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Abstract
Accurate Monte Carlo simulations for high-energy events at CERN’s Large Hadron Collider, are very expensive, both from the computing and storage points of view. We describe a method that allows to consistently re-use parton-level samples accurate up to NLO in QCD under different theoretical hypotheses. We implement it in MadGraph5_aMC@NLO and show its validation by applying it to several cases of practical interest for the search of new physics at the LHC.
Highlights
The search of new physics is one of the main priorities of the LHC
Following the Monte Carlo (MC)@Next-to-Leading Order (NLO) method [24,25], the cross-section can be decomposed in two parts, each of which can be used to generate events associated to a given final state multiplicity: In order to have an accurate NLO re-weighting method, one should explicitly factorise out the dependence in the matrix elements
We have presented the implementation of several methods that can be used for re-weighting Leading Order (LO) and NLO samples and discuss the associated intrinsic limitations
Summary
The search of new physics is one of the main priorities of the LHC. The recent observation of an anomaly in the di-photon spectra [1,2] gives hope that we might have a first evidence of Beyond Standard Model (BSM) physics very soon. Changes in local probabilities happening at very short distance, i.e. from BSM physics, decouple from the rest of the simulation stages This is interesting since the slowest part of the simulation is the full simulation of the detector. A logical possibility arises: one can generate large samples under a SM or basic BSM hypothesis and continuously and locally deform the probability functions associated to the distributions of parton-level events in the phase space by changing the “weight” of each event in a sample to account for an alternative theory or benchmark point. Under a not-too-restrictive set of hypotheses which are easy to list, such an event-by-event re-weighting can be shown to be exactly equivalent (at least in the infinite statistic limit) to a direct generation in the BSM.
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