Journée(s) Machine Learning et Physique Nucléaire

Europe/Paris
Bâtiment 108, 1er étage (Orsay)

Bâtiment 108, 1er étage

Orsay

Description

L'objectif de cette réunion est d'informer, d'échanger, de débattre sur l'application des technologies relevant du "Machine Learning" en privilégiant des utilisations en lien avec la Physique Nucléaire.

La réunion sera découpée en quatre sessions :

  1. Informations générales 
    • Panorama, possibilités, enjeux et technologies seront présentés
  2. Example(s) d'application(s) concrète(s)
    • Objectif: montrer une(des) technologie(s) mises en oeuvre
  3. Retours / attentes de la communauté 
    • Objectif: collecter les besoins (actions) de la communauté, donner des pistes pour commencer ...
  4. Table ronde : discussions 

 

*** Appel à contribution fermé ***

Si vous souhaitez contribuer, notamment lors des sessions 2 ou 3, n'hésitez pas à nous envoyer vos contributions (meme si le travail sur les aspects Machine Learning n'a pas encore commencé) en donnant quelques lignes d'explications et éventuellement avec un titre provisoire

Adrien, Eric et Olivier

 

The goal of this meeting is to inform, exchange, discuss on Machine Learning technologies in particular for applications in Nuclear Physics.

The meeting is divided in four sessions :

  1. General information
    • comprehensive overview, possibilities, issues and technologies to be presented
  2. Practical exemple(s) 
    • goal: show one (or more) concrete application(s) 
  3. Feedbacks / expectations from the community
    • goal: to gather needs (actions), to provide hints to start with ...
  4. Table ronde : discussions 

 

*** Call for contributions closed ***

If you would like to give a talk, in particular in sessions 2 and 3, do not hesitate to send contributions (even if the work about Machine learning has not yet started) by giving a short explanation about the work and by providing a temporary title. 

Adrien, Eric et Olivier

 

Registration
Liste des participants
Participants
  • Adrien Matta
  • Alessandro Pastore
  • Amel KORICHI
  • Araceli Lopez-Martens
  • Armel KAMENYERO
  • Bertrand ROSSE
  • Christophe DIARRA
  • Cyril Lenain
  • Cyrille De Saint Jean
  • David Boilley
  • David Denis-Petit
  • David REGNIER
  • Dominique Touchard
  • Eric Legay
  • Françoise Bouvet
  • Guillaume BAULIEU
  • Guillaume Hupin
  • Hicham KHODJA
  • Jean-Christophe DAVID
  • Jean-Eric Ducret
  • Jiaxin Xu
  • Joana Frontera
  • Jérémie Dudouet
  • Jérôme Margueron
  • Karl Hauschild
  • Louis Lalanne
  • Marc ERNOULT
  • Marco Martini
  • Noëlie Cherrier
  • Olivier Delaune
  • Olivier Dorvaux
  • Olivier Stezowski
  • Olivier Vasseur
  • Paolo Mutti
  • Paolo Napolitani
  • Pierre Chau
  • Pierre Dossantos-Uzarralde
  • Quentin Hourdillé
  • Raphael-David Lasseri
  • Redamy Pérez-Ramos
  • Saba Ansari
  • Thomas Duguet
  • Thomas Goigoux
  • Thomas Roger
  • Viet Hung Dinh
  • Vincent LAFAGE
  • Xavier Fabian
O. Stezowski
  • Tuesday, 29 October
    • 10:00 11:00
      Accueil
      • 10:00
        Café d'accueil 30m
      • 10:30
        Introduction 30m
    • 11:00 15:30
      Introduction générale
      • 11:00
        Cours d'introduction au réseaux de neurones, avec description de quelques algorithmes populaires 1h 30m
        Speaker: Julien Donini (LPC)
      • 12:30
        Déjeuner 1h 30m
      • 14:00
        Review des méthode de ML utilisées en physique de particules 1h 30m
        Speaker: Yann Coadou (CPPM, Aix-Marseille Université, CNRS/IN2P3)
    • 15:30 20:30
      Examples d'applications
      • 15:30
        Utilisation du machine learning en imagerie radio-isotopique per-opératoire 45m
        Speaker: Françoise Bouvet (IMNC)
      • 16:15
        Pause café 30m
      • 16:45
        Machine Learning pour la discrimination gamma-neutron : études 30m
        Speaker: Xavier Fabian (IPN Lyon)
      • 17:15
        Machine Learning pour la discrimination gamma-neutron : implementation 30m
        Speaker: Guillaume BAULIEU (IPNL)
      • 17:45
        Table ronde 45m
      • 18:30
        Cocktail 2h
  • Wednesday, 30 October
    • 09:00 13:00
      Machine Learning en Physique Nucléaire
      • 09:00
        Intelligence artificielle et machine learning pour l'étude de réacteurs et de scénarios électro-nucléaires 30m
        Speaker: Marc ERNOULT (IPNO)
      • 09:30
        Algorithme d'auto-apprentissage sur des signaux de courant issus de détecteurs silicium. 30m
        Speaker: Marin Chabot (Institut de physique Nucléaire d'Orsay)
      • 10:00
        Titre à définir 30m
        Speaker: Vincent LAFAGE (CNRS)
      • 10:30
        Machine Learning @ ILL 30m

        Recently, by using deep learning methods, computers are able to surpass or come close to matching human performance on image analysis and pattern recognition. This advanced method could also help interpreting data from neutron scattering experiments. Those data contain rich scientific information about structure and dynamics of materials under investigation, and deep learning could help researchers better understand the link between experimental data and materials properties. We applied deep learning techniques to scientific neutron scattering data. This is a complex problem due to the multi-parameter space we have to deal with. We have used a convolutional neural network-based model to evaluate the quality of experimental neutron scattering images, which can be influenced by instrument configuration, sample and sample environment parameters. Sample structure can be deduced during data collection that can be therefore optimised. The neural network model can predict the experimental parameters to properly setup the instrument and derive the best measurement strategy. This results in a higher quality of data obtained in a shorter time, facilitating data analysis and interpretation.

        Speaker: Paolo Mutti (ILL)
      • 11:00
        Pause café 30m
      • 11:30
        Tracking particles in an active target 15m
        Speaker: Thomas ROGER (GANIL)
      • 11:45
        learning to unmix in gamma-ray spectrometry 15m

        In the context of gamma-ray spectrum analysis, spectral unmixing tackles the activity estimation problem as an inverse problem, where individual activities appear as mixing weights related to individual spectra. Current approaches are agnostic to the available archive of past measurements, which bring valuable information to perform accurate radionuclide activity estimation. For that purpose, we propose unrolling methods to learn a data-driven prior from the available archive of measurements. Preliminary results show the efficiency of the proposed approach, with improved estimation accuracy both in estimation bias and variance.

        Speaker: Jiaxin Xu (LMRE / IRSN)
      • 12:00
        Taming nuclear complexity using deep neural networks 30m
        Speaker: Raphael-David Lasseri (IPNO)
      • 12:30
        NPB, MCMC, GPE and other funny acronyms 30m

        In my talk i will discuss parameter estimate of a simple liquid drop model using at first simple Monte Carlo methods. The goal is to capture the correlations in the residuals and get a better estimate of error bars.
        I will then apply Gaussian Process Emulator to investigate how to study the chi2 surface and speed up the convergence process.

        Speaker: Alessandro Pastore (University of York)