into physics reach. An often-overlooked strength is his demonstrated ability to move between experiment-wide infrastructure roles and focused exotic-decay searches, accelerating both data quality and discovery potential.
9 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD Elementary Particle Physics, Doctor of Philosophy - PhD Elementary Particle Physics at Scuola Normale Superiore
Master of Science - MS Elementary Particle Physics, Master of Science - MS Elementary Particle Physics at Università di Pisa
Contributions:170 reviews, 40 commits, 5 PRs in 2 years 1 month
Contributions summary:Valerio contributed to the development of validation plots and tools for tracking reconstruction in the CMS offline software. They modified code to include new plots, particularly focusing on chi2 and pull distributions related to track-jet association. The user also worked on integrating a DeepCore model, suggesting involvement in machine learning aspects of the project. Their contributions span both adding plotting capabilities and potentially incorporating machine learning for track validation and improvement.
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