STAMPS@CMU and the NSF AI Planning Institute for Data-Driven Discovery in Physics jointly present:

by Glen Cowan (Department of Physics, Royal Holloway, University of London)

Online webinar November 12, 2021 at 1:30-2:30 PM ET.
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Abstract
In a statistical analysis in Particle Physics, one faces two distinct challenges: the limited number of particle collisions and imperfections in the model itself, corresponding to “statistical” and “systematic” errors in the result. To combat the modeling uncertainties one includes nuisance parameters, whose best estimates are often treated as a Gaussian distributed with given standard deviations. The appropriate values for these standard deviations are, however, often the subject of heated argument, which is to say that the uncertainties themselves are uncertain.

A type of model is presented where estimates of the systematic variances are modeled as gamma distributed variables. The resulting confidence intervals show interesting and useful properties. For example, when averaging measurements to estimate their mean, the size of the confidence interval increases as a for decreasing goodness-of-fit, and averages have reduced sensitivity to outliers. The basic properties of the model are presented and several examples relevant for Particle Physics are explored.

Bio PhD in Physics 1988 from University of California, Berkeley, followed by postdoc positions in Munich and Siegen working on electron-positron collisions at LEP (CERN). Research focus on Quantum Chromodynamics (multijet production, measurements of alpha_s, properties of hadronic Z decays). 1998-present, faculty member in Department of Physics, Royal Holloway, University of London. My research in High Energy Physics has involved experiments at the Large Hadron Collider (CERN) on proton-proton collisions, with focus on application and development of statistical methods.