A novel approach to the bias-variance problem in bump hunting
Name
1705.03578.pdf
Size
1.21 MB
Format
Adobe PDF
Checksum (MD5)
ead929e0fa14f65d93f23733355d62ca
Author(s)
Williams, Michael
Date Issued
September 2017
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
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.
MIT Department
Massachusetts Institute of Technology. Laboratory for Nuclear Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1088/1748-0221/12/09/P09034
