Conveners
Tagging Techniques
- Freya Blekman (Deutsches Elektronen-Synchrotron (DE))
Experimental uncertainties related to the calibration of hadronic objects (particularly the jet energy scale and resolution) can limit the precision of physics analyses at the LHC, and so improvements in performance have the potential to broadly increase the impact of results. Such settings are among most promising for cutting-edge machine learning and artificial intelligence algorithms at the...
This talk will overview the usage of boosted multi-prong jet tagging in CMS and how such taggers are calibrated. It will highlight a new method for calibrating the tagging of multi-prong jets using the Lund Jet Plane to correct the substructure of simulated jets. The method is shown to significantly improve the data-simulation agreement of substructure observables.
The identification of heavy-flavour jets (tagging) remains a critical task at hadron colliders. A key signature of such jets is the displaced decay vertices left by boosted b- and c-hadrons. While existing tagging algorithms leveraged manually designed algorithms to identify and fit vertices, they were succeeded by edge-classification based Graph Neural Networks (GNNs) that, despite...
Physics measurements in the highly Lorentz-boosted regime, including the search for the Higgs boson or beyond standard model particles, are a critical part of the LHC physics program. In the CMS Collaboration, various boosted-jet tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to bb̅ or cc̅, have been developed and deployed in a variety of...
Inspired by the recent successes of language modelling and computer vision machine learning techniques, we study the feasibility of repurposing these developments for particle track reconstruction in the context of high energy physics. In particular, drawing from developments in the field of language modelling we showcase the performance of multiple implementations of the transformer model,...
