Speaker
Jordan Tucker
(UCLA)
Description
Hypothesis tests for the presence of new sources of Poisson counts
amidst background processes are frequently performed in high energy
physics, gamma ray astronomy, and other branches of science. While there
are conceptual issues already when the mean rate of background is
precisely known, the issues are even more difficult when the mean
background rate has non-negligible uncertainty, as some commonly used
techniques are not on a sound foundation. In this paper, we evaluate two
classes of algorithms by the criterion of how close the ensemble-average
Type I error rate (rejection of the background-only hypothesis when it
is true) compares with the nominal significance level given by the
algorithm. Following J. Linnemann, we recommend wider use of an
algorithm firmly grounded in frequentist tests of the ratio of Poisson
means.
Authors
Jordan Tucker
(UCLA)
Robert Cousins
(UCLA)