A review of transformer-based jet tagging that highlights the authors' CA-Mixer network as a state-of-the-art, faster alternative to Particle Transformer.
Hypergraphs in LHC Phenomenology -- The Next Frontier of IRC-Safe Feature Extraction
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In this study, we critically evaluate the approximation capabilities of existing infra-red and collinear (IRC) safe feature extraction algorithms, namely Energy Flow Networks (EFNs) and Energy-weighted Message Passing Networks (EMPNs). Our analysis reveals that these algorithms fall short in extracting features from any $N$-point correlation that isn't a power of two, based on the complete basis of IRC safe observables, specifically C-correlators. To address this limitation, we introduce the Hypergraph Energy-weighted Message Passing Networks (H-EMPNs), designed to capture any $N$-point correlation among particles efficiently. Using the case study of top vs. QCD jets, which holds significant information in its 3-point correlations, we demonstrate that H-EMPNs targeting up to N=3 correlations exhibit superior performance compared to EMPNs focusing on up to N=4 correlations within jet constituents.
fields
hep-ph 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Transformer networks for Heavy flavor jet tagging
A review of transformer-based jet tagging that highlights the authors' CA-Mixer network as a state-of-the-art, faster alternative to Particle Transformer.