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May 8 – 12, 2023
Norfolk Waterside Marriott
US/Eastern timezone

Development of a Deep-learning based Full Event Interpretation (DFEI) algorithm for the future LHCb trigger

May 11, 2023, 3:00 PM
15m
Hampton Roads VII (Norfolk Waterside Marriott)

Hampton Roads VII

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Oral Track 9 - Artificial Intelligence and Machine Learning Track 9 - Artificial Intelligence and Machine Learning

Speaker

Mr Eschle, Jonas (University of Zürich)

Description

In a decade from now, the Upgrade II of LHCb experiment will face an instantaneous luminosity ten times higher than in the current Run 3 conditions. This will bring LHCb to a new era, with huge event sizes and typically several signal heavy-hadron decays per event. The trigger scope will shift from selecting interesting events to select interesting parts of multi-signal events. To allow for an inclusive, automatic and accurate multi-signal selection per event, we propose evolving from the current signal-based trigger to a Deep-learning based Full Event Interpretation (DFEI) approach. We have designed the first prototype for the DFEI algorithm, leveraging the power of Graph Neural Networks (GNN). The algorithm takes as input the final-state particles and has a two-folded goal: select the sub-set of particles originated in heavy-hadron decays, and reconstruct the decay chains in which they were produced. In this talk, we describe the design and development of this prototype, its current performance on simulated data for Run 3 conditions, and the studies and developments done so far towards and eventual integration in the Real Time Analysis (RTA) system of LHCb.

Consider for long presentation No

Primary authors

Garcia Pardinas, Julian (CERN) Prof. Calvi, Marta (University of Milano-Bicocca) Mr Eschle, Jonas (University of Zürich) Dr Mauri, Andrea (Imperial College of London) Ms Mozzanica, Martina (University of Milano-Bicocca) Prof. Serra, Nicola (University of Zürich)

Presentation materials