Efficient propagation of systematic uncertainties from calibration to analysis with the SnowStorm method in IceCube

TL;DR

The paper introduces the SnowStorm method, a computationally efficient approach for propagating systematic uncertainties with many nuisance parameters in particle physics analyses, demonstrated on IceCube data.

Contribution

It presents a novel method that uses a single simulation set with multiple continuous nuisance parameters to efficiently handle complex systematic uncertainties.

Findings

Enables comprehensive uncertainty treatment with reduced computational cost

Successfully applied to IceCube's dust distribution modeling

Improves analysis accuracy for high-statistics Monte Carlo studies

Abstract

Efficient treatment of systematic uncertainties that depend on a large number of nuisance parameters is a persistent difficulty in particle physics experiments. Where low-level effects are not amenable to simple parameterization or re-weighting, analyses often rely on discrete simulation sets to quantify the effects of nuisance parameters on key analysis observables. Such methods may become computationally untenable for analyses requiring high statistics Monte Carlo with a large number of nuisance degrees of freedom, especially in cases where these degrees of freedom parameterize the shape of a continuous distribution. In this paper we present a method for treating systematic uncertainties in a computationally efficient and comprehensive manner using a single simulation set with multiple and continuously varied nuisance parameters. This method is demonstrated for the case of the depth-dependent effective dust distribution within the IceCube Neutrino Telescope.