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.
Model-independent and quasi-model-independent search for new physics at CDF
1 Pith paper cite this work, alongside 68 external citations. Polarity classification is still indexing.
1
Pith paper citing it
68
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.