Statistics

Statistics - Spring 2018

Course for Ph.D. students in High Energy Physics.
Organised by Anna Sfyrla.
Assistants / Hands-on exercises: T.J. Khoo and Stefan Gadatsch.

Thursdays 10:15-13:00. Taking place at Salle MANEP.

Course contents

  • Introduction to statistics for high energy physicists (x3, by Nicolas Berger)
  • Unfolding (x1, by Bogdan Malaescu)
  • Machine learning (x2, by Michael Kagan)

    Each lecture will be followed by hands-on exercises.

    Requirements

    Students are required to have ROOT (incl. pyROOT) installed in their laptops before coming to the first lecture. We will be using PYTHON for most of the coding part. It is assumed that all students have a cern account and access to swan.web.cern.ch.

    Basic knowledge of probability and statistics is assumed. Similar for basic programming skills.

    Instructions for accessing the hands-on material

    Please make sure you go through the following steps before the first lecture!

  • You will need to create a directory in CERNBox, where you add the contents of this directory.
  • Start a session at swan.web.cern.ch with default settings.
  • Choose the CERNBox tab and select the corresponding ipynb file from the directory you had just created.
  • You are ready to get started!
  • Practical hint: if something doesn't seem to work, try swapping swan servers (link points to swan001, swan002, etc).
  • A webpage corresponding to the notebook of each lecture can be found in the links below, under Course material.

    Course evaluation

    To get the course credit, students are required to attend at least 5/6 classes and the corresponding hands-on sessions. They are required to successfully and on-time hand-in the weekly assignments. A short paper presentation will take place after the end of the course. There will be no exam.

    Course material

    Introduction to statistics for high energy physicists

  • Lecture 1 (19 April); Hands-on (with solutions)
  • Lecture 2 (26 April); Hands-on (with solutions)
  • Lecture 3 (3 May); Hands-on

    Unfolding

  • Lecture (17 May); Hands-on

    Machine learning

  • Lecture 1 (24 May); Hands-on
  • Lecture 2 (31 May); Hands-on (a) (b)

    Useful / interesting links

    Other online courses

  • Glen Cowan’s Cours d’Hiver and 2010 CERN Academic Training lectures
  • Louis Lyons’and Lorenzo Moneta’s CERN Academic Training Lectures

    Various

  • LEE-like study in ATLAS/CMS SUSY searches: arXiv:1410.2270 and arXiv:1209.3522