MadGraph5_aMC@NLO automates tree-level, NLO, shower-matched, and merged cross-section computations for collider processes in a unified flexible framework.
Automation of the matrix element reweighting method
2 Pith papers cite this work. Polarity classification is still indexing.
abstract
Matrix element reweighting is a powerful experimental technique widely employed to maximize the amount of information that can be extracted from a collider data set. We present a procedure that allows to automatically evaluate the weights for any process of interest in the standard model and beyond. Given the initial, intermediate and final state particles, and the transfer functions for the final physics objects, such as leptons, jets, missing transverse energy, our algorithm creates a phase-space mapping designed to efficiently perform the integration of the squared matrix element and the transfer functions. The implementation builds up on MadGraph, it is completely automatized and publicly available. A few sample applications are presented that show the capabilities of the code and illustrate the possibilities for new studies that such an approach opens up.
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AI-driven symbolic evolution discovers interpretable event-level observables that retain substantially more local Fisher information than angular baselines for CP-sensitive HZ interference in two collider channels.
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The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
MadGraph5_aMC@NLO automates tree-level, NLO, shower-matched, and merged cross-section computations for collider processes in a unified flexible framework.