Conveners
Tuesday Morning Session
- Cecile Germain-Renaud (LRI)
Lorenzo Bianchini
(Eidgenoessische Tech. Hochschule Zuerich (CH))
10/11/2015, 09:00
The Matrix Element Method (MEM) is a HEP-specific technique to directly calculate the likelihood for a collision event based on the “matrix elements” of quantum field theory and a simplified detector description. The goal of this talk is to be a description of the matrix element method, current implementations, and comparisons with other multivariate approaches.
Richard Wilkinson
(University of Sheffield)
10/11/2015, 09:45
Approximate Bayesian computation (ABC) is the name given to a collection of Monte Carlo algorithms used for fitting complex computer models to data. The methods rely upon simulation, rather than likelihood based calculation, and so can be used to calibrate a much wider set of simulation models. The simplest version of ABC is intuitive: we sample repeatedly from the prior distribution, and...
Kyle Stuart Cranmer
(New York University (US))
10/11/2015, 10:45
Most physics results at the LHC end in a likelihood ratio test. This includes discovery and exclusion for searches as well as mass, cross-section, and coupling measurements. The use of Machine Learning (multivariate) algorithms in HEP is mainly restricted to searches, which can be reduced to classification between two fixed distributions: signal vs. background. I will show how we can extend...
Amir Farbin
(University of Texas at Arlington (US))
10/11/2015, 12:15
In the next decade, the frontiers of High Energy Physics (HEP) will be explored by three machines: the High Luminosity Large Hadron Collider (HL-LHC) in Europe, the Long Base Neutrino Facility (LBNF) in the US, and the International Linear Collider (ILC) in Japan. These next generation experiments must address two fundamental problems in the current generation of HEP experimental software: the...
