Carlos’s work focused on developing advanced data analysis methods to detect rare particle interactions that could point to new physics. Using data from the upgraded LHCb detector, he designed techniques to identify and reconstruct subtle signals in large, complex datasets, selecting specific decay events and tracking low-energy particles with high precision. This approach allows the collection of an unprecedentedly large dataset, several orders of magnitude larger than previous experiments, which he is using to test theoretical models and investigate anomalies in the data.
ESR9: Real-time analysis for Dark Photons search in LHCb and smart vehicles
Thesis
Planned Title: Real-time analysis for Dark Photons search in LHCb
Date: June 2026
Publications
Stephanie Hansmann-Menzemer
Stephanie started her career in particle physics on a reactor neutrino physics experiment in Grenoble/France and then joined for her PhD at Karlsruhe/Germany the CDF Collaboration at the Tevatron. She took part in the discovery of the Bs-Bs-bar oscillation at CDF as a postdoc at MIT (USA) and at Cantabria University (Spain). She decided to stay in flavour physics and joined LHCb and Heidelberg University in 2006. In 2014 she became a full professor.
Since then her main work is pattern recognition, trigger software and flavour physics analysis at LHCb. In 2018 she joined the future direct Dark Matter experiment DARWIN.
Martino Borsato
Martino started his journey through particle physics and Europe when he left his home country (Italy) for an Erasmus in France. He stayed in France for a PhD, but the experiment he was contributing to building was soon cancelled. He then joined the fantastic LHCb collaboration and never left. After three years in Santiago de Compostela and CERN, he is now part of the Heidelberg LHCb group, where he leads his research as a junior group leader. Currently, his focus consists of the search for dark sector particles and the study of rare decays of beauty quarks.
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SMARTHEP is funded by the European Union’s Horizon 2020 research and innovation programme, call H2020-MSCA-ITN-2020, under Grant Agreement n. 956086
