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1811077143899144192
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| collection |
MIT
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| description |
© 2019 IOP Publishing Ltd and Sissa Medialab. 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. This method is demonstrated for the case of the depth-dependent effective dust distribution within the IceCube Neutrino Telescope.
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| first_indexed |
2024-09-23T10:38:35Z
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Article
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| id |
mit-1721.1/132225
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| institution |
Massachusetts Institute of Technology
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| language |
English
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| last_indexed |
2024-09-23T10:38:35Z
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| publishDate |
2021
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| publisher |
IOP Publishing
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| record_format |
dspace
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| spelling |
mit-1721.1/1322252022-04-01T17:20:49Z Efficient propagation of systematic uncertainties from calibration to analysis with the SnowStorm method in IceCube © 2019 IOP Publishing Ltd and Sissa Medialab. 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. This method is demonstrated for the case of the depth-dependent effective dust distribution within the IceCube Neutrino Telescope. 2021-09-20T18:21:24Z 2021-09-20T18:21:24Z 2020-09-24T12:26:09Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/132225 en 10.1088/1475-7516/2019/10/048 Journal of Cosmology and Astroparticle Physics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IOP Publishing arXiv
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| spellingShingle |
Efficient propagation of systematic uncertainties from calibration to analysis with the SnowStorm method in IceCube
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| title |
Efficient propagation of systematic uncertainties from calibration to analysis with the SnowStorm method in IceCube
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| title_full |
Efficient propagation of systematic uncertainties from calibration to analysis with the SnowStorm method in IceCube
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| title_fullStr |
Efficient propagation of systematic uncertainties from calibration to analysis with the SnowStorm method in IceCube
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| title_full_unstemmed |
Efficient propagation of systematic uncertainties from calibration to analysis with the SnowStorm method in IceCube
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| title_short |
Efficient propagation of systematic uncertainties from calibration to analysis with the SnowStorm method in IceCube
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| title_sort |
efficient propagation of systematic uncertainties from calibration to analysis with the snowstorm method in icecube
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| url |
https://hdl.handle.net/1721.1/132225
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