In Run 3, LHCb will collect physics events at higher rates thanks to newly installed detectors that can sustain higher instantaneous luminosity and a revolutionary software trigger that will enable LHCb to rapidly process signal data. The EU-funded LHCbDFEI project will design full event interpretation algorithms to enhance the trigger performance. A deep neural network will process the low-level information from the detector and infer the heavy-hadron decays that occurred in the event. This should allow the quick identification of different types of background events and will provide further information on parent particles.
Julian's presentation at the International Conference of High-Energy Physics 2022
Presentation at IML Machine Learning Working group 5/10/2021
GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions, J. García Pardinas et al, arXiv:2304.08610
A fully upgraded detector offers the opportunity of a redesign of the software used to analyse its recorded data. Such an opportunity has been taken with LHCb's DaVinci analysis code, currently a mastodontic code, which is being skimmed to remove redundant functionalities and is being updated to the latest C++ and python standards. This work is coordinated by the Data Processing & Analysis (DPA) project in LHCb and in Milano we are particularly active in the definition of the configuration of the workflow of the package.
Some of our contributions in GitHub:
Hydra: a package for generating toys of Dalitz decays that is fast, easily configurable and hopefully user friendly.