Machine learning for Flavour tagging

Flavour tagging the identification of jets containing b and c hadrons is key to many fundamental measurements and searches for new phenomena performed by the ATLAS experiment The existing algorithms for flavour tagging in ATLAS use a few different tools to identify the distinctive topology of b gt c decay which gives rise to two secondary vertices with high invariant mass and several associated charged tracks Machine learning is currently used to combine the information from these tools to infer the probability of a jet to be a b c or light jet I will present the possibility of using more advanced machine learning techniques to directly identify the decay topology without intermediate human designed algorithms I will discuss some relevant types of neural net architectures and compare their performance to existing flavour tagging algorithms