CERN Academic Training Programme 2008/2009

LECTURE SERIES

21-22-23 January 2009

11:00-12:00 hrs., Main Auditorium, Bldg 500-1-001

The Opposite Ends of Supersymmetry and their Implications for the LHC

James WELLS / CERN-TH

There have been many predictions for the mass patterns of superpartners. In these lectures I discuss two interesting opposite-end approaches to supersymmetry breaking that determine the superpartner masses: zero scalar mass supersymmetry (no scale, gaugino mediation, etc.) and heavy scalar mass supersymmetry (split susy, PeV-scale susy, etc.). We will step through the theory motivations for each scenario, and detail the rich phenomena that each implies for LHC discovery.

26-27-28 January 2009

11:00-12:00 hrs., Main Auditorium, Bldg 500-1-001

Electroweak symmetry breaking: to Higgs or not to Higgs

Christophe Grojean / CERN-PH-TH

How do elementary particles acquire their mass? What makes the photon different from the Z boson? In a word: How is electroweak symmetry broken? This is one of the pressing questions in particle physics that the LHC will answer soon. The aim of this lecture is, after briefly introducing SM physics and the conventional Higgs mechanism, to give a survey of recent attempts to go beyond a simple elementary Higgs. In particular, I will describe composite models (where the Higgs boson emerges from a strongly-interacting sector) and Higsless models. Distinctive signatures at the LHC are expected and will reveal the true nature of the electroweak symmetry sector.

Sponsor: Angel Uranga

2-5 February 2009

11:00-12:00 hrs., Main Auditorium, Bldg 500-1-001

Statistical Techniques for Particle Physics

Kyle Cranmer / CERN-PH

This series will consist of four 1-hour lectures on statistics for particle physics. The goal will be to build up to techniques meant for dealing with problems of realistic complexity while maintaining a formal approach. I will also try to incorporate usage of common tools like ROOT, RooFit, and the newly developed RooStats framework into the lectures. The first lecture will begin with a review of the basic principles of probability, some terminology, and the three main approaches towards statistical inference (Frequentist, Bayesian and Likelihood-based). I will then outline the statistical basis for multivariate analysis techniques (the Neyman-Pearson lemma) and the motivation for machine learning algorithms. Later, I will extend simple hypothesis testing to the case in which the statistical model has one or many parameters (the Neyman Construction and the Feldman-Cousins technique). From there I will outline techniques to incorporate background uncertainties. If time allows, I will touch on the statistical challenges of searches for physics beyond the standard model and the look-elsewhere effect.

by HR Department