Speaker
Description
One of the prominent questions in particle physics is the apparent assymetry of matter and antimatter, which in the early universe led to an abundance of the former, resulting in our cosmos as we see it today. One important part in understanding this asymmetry is the so called Charge and Parity violation (CPV), which describes the different behaviour under the combination of charge conjugation and spatial reflection. To study this phenomenon, the decays of neutral B mesons, which oscillate between particle and antiparticle state are examined at the LHCb experiment. Particularly for those final states that can be reached from both, it is essential to know, in which flavour the B meson was produced. At LHCb, this information is obtained by (MachineLearning based) flavour tagging alogrithms, so called taggers. The existing taggers try to indentify individual tracks related to the production of the signal meson to provide this information on the initial state. When requiring a high tagging quality, using these classic approaches leads to low efficiencies, reducing the statistic power of the data samples. Furthermore starting from Run 3 the higher instantaneous luminosity at LHCb, leading to higher track multiplicities in the detected events, makes flavour tagging an increasingly challenging task. Therefore work is ongoing to develop an Inclusive Flavour Tagger, which aims to process the full event information exploiting more advanced machine learning techniques, to provide a better prediction of the initial B meson flavour.
