Five ML anomaly-detection methods enhance model-agnostic dijet searches at CMS, and a weakly supervised tagger identifies hadronic top-quark decays in data nearly as well as a supervised classifier.
Dijet resonance search with weak supervision using√s=13 TeVppcollisions in the ATLAS detector
1 Pith paper cite this work, alongside 127 external citations. Polarity classification is still indexing.
1
Pith paper citing it
127
external citations · OpenAlex
fields
hep-ex 1years
2025 1verdicts
ACCEPT 1representative citing papers
citing papers explorer
-
Machine-learning techniques for model-independent searches in dijet final states
Five ML anomaly-detection methods enhance model-agnostic dijet searches at CMS, and a weakly supervised tagger identifies hadronic top-quark decays in data nearly as well as a supervised classifier.