Speaker
Description
Recently, the 1994 dataset from the ALEPH experiment was converted into EDM4HEP format. Using this archived and converted ALEPH data, we apply modern software intended for FCC studies to process the data and train and employ state-of-the art, deep-learning based jet-tagging techniques. We obtain significant improvements in heavy-flavour tagging performance with respect to the legacy algorithms, and observe good agreement between simulation and data as a function of the tagger output score. These results open promising prospects on enhancing the precision of various legacy electroweak measurements by re-analyzing LEP data with improved analysis methods. Furthermore, these studies form a useful testbed for the development of software for future electron-positron colliders with real data.
Summary
We train and evaluate advanced b-tagging techniques with modern software on ALEPH data converted to EDM4HEP format, showing the feasibility of complex data analysis with archived data.