Boosted Top Tagging Method Overview
Gregor Kasieczka
TL;DR
This paper reviews various techniques, including jet substructure variables, algorithms, and deep learning, for identifying boosted top quarks at the LHC, highlighting recent advancements in the field.
Contribution
It provides a comprehensive overview of current methods and recent developments in boosted top quark tagging at the LHC.
Findings
Deep learning techniques have improved top quark identification accuracy.
Jet substructure variables remain essential tools for top tagging.
Recent methods enhance discrimination between top jets and background.
Abstract
We briefly review common tools and methods to identify boosted, hadronically decaying top quarks at the LHC experiments. This includes generic jet substructure variables, specific top identification algorithms, and recent developments in deep learning techniques.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsParticle physics theoretical and experimental studies · Algorithms and Data Compression · Computational Physics and Python Applications