Speaker
Description
The LHCb experiment has deployed machine learning and artificial intelligence models in its real-time data processing from the start of Run 1 data taking, and by Run 3 such models are an integral part of the trigger.
This talk will describe the usage of machine learning and AI models and algorithms within the LHCb real-time analysis data processing paradigm as part of both reconstruction and online selection, as well as developments in using such algorithms for online monitoring and software QA.
Moreover, for the LHC Run 5, the LHCb collaboration is proposing to build a second upgrade of its detector, targeting the creation of an ultimate flavour factory machine in the forward region at the LHC. This talk will also sketch the unprecedented challenges this proposal will pose to the real-time reconstruction and selection of physics-quality signals and the ways in which machine-learning and AI models are anticipated to play a central role.