Observational constraints on early coupled quintessence
By Lisa Goh
/ April 3, 2024
Authors: Lisa W. K. Goh, Joan Bachs-Esteban, Adrià Gómez-Valent, Valeria Pettorino, Javier Rubio Journal: Physical Review D Year: 2023 DOI: https://doi.org/10.1103/PhysRevD.109.023530 Download: ADS |...
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Constraining constant and tomographic coupled dark energy with low-redshift and high-redshift probes
By Lisa Goh
/ April 3, 2024
Authors: Lisa W. K. Goh, Adrià Gómez-Valent, Valeria Pettorino, Martin Kilbinger Journal: Physical Review D Year: 2023 DOI: https://doi.org/10.1103/PhysRevD.107.083503 Download: ADS | arXiv...
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Rethinking data-driven point spread function modeling with a differentiable optical model
By Jean-Luc Starck
/ February 25, 2023
Authors: Tobias Liaudat, Jean-Luc Starck, Martin Kilbinger, Pierre-Antoine Frugier Journal: Inverse Problems Year: 2023 DOI: Download: ADS | arXiv...
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Deep Learning-based galaxy image deconvolution
By Jean-Luc Starck
/ February 25, 2023
Authors: Utsav Akhaury, Jean-Luc Starck, Pascale Jablonka, Frédéric Courbin, Kevin Michalewicz Journal: A&A Year: 2022 DOI: Download: ADS | arXiv Abstract With the...
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UNIONS: The impact of systematic errors on weak-lensing peak counts
By Samuel Farrens
/ July 7, 2022
Authors: E. Ayçoberry, V. Ajani, A. Guinot, M. Kilbinger, V. Pettorino, S. Farrens, J.-L. Starck, R. Gavazzi, M. Hudson...
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NC-PDNet: a Density-Compensated Unrolled Network for 2D and 3D non-Cartesian MRI Reconstruction
Deep Learning has become a very promising avenue for magnetic resonance image (MRI) reconstruction. In this work, we explore the ...
Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction
Deep neural networks have recently been thoroughly investigated as a powerful tool for MRI reconstruction. There is a lack of ...
Starlet l1-norm for weak lensing cosmology
Starlet l1-norm for weak lensing cosmology Authors: Virginia Ajani, Jean-Luc Starck, Valeria Pettorino Journal: Astronomy & Astrophysics , Forthcoming article, ...
State-of-the-art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted ...
Faster and better sparse blind source separation through mini-batch optimization
Sparse Blind Source Separation (sBSS) plays a key role in scientific domains as different as biomedical imaging, remote sensing or ...
Multi-CCD Point Spread Function Modelling
Context. Galaxy imaging surveys observe a vast number of objects that are affected by the instrument’s Point Spread Function (PSF) ...
Probabilistic Mapping of Dark Matter by Neural Score Matching
The Dark Matter present in the Large-Scale Structure of the Universe is invisible, but its presence can be inferred through ...
XPDNet for MRI Reconstruction: an Application to the fastMRI 2020 Brain Challenge
We present a modular cross-domain neural network the XPDNet and its application to the MRI reconstruction task. This approach consists ...
Denoising Score-Matching for Uncertainty Quantification in Inverse Problems
Deep neural networks have proven extremely efficient at solving a wide range of inverse problems, but most often the uncertainty ...
Wavelets in the Deep Learning Era
Sparsity based methods, such as wavelets, have been state-of-the-art for more than 20 years for inverse problems before being overtaken ...