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Deep CosmoStat Days

16–17 janv. 2025
CEA/DAp
Fuseau horaire Europe/Paris

Jean-Luc Starck

Liste des Contributions

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  1. 11. Introduction
    Jean-Luc Starck (CosmoStat, CEA Paris-Saclay)
    16/01/2025 10:00
  2. 13. Field-level inference for weak lensing
    Natalia Porqueres (Imperial College)
    16/01/2025 10:20

    Capturing the full information in weak lensing data requires new analysis techniques going beyond the standard two-point statistics, which discard the non-Gaussian information in the data. I will present a field-level approach that directly analyses the shear maps at the pixel level and provides uncertainties on the cosmological parameters up to a factor 5 smaller than the two-point statistics...

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  3. 2. Higher order statistics for neutral hydrogen intensity mapping
    Pauline Gorbatchev (FORTH)
    16/01/2025 10:45
  4. 6. Impact of Mass Mapping Algorithms on Cosmology Inference
    Andreas Tersenov
    16/01/2025 11:10

    Weak gravitational lensing is a powerful tool for probing the distribution of dark matter in the Universe. Mass mapping algorithms, which reconstruct the convergence field from galaxy shear measurements, play a crucial role in extracting higher-order statistics from weak lensing data in order to constrain cosmological parameters. However, there has been limited research on whether the choice...

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  5. 8. High Order Statistics and Theory
    Vilasini T.S. (CEA)
    16/01/2025 11:35
  6. 16. Towards fast and scalable uncertainty quantification for scientific imaging: Application to radio interferometric imaging
    Tobias Liaudat
    16/01/2025 14:30
  7. 4. Deep Learning Mass Mapping with Conformal Predictions
    Hubert Leterme (ENSICAEN)
    16/01/2025 14:55

    In this talk, I will present a plug-and-play (PnP) approach for reconstructing convergence maps from noisy shear measurements. The method aims to provide accurate estimates efficiently while eliminating the need to train a deep learning model for each new galaxy survey or region of the sky. Instead, the approach requires training a denoiser just once on simulated convergence maps corrupted...

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  8. 14. Plug-and-Play PET reconstruction: a fixed point approach
    16/01/2025 15:20

    Deep learning has shown great promise for improving medical image reconstruction, often surpassing traditional MBIR methods. However, concerns remain about the stability and robustness of these approaches, particularly when trained on limited data. The Plug-and-Play framework offers a promising solution, showing that a stable reconstruction can be ensured, provided conditions on the plugged...

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  9. 12. Joint multi-band deconvolution for Euclid and Vera C. Rubin images
    Utsav Akhaury (EPFL)
    16/01/2025 15:45

    With the advent of surveys like Euclid and Vera C. Rubin, astrophysicists will have access to both deep, high-resolution images, and multi-band images. However, these two conditions are not simultaneously available in any single dataset. It is therefore vital to devise image deconvolution algorithms that exploit the best of the two worlds and can jointly analyse datasets spanning a range of...

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  10. 5. Update on FornaX
    Marguerite PIERRE (CEA Saclay)
    16/01/2025 16:35
  11. 7. TITAN update
    Grigorios Tsagkatakis (FORTH)
    16/01/2025 17:00
  12. 17. Uncertainty Estimation in Bitemporal Image Segmentation
    Klea Panayidou (Cyprus)
    16/01/2025 17:25
  13. 3. Spectral data processing and exploring galaxy evolution at high redshifts with observed and simulated galaxies, and machine learning
    Arnab Lahiry (FORTH)
    16/01/2025 17:40

    Exploring different denoising methods for spectral-cube data to find the best way to extract the maximum possible signal from ALMA and JWST data. Mock IFU spectral cubes are generated from state-of-the-art high-resolution simulations - FIRE (Feedback In Realistic Environments), and methods include BSS, Wavelet transforms, and machine learning. The subsequent project (data in prep) involves...

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  14. 1. Single-channel source separation in wide-field astronomical imaging
    Emmanuel Bertin (CEA Saclay)
    17/01/2025 10:00

    Wide-field astronomical images contain a mixture of overlapping sources from very different natures, including stars, galaxies, diffuse emission, and artifacts, complicating scientific analyses. Single-channel source separation methods based on deep learning offer a direct and powerful approach to disentangle these components using only individual observations. In this presentation I will...

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  15. 9. Component Separation
    Victor Bonjean Sia Gkokou
    17/01/2025 10:30
  16. 10. SPT Component Separation Challenges
    Cail Daley (CEA)
    17/01/2025 10:55
  17. 15. Interpretable and scalable deep learning methods for blind source separation
    Christophe Kervazo
    17/01/2025 11:20

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