Abstract
Abstract
In high-energy physics it is a recurring challenge to
efficiently and precisely (enough) calculate the global significance
of, e.g., a potential new resonance. We propose a new method that
models the significance in the search region as a Gaussian Process.
The kernel of the Gaussian Process is approximated with a covariance
matrix and is calculated with a carefully designed set of
background-only data sets, comparable in number to the random
background-only data sets used in a typical analysis that relies on
the average upcrossings of the significance. The trials factor for
both low and moderate significances can subsequently be calculated
to the desired precision with a computationally inexpensive random
sampling of the Gaussian Process. In addition, once the covariance
of the Gaussian Process is determined, the average number of
upcrossings can be computed analytically. In our work we give some
highlights of the analytic calculation and also discuss some
peculiarities of the trials factor estimation on a finite grid. We
illustrate the method with studies of three complementary
statistical models.
Subject
Mathematical Physics,Instrumentation
Cited by
2 articles.
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