STAMPS@CMU presents:

The Discrete Profiling Method: Handling Uncertainties in Background Shapes

by Nicholas Wardle (Department of Physics, Imperial College London)

Online webinar February 18, 2022 at 1:30-2:30 PM ET.
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Abstract
Model selection is a huge topic in statistics and often in HEP experiments, we don’t know the exact model appropriate for a particular process. Typically HEP experiments will rely on using data to directly constrain or choose which (parametric) models are best suited to extract the underlying physics, however this choice naturally represents a systematic uncertainty in the analysis of the data. While there are several methods to incorporate these uncertainties related to choices of continuous parameter values, the uncertainty associated to the choice of discrete model is less clear. In this presentation, Nicholas will describe a method developed in the context of the search for the Higgs boson at CMS that aims to incorporate the uncertainty related to model selection into statistical analysis of data “the discrete profiling method”. Nicholas will discuss various studies on the bias and coverage properties of the method and open extensions where further work is needed.

Bio

Nicholas did his PhD at Imperial College where he started working in early W/Z cross-section measurements with electrons at CMS, and then moved onto searching for the Higgs boson in the diphoton decay channel, and the discovery in that channel formed his thesis in 2013. After that he held a fellowship at CERN where he spent most of his time on searches for dark matter and H->invisible decays. He moved back to London in 2017 as an STFC fellow at Imperial College and now as a lecturer where he mainly focuses on Higgs combinations and interpretations of precision Higgs boson measurements in the search for physics beyond the SM, and teaches postgraduate courses on statistics and machine learning for physicists.