Parametric neural networks learn likelihood ratios to infer top-philic scalar resonances from dip patterns caused by signal-background interference in hadron collider data.
Gauge-Invariant Resummation Formalism for Two-Point Correlation Functions
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abstract
The consistent description of unstable particles, renormalons, or other Schwinger--Dyson-type of solutions within the framework of perturbative gauge field theories necessitates the definition and resummation of off-shell Green's functions, which must respect several crucial physical requirements. A formalism is presented for resummation of off-shell two-point correlation functions, which is mainly based on arguments of analyticity, unitarity, gauge invariance and renormalizability. The analytic results obtained with various methods, including the background field gauges and the pinch technique are confronted with the physical requirements imposed; to one-loop order the pinch technique approach satisfies all of them. Using renormalization group arguments, we discuss issues of uniqueness of the resummation procedure related to the latter method.
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S, T, U parameters are redefined for the B-LSSM using pinch technique contributions to gauge boson self-energies and expressed as functions of B-LSSM parameters, with experimental constraints applied.
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Big Dipper, Help Me Find A Way -- Dip-hunting at hadron colliders
Parametric neural networks learn likelihood ratios to infer top-philic scalar resonances from dip patterns caused by signal-background interference in hadron collider data.
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$S, T, U$ Parameters in The B-LSSM
S, T, U parameters are redefined for the B-LSSM using pinch technique contributions to gauge boson self-energies and expressed as functions of B-LSSM parameters, with experimental constraints applied.