Efficient propagation of systematic uncertainties from calibration to analysis with the SnowStorm method in IceCube
Efficient treatment of systematic uncertainties that depend on a large number of nuisance parameters is a persistent difficulty in particle physics and astrophysics 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. Finally, this method is demonstrated for the case of the depth-dependent effective dust distribution within the IceCube Neutrino Telescope.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- Contributing Organization:
- IceCube Collaboration
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 1603535
- Journal Information:
- Journal of Cosmology and Astroparticle Physics, Vol. 2019, Issue 10; ISSN 1475-7516
- Publisher:
- Institute of Physics (IOP)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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