13 research outputs found
Asymptotic formulae for likelihood-based tests of new physics
We describe likelihood-based statistical tests for use in high energy physics
for the discovery of new phenomena and for construction of confidence intervals
on model parameters. We focus on the properties of the test procedures that
allow one to account for systematic uncertainties. Explicit formulae for the
asymptotic distributions of test statistics are derived using results of Wilks
and Wald. We motivate and justify the use of a representative data set, called
the "Asimov data set", which provides a simple method to obtain the median
experimental sensitivity of a search or measurement as well as fluctuations
about this expectation.Comment: fixed typo in equations 75 & 7
Trial factors for the look elsewhere effect in high energy physics
When searching for a new resonance somewhere in a possible mass range, the
significance of observing a local excess of events must take into account the
probability of observing such an excess anywhere in the range. This is the so
called "look elsewhere effect". The effect can be quantified in terms of a
trial factor, which is the ratio between the probability of observing the
excess at some fixed mass point, to the probability of observing it anywhere in
the range. We propose a simple and fast procedure for estimating the trial
factor, based on earlier results by Davies. We show that asymptotically, the
trial factor grows linearly with the (fixed mass) significance
Testing minimal lepton flavor violation with extra vector-like leptons at the LHC
Models of minimal lepton flavor violation where the seesaw scale is higher
than the relevant flavor scale predict that all lepton flavor violation is
proportional to the charged lepton Yukawa matrix. If extra vector-like leptons
are within the reach of the LHC, it will be possible to test the resulting
predictions in ATLAS/CMS.Comment: 19 pages, 8 figure
Estimating the "look elsewhere effect" when searching for a signal
The "look elsewhere effect" refers to a common situation where one searchesfor a signal in some space of parameters-for example, a resonance search withunknown mass, or a search for astrophysical point sources with unknown locationin the sky. Since Wilks' theorem does not apply in such cases, one usually hasto resort to computationally expansive Monte-Carlo simulations in order tocorrectly estimate the significance of a given observation. Recent results fromthe theory of random fields provide powerful tools which may be used toalleviate this difficulty, in a wide range of applications. We review thoseresults and discuss their implementation in problems of practical interest