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A novel approach to the bias-variance problem in bump hunting

Author(s)
Williams, Michael
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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.
Date issued
2017-09
URI
http://hdl.handle.net/1721.1/120588
Department
Massachusetts Institute of Technology. Laboratory for Nuclear Science
Journal
Journal of Instrumentation
Publisher
IOP Publishing
Citation
Williams, M. “A Novel Approach to the Bias-Variance Problem in Bump Hunting.” Journal of Instrumentation 12, 9 (September 2017): P09034–P09034 © 2017 IOP Publishing Ltd
Version: Author's final manuscript
ISSN
1748-0221

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