This course aims at providing an introduction to the most commonly used statistical methods for high energy physics.
Morning lectures will be supplemented by afternoon exercise sessions, where the material of the morning will be put in practice.
This course provides 3 credits for the UCLouvain doctoral school (CDD Sciences) and also contributes to the activities of the Excellence of Science (EOS) Be.h network
The course will be recorded, and the link to the recordings will be shared with the participants.
COVID-19 measures: the course will take place online only (the zoom link will be sent to the registered participants by email).
Should the situation allow it, there will be space for in-person attendance of 8--10 participants at most in the in room E349 at CP3. However, as of Oct 26th I wouldn't bet on any possibility of having in-person participants.
The evolution is governed by the three colour-codes of the university. We are currently in Code-Red. Please do not make any travel arrangement until we get back to you about this.
In case of Code Yellow, the course will be held in a hybrid in-erson/online way. The room will host at most 8-10 participants: priority will be given to PhD students.
In case of Code-Orange the course will be held fully online (the room would host 4-5 people, and it would therefore make much more sense to do it online only.
In case of Code-Red (the current status) the course will be of course held fully online.
If, as any good Bayesian, I had to bet, given the current situation and its likely evolution, I would bet the course will be held fully online.
Tentative program (subject to changes):
Lesson 1 - Fundaments: Bayesian and frequentist probability, theory of measure, correlation and causality, distributions
Lesson 2 - Point and Interval estimation: maximum likelihood methods, confidence intervals, most probable values, credible intervals
Lesson 3 - Test of hypotheses: frequentist and Bayesian tests, CLs, significance, look-elsewhere effect, reproducibility crysis
Lesson 4 - Commonly-used methods in particle physics: unfolding, ABCD, ABC, MCMC
Lesson 5 - Machine Learning: overview and mathematical foundations, generalities most used algorithms, automatic Differentiation and Deep Learning
for the frontal lectures you just need to be able to view the video stream and/or the PDF of the lectures. For the exercise sessions, you are expected to be able to run python2/3 jupyter notebooks on your laptop/desktop, on your computing cluster, or on services like Google COLAB. I'll advertise a full list of required packages (typically as conda yml environment) a few days before the beginning of the course.
I am also trying (no guarantee) to see if I can produce a docker image with all the required software preinstalled.
Pietro Vischia (teacher and chair)
Carinne Mertens (secretariat)