Speaker
Louis Moureaux
(UNI/EXP (Uni Hamburg, Institut fur Experimentalphysik))
Description
We present a model-agnostic search for new physics in the dijet final state using five different novel machine-learning techniques. Other than the requirement of a narrow dijet resonance, minimal additional assumptions are placed on the signal hypothesis. Signal regions are obtained utilizing multivariate machine learning methods to select jets with anomalous substructure. A collection of complimentary methodologies -- based on unsupervised, weakly-supervised and semi-supervised paradigms -- are used in order to maximize the sensitivity to unknown New Physics signatures.
Primary authors
Gregor Kasieczka
(UNI/EXP (Uni Hamburg, Institut fur Experimentalphysik))
Louis Moureaux
(UNI/EXP (Uni Hamburg, Institut fur Experimentalphysik))
Manuel Sommerhalder
(UNI/EXP (Uni Hamburg, Institut fur Experimentalphysik))
Tobias Quadfasel
(UNI/EXP (Uni Hamburg, Institut fur Experimentalphysik))
