Speaker
Description
Machine learning techniques have been successfully utilized in data processing and analysis for decades.
Hand in hand with the "deep learning revolution", the importance of Neural Networks in this area is still growing.
One advantage of employing a Neural Network is that certain features do not have to be engineered manually but are constructed by representations of the network.
This can enable a higher exploitation of correlations between input variables and also allow an increased variable space compared to other techniques. Modern machine learning frameworks make construction and usage of these techniques even more accessible and utilizable.
This presentation covers the successful deployment of a Deep Neural Network for the discrimination of neutral $B$ and $\bar{B}$ mesons at the Belle II experiment, the so-called flavour tagging. Implementation and results of this approach will be presented.
