This is a course on statistical methods in particle physics mainly targeting PhD students but it is open to other interested students and post-docs. The school covers key analysis tasks in theory and practice as well as going deep with lectures into some specific areas of high relevance. Below is the preliminary agenda that may be still subject to some modifications.
The program comprises:
- Statistical inference - walk through main analysis tasks
- Lectures by Glen Cowan (RHUL) complemented by Combine Tool hands on Tutorial by Aliya Nigamova (DESY) and Nick Wardle (Imperial)
- Topics include: Parameter Estimation, Hypothesis testing, Significances, Confidence intervals (for limit settings and measurements), asymptotic formulae and corrections
- Please take note: this is a world premiere, having together the Lectures from Glen Cowan on statistical foundations like profile likelihood asymptotic formulae and applying/practicing these with the Combine tool that provides interfaces to RooFit/RooStats (no prior knowledge/experience with the Combine tool is required)
- Statistics for Machine Learning - looking behind the scenes
- Lectures by Tilman Plehn (Heidelberg)
- Topics include: Bayesian Network fitting, Classification, Generative nets and Likelihood free inference
- Special session on uncertainties - from basics to advanced
- Lectures by Roger Barlow (Huddersfield) and Glen Cowan (RHUL)
- Topics include: Introduction, treatment of asymmetric uncertainties and errors on errors
- Unfolding of differential measurements - correcting for detector effects
- Lectures by Vince Croft (Nikhef)
- School summary - what you should have learned from the School
- Lecture by Roger Barlow (Huddersfield)
It is expected that you bring your own laptop for the hands on sessions.
The school fee is 80 Euro.
Please register until 11 March 2024.
I. Henning, A. Hinzmann, D. Kruecker, I. Melzer-Pellmann, O. Behnke