Derives that the MC replica method produces a distribution differing from the Bayesian Laplace approximation by a single computable matrix (residual-weighted Hessian), whose sign and magnitude determine over- or under-estimation of uncertainties in nonlinear models.
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The report reviews progress since 2021 in fixed-order computations for LHC applications and identifies processes requiring missing higher-order corrections to match anticipated experimental precision.
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Propagating data noise through the fit: the Monte Carlo replica distribution
Derives that the MC replica method produces a distribution differing from the Bayesian Laplace approximation by a single computable matrix (residual-weighted Hessian), whose sign and magnitude determine over- or under-estimation of uncertainties in nonlinear models.
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Les Houches 2023 -- Physics at TeV Colliders: Report on the Standard Model Precision Wishlist
The report reviews progress since 2021 in fixed-order computations for LHC applications and identifies processes requiring missing higher-order corrections to match anticipated experimental precision.