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Journée(s) Machine Learning et Physique Nucléaire

29–30 oct. 2019
Orsay
Fuseau horaire Europe/Paris

O. Stezowski

Liste des contributions

16 / 16
1. Café d'accueil
29/10/2019 10:00
2. Introduction
29/10/2019 10:30
3. Cours d'introduction au réseaux de neurones, avec description de quelques algorithmes populaires
Julien Donini (LPC)
29/10/2019 11:00
4. Review des méthode de ML utilisées en physique de particules
Yann Coadou (CPPM, Aix-Marseille Université, CNRS/IN2P3)
29/10/2019 14:00
5. Utilisation du machine learning en imagerie radio-isotopique per-opératoire
Françoise Bouvet (IMNC)
29/10/2019 15:30
6. Machine Learning pour la discrimination gamma-neutron : études
Xavier Fabian (IPN Lyon)
29/10/2019 16:45
7. Machine Learning pour la discrimination gamma-neutron : implementation
Guillaume BAULIEU (IPNL)
29/10/2019 17:15
8. Table ronde
29/10/2019 17:45
9. Intelligence artificielle et machine learning pour l'étude de réacteurs et de scénarios électro-nucléaires
Marc ERNOULT (IPNO)
30/10/2019 09:00
12. Algorithme d'auto-apprentissage sur des signaux de courant issus de détecteurs silicium.
Marin Chabot (Institut de physique Nucléaire d'Orsay)
30/10/2019 09:30
11. GPU, Machine Learning et Online
Vincent LAFAGE (CNRS)
30/10/2019 10:00
10. Machine Learning @ ILL
Paolo Mutti (ILL)
30/10/2019 10:30

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...
13. Tracking particles in an active target
Thomas ROGER (GANIL)
30/10/2019 11:30
16. learning to unmix in gamma-ray spectrometry
Jiaxin Xu (LMRE / IRSN)
30/10/2019 11:45

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,...
14. Taming nuclear complexity using deep neural networks
Raphael-David Lasseri (CEA)
30/10/2019 12:00
15. NPB, MCMC, GPE and other funny acronyms
Alessandro Pastore (University of York)
30/10/2019 12:30

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.

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