STAMPS@CMU presents:

Frequentist Statistics, the Particle Physicists’ Way: How To Claim Discovery or Rule Out Theories

by Tommaso Dorigo (INFN-Padova)

Online webinar August 14, 2020 at 1:30-2:30 PM EDT.
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Abstract
Fundamental research in particle physics progresses by investigating the merits of  theories that describe matter and its interactions at the smallest distance scales, as well as by looking for new phenomena in high-energy particle collisions. The large datasets today commonly handled by experiments at facilities such as the CERN Large Hadron Collider, together with the well-defined nature of the questions posed to the data, have fostered the development of an arsenal of specialized Frequentist methods for hypothesis testing and parameter estimation, which strive for severity and calibrated coverage, and which enforce type-I error rates below 3 x 10-7 for discovery claims. In this lecture I will describe the generalities and needs of inference problems at particle physics experiments, and examine the statistical procedures that allow us to rule out or confirm new phenomena.

Bio
Tommaso Dorigo is an experimental particle physicist who works as a First Researcher at the INFN in Italy. He obtained his Ph.D. in Physics in 1999 with a thesis on data analysis for the CDF experiment at the Fermilab Tevatron. After two years as a post-doctoral fellow with Harvard University, when he contributed to the upgrade of the muon system of the CDF-II experiment, he has worked as a researcher for INFN in Padova, Italy.

He collaborates with the CMS experiment at the CERN LHC, where he is a member (formerly chair) of the Statistics Commitee of the experiment. He is the author of several innovative algorithms and machine learning tools for data analysis in particle physics. In 2014-2019 Dorigo has been the founder and scientific coordinator of the ETN “AMVA4NewPhysics” which focused on training PhD students in machine learning applications to physics. His current interests focus on end-to-end optimization of physics experiments and measurements with machine learning. He is also very active in science outreach with a blog, and in 2016 he published the book “Anomaly! Collider Physics and the Quest for New Phenomena at Fermilab”.