IN2P3 School of Statistics 2020

Europe/Paris
Sabine Crépé-Renaudin (LPSC Grenoble; IN2P3-CNRS)
Description

Due to the actual sanitary situation, we are discussing how to maintain the school for this year. More news to come...

[The school was planned in the region of Marseille from Monday November 30 to Friday December 4 2020 (orginal period from Monday 11 May to Friday 15 May 2020 was cancelled due to pandemy).]

The seventh edition of the IN2P3 School Of Statistics will give an overview of the concepts and tools used in particle physics, astroparticle physics and cosmology when probabilities and statistics come to play.
This school is targeted towards PhD students and senior physicists, aiming at extending their knowledge and skills in the field of statistical tools and frameworks developed for their fields.

The school combines lectures and hands-on sessions. The lectures are subdivided into three sections:

  • a reminder of the fundamental concepts used in Probabilities, Statistics and Hypothesis testing applied to physics analysis;
  • the presentation of the concepts and basics of most popular multivariate techniques;
  • and a section dedicated to actual multivariate tools and machine learning.

All lectures will be given in English. The School Of Statistics is supported by CNRS/IN2P3.

Financial support:
Main support comes from CNRS/IN2P3 however SOS 2020 get also substantial financial support from ENIGMASS, OCEVU and P2IO labex, and AMVA4NewPhysics ITN, as well as administrative and technical support from the CPPM laboratory.

Organizing Committee:

Johan Bregeon (LPSC, Grenoble), Nicolas Chanon (IP2I Lyon), Yann Coadou (CPPM, Marseille), Guillaume Mention (IRFU-DPhP, Saclay),  Sabine Crépé-Renaudin (LPSC, Grenoble) - chair-, Laurent Derome (LPSC, Grenoble), Julien Donini (LPC, Clermont), Éric Chabert (IPHC, Strasbourg), David Rousseau (IJClab, Orsay)

Administrative Support:
Angélique Pèpe (CPPM, Marseille).

Preliminary Program:

Courses:

  • Basic concepts of statistics: Romain Madar (LPC, Clermont-Ferrand)
  • Classical interval estimation, limits, systematics and beyond: Glen Cowan (Royal Holloway, UK)
  • Introduction to Machine Learning: Vincent Barra (LIMOS, Clermont-Ferrand)
  • Boosted Decision Trees: Yann Coadou (CPPM, Marseille)
  • Introduction to Deep Learning: Michael Kagan (SLAC, USA)
  • Deep learning at colliders: Jean-Roch Vlimant (CalTech, USA)
  • Multitask learning for astroparticle physics: Thomas Vuillaume (LAPP, Annecy)

Hands-on sessions:

  • Statistics: Guillaume Mention (CEA/IRFU, Saclay)
  • Machine Learning tools: David Rousseau (IJCLab, Orsay)
  • Advanced Machine Learning: Jean-Roch Vlimant (CalTech, USA)

Registration: opening of registration will be announced later

    • 11:00 12:00
      Arrival 1h
    • 12:00 14:30
      Lunch / buffet / Room Registration 2h 30m
    • 14:30 14:45
      Introduction
      Convener: Sabine Crepe-Renaudin (LPSC Grenoble; IN2P3-CNRS)
    • 14:45 16:15
      Basic concepts of statistics
      • 14:45
        Basic concepts of statistics 1/2 1h 30m
        Speaker: Romain Madar (Laboratoire de Physique Corpusculaire de Clermont-Ferrand (LPC))
    • 16:15 16:45
      Coffee break 30m
    • 16:45 18:15
      Basic concepts of statistics
      • 16:45
        Basic concepts of statistics 2/2 1h 30m
    • 19:30 20:30
      Dinner 1h
    • 09:00 10:30
      Introduction to Machine Learning
      • 09:00
        Introduction to Machine Learning 1h 30m
        Speaker: Vincent Barra (ISIMA)
    • 10:30 11:00
      Coffee Break 30m
    • 11:00 12:30
      Boosted decisions trees
    • 12:30 14:00
      Lunch 1h 30m
    • 14:00 15:30
      Statistics Hands-on
      • 14:00
        Statistics Hands-on 1h 30m
        Speaker: Guillaume MENTION (CEA Saclay)
    • 15:30 16:00
      Coffee break 30m
    • 16:00 17:30
      Introduction to Deep learning
      • 16:00
        Introduction to Deep Learning 1/2 1h 30m
        Speakers: Michael Kagan (SLAC), Michael Kagan (SLAC)
    • 19:30 20:30
      Dinner 1h
    • 09:00 10:30
      Introduction to Deep learning
      • 09:00
        Introduction to Deep Learning 2/2 1h 30m
        Speakers: Michael Kagan (SLAC), Michael Kagan (SLAC)
    • 10:30 11:00
      Coffee Break 30m
    • 11:00 12:30
      Machine Learning tools: hands-on
      • 11:00
        Machine Learning tools: hands-on 1h 30m
        Speaker: David Rousseau (IJCLab, CNRS/IN2P3, Université Paris-Saclay)
    • 12:30 17:00
      Lunch / Free afternoon 4h 30m
    • 19:30 20:30
      Dinner 1h
    • 09:00 10:30
      Classical interval estimation, limits, systematics and beyond
      • 09:00
        Classical interval estimation, limits, systematics and beyond 1/2 1h 30m
        Speaker: Glen Cowan (Royal Holloway)
    • 10:30 11:00
      Coffee Break 30m
    • 11:00 12:30
      Deep learning at colliders
      • 11:00
        Deep learning at colliders 1h 30m
        Speaker: Jean-Roch Vlimant (UCSB)
    • 12:30 14:00
      Lunch 1h 30m
    • 14:00 15:30
      Advanced Machine Learning: hands-on
      • 14:00
        Advanced Machine Learning: hands-on 1/2 1h 30m
        Speaker: Jean-Roch Vlimant (UCSB)
    • 15:30 16:00
      Coffee break 30m
    • 16:00 17:30
      Advanced Machine Learning: hands-on
      • 16:00
        Advanced Machine Learning: hands-on 2/2 1h 30m
        Speaker: Jean-Roch Vlimant (UCSB)
    • 19:30 20:30
      Dinner 1h
    • 08:30 10:00
      Classical interval estimation, limits, systematics and beyond
      • 08:30
        Classical interval estimation, limits, systematics and beyond 2/2 1h 30m
        Speaker: Glen Cowan (Royal Holloway)
    • 10:00 10:30
      Coffee Break 30m
    • 10:30 12:00
      Multitask learning for astroparticle physics
    • 12:00 12:05
      School end
      Convener: Sabine Crepe-Renaudin (LPSC Grenoble; IN2P3-CNRS)
    • 12:15 13:30
      Lunch 1h 15m
    • 13:30 13:40
      Bus departure 10m