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We present a model-independent study aimed at characterizing the nature of possible resonances in the jet-photon or jet-$Z$ final state at hadron colliders. Such resonances are expected in many models of compositeness and would be a clear indication of new physics. At leading order, in the narrow width approximation, the matrix elements are parameterized by just a few constants describing the coupling of the various helicities to the resonance. We present the full structure of such amplitudes up to spin two and use them to simulate relevant kinematic distributions that could serve to constrain the coupling structure. This also generalizes the signal generation strategy that is currently pursued by ATLAS and CMS to the most general case in the considered channels. While the determination of the P/CP properties of the interaction seems to be out of reach within this framework, there is a wealth of information to be gained about the spin of the resonance and the relative couplings of the helicities.
We show that in studies of light quark- and gluon-initiated jet discrimination, it is important to include the information on softer reconstructed jets (associated jets) around a primary hard jet. This is particularly relevant while adopting a small
We calculate the production of a W boson and a single b jet to next-to-leading order in QCD at the Fermilab Tevatron and the CERN Large Hadron Collider. Both exclusive and inclusive cross sections are presented. We separately consider the cross secti
The production of vector boson tagged heavy quark jets provides potentially new tools to study jet quenching, especially the mass hierarchy of parton energy loss. In this work, we present the first theoretical study on $Z^0,+,$b-jet in heavy-ion coll
Spectroscopic methods allow to measure energy differences with unrivaled precision. In the case of gravity resonance spectroscopy, energy differences of different gravitational states are measured without recourse to the electromagnetic interaction.
Classification of jets with deep learning has gained significant attention in recent times. However, the performance of deep neural networks is often achieved at the cost of interpretability. Here we propose an interpretable network trained on the je