A novel approach to the bias-variance problem in bump hunting
Abstract
This study explores various data-driven methods for performing background-model selection, and for assigning uncertainty on the signal-strength estimator that arises due to the choice of background model. The performance of these methods is evaluated in the context of several realistic example problems. Furthermore, a novel strategy is proposed that greatly simplifies the process of performing a bump hunt when little is assumed to be known about the background. This new approach is shown to greatly reduce the potential bias in the signal-strength estimator, without degrading the sensitivity by increasing the variance, and to produce confidence intervals with valid coverage properties.
- Authors:
-
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Lab. for Nuclear Science
- Publication Date:
- Research Org.:
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1535615
- Grant/Contract Number:
- SC0010497
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Instrumentation
- Additional Journal Information:
- Journal Volume: 12; Journal Issue: 09; Journal ID: ISSN 1748-0221
- Publisher:
- Institute of Physics (IOP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 47 OTHER INSTRUMENTATION; Instruments & Instrumentation
Citation Formats
Williams, M. A novel approach to the bias-variance problem in bump hunting. United States: N. p., 2017.
Web. doi:10.1088/1748-0221/12/09/p09034.
Williams, M. A novel approach to the bias-variance problem in bump hunting. United States. https://doi.org/10.1088/1748-0221/12/09/p09034
Williams, M. Thu .
"A novel approach to the bias-variance problem in bump hunting". United States. https://doi.org/10.1088/1748-0221/12/09/p09034. https://www.osti.gov/servlets/purl/1535615.
@article{osti_1535615,
title = {A novel approach to the bias-variance problem in bump hunting},
author = {Williams, M.},
abstractNote = {This study explores various data-driven methods for performing background-model selection, and for assigning uncertainty on the signal-strength estimator that arises due to the choice of background model. The performance of these methods is evaluated in the context of several realistic example problems. Furthermore, a novel strategy is proposed that greatly simplifies the process of performing a bump hunt when little is assumed to be known about the background. This new approach is shown to greatly reduce the potential bias in the signal-strength estimator, without degrading the sensitivity by increasing the variance, and to produce confidence intervals with valid coverage properties.},
doi = {10.1088/1748-0221/12/09/p09034},
journal = {Journal of Instrumentation},
number = 09,
volume = 12,
place = {United States},
year = {Thu Sep 28 00:00:00 EDT 2017},
month = {Thu Sep 28 00:00:00 EDT 2017}
}
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