A novel approach to the bias-variance problem in bump hunting
MetadataShow full item record
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.
DepartmentMassachusetts Institute of Technology. Laboratory for Nuclear Science
Journal of Instrumentation
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
Author's final manuscript