S'authentifier

IN2P3/IRFU Machine Learning workshop 2024

20–22 nov. 2024
IPHC, Strasbourg
Fuseau horaire Europe/Paris

Organisers

Session

Thursday morning

21 nov. 2024, 09:00
Amphi Grïnewald (IPHC, Strasbourg)

Amphi Grïnewald

IPHC, Strasbourg

Batiment 27, BP28, 67037 Cedex 2, 23 Rue du Loess, 67200 Strasbourg

Présidents de session

Thursday morning

  • Julien DONINI (UBP/LPC/IN2P3)
  • Emmanuel Gangler (LPC)

Documents de présentation

Aucun document.

  1. 108. GammaLearn : deep learning applied to CTAO event reconstruction
    Dr Thomas Vuillaume (LAPP, Univ. Savoie Mont-Blanc, CNRS)
    21/11/2024 09:00
    Analysis : event classification, statistical analysis and inference, anomaly detection

    GammaLearn is a project to develop deep learning solutions for Imaging Atmospheric Cherenkov Telescopes data analysis and in particular for the Cherenkov Telescope Array Observatory (CTAO) currently under construction. Its first application is event reconstruction based on the images or videos recorded by Cherenkov telescopes.
    In this talk, I will present the recent results obtained applying...

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  2. 125. Stereograph: stereoscopic event reconstruction using graph neural networks applied to CTAO
    Mlle Hana Ali Messaoud (LAPP, Univ. Savoie Mont-Blanc)
    21/11/2024 09:25
    Analysis : event classification, statistical analysis and inference, anomaly detection

    The CTAO (Cherenkov Telescope Array Observatory) is an international observatory currently under construction. With more than sixty telescopes, it will eventually be the largest and most sensitive ground-based gamma-ray observatory.

    CTAO studies the high-energy universe by observing gamma rays emitted by violent phenomena (supernovae, black hole environments, etc.). These gamma rays produce...

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  3. 115. Interpretability of anomalies in featurized data with signatures
    Emmanuel Gangler (LPC)
    21/11/2024 09:50
    Analysis : event classification, statistical analysis and inference, anomaly detection

    Machine learning is often viewed as a black box when it comes to understanding its output, be it a decision or a score. Automatic anomaly detection is no exception to this rule, and quite often the data analyst is left to independently analyze the data in order to understand why a given event is tagged as an anomaly. Worst, the expert may end up scrutinizing over and over the same kind of rare...

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  4. 113. Gamma-ray spectrometry of fission fragments : ML analysis of multi-dimensional spectra
    Mattéo Ballu
    21/11/2024 10:45
    Analysis : event classification, statistical analysis and inference, anomaly detection

    The analysis of gamma radiation emitted by fission fragments has become an essential tool for studying the nuclear fission process. It allows probing the intrinsic properties of the fragments or exploring effects that are little studied experimentally, such as the sharing of excitation energy between fragments during nuclear fission.

    However, the analysis of experimental fission gamma-ray...

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  5. 120. Utilizing machine learning for the Data Analysis of AGATA’s PSA database.
    Mojahed Abushawish (Lyon-IP2I)
    21/11/2024 11:10
    Analysis : event classification, statistical analysis and inference, anomaly detection

    In-beam gamma-ray spectroscopy, particularly with high-velocity recoil nuclei, necessitates precise Doppler correction. The Advanced GAmma Tracking Array (AGATA) represents a groundbreaking development in gamma-ray spectrometers, boasting the ability to track gamma-rays within the detector. This capability leads to exceptional position resolution which ensures optimal Doppler...

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  6. 135. Grants for AI project
    David Rousseau (IJCLab, Université Paris-Saclay), Dr Thomas Vuillaume (LAPP, Univ. Savoie Mont-Blanc, CNRS)
    21/11/2024 11:35
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