The student (ioleksiy) will work on improving the current pre-processing chain for the machine-learning based flavour tagging algorithms. This work will be carried out within the FTAG Algorithm group. It consists of the following aspects:
- he will start fixing pre-processing-related issues in the umami framework implementing full unit and integration tests for the preprocessing part. The preprocessing needs to be rewritten in a more modular way and needs to be prepared such that the preprocessing base module can be pulled out of umami
- The preprocessing should be adapted such that beam spot weights are taken into account.
- Neutral PFlow object will be implemented in the Athena workflow, such that they are available in the training-dataset-dumper. Whenever possible the code shall be written such that it is portable for large-R jet. Afterwards, he will evaluate how much the neutral particles information helps the tagger training. For this, the latest version of the b-tagging algorithm at that time should be used.
The complete workflow will be maintained and documented in git, making sure all the different pre-processing methods are documented.
The work will be reported in the Algo meeting regularly and be documented in git (SW) and as an internal note (performance studies).
532770 Performance Studies - Flavour Tagging
556629 Performance Studies for Flavour Tagging