Speaker
Jonah Evan Blank
(Technical University Dortmund)
Description
We present a new algorithm for tagging the production flavour of neutral 𝐵0 and 𝐵0𝑠 mesons in proton-proton collisions. It is based on a deep neural network, DeepSets, and exploits a comprehensive set of tracks associated with the hadronization process. The algorithm is calibrated on data collected by the LHCb experiment at a centre-of-mass energy of 13TeV. This inclusive approach enhances the flavour tagging performance beyond the established same-side and opposite-side tagging methods. The gains
in tagging power offer significant benefits for precision measurements
of 𝐶𝑃 violation and mixing in the neutral 𝐵 meson systems.
Authors
Jonah Blank
(Purdue University)
Jonah Evan Blank
(Technical University Dortmund)
Co-author
Keri Vos
(Nikhef National institute for subatomic physics (NL))
