arXiv subjects

Jean-Luc Starck

Publications and source records attributed to Jean-Luc Starck.

At least 19 recordsLinked to original sources

arXiv2026

Mitigating baryonic effects in weak lensing with higher-order statistics

Weak gravitational lensing is a premier cosmological probe, but its small-scale statistical power is compromised by baryonic feedback. Higher-order statistics capture non-Gaussian information that the power spectrum misses, yet their sensitivity to feedback remains a concern for Stage IV surveys. We quantify how unmodeled feedback biases the cosmological parameters inferred from the angular power spectrum (PS), starlet peak counts, and the starlet $\ell_1$-norm, and we determine the scale cuts needed to remove that bias. We also test the Bernardeau-Nishimichi-Taruya (BNT) transform as a strategy for more precise scale cuts. Our analysis is based on the cosmoGRID V1 suite, which imprints feedback on dark matter convergence maps with a baryon correction model, and on simulation-based inference with neural posterior estimation, carried out across footprints ranging from Stage III-like to the full sky. We find that biases grow with survey area, exceeding $2\sigma$ for all three statistics at Stage IV-like footprints, and removing them costs a substantial fraction of the signal. Restricted to these ``baryon-safe'' scales, the starlet $\ell_1$-norm still reaches a figure of merit almost twice that of the PS. The BNT transform localizes the baryonic sensitivity to the lowest transformed redshift bin and improves the PS figure of merit by a factor of $\sim$1.4, while its linear mixing of shape noise inflates the contours of the map-based higher-order statistics. Higher-order statistics therefore deliver a substantial gain over the power spectrum with no baryonic modeling at all, and an even larger one as modeling improves.

astro-ph.CO↗

arXiv2026

Point spread function wavefront recovery from in-focus stellar observations

Recovering the wavefront error (WFE) field of an optical system from intensity in-focus observations is a challenging inverse problem with broad implications for telescope point spread function (PSF) modelling. Accurate WFE recovery enables both precise PSF modelling and direct insight into the state of the telescope optics, facilitating the detection of potential malfunctions. Recently, non-parametric PSF models have shown promising performance in modelling complex optical systems in space-based telescopes. WaveDiff is a semi-parametric PSF model that represents the PSF in wavefront space by combining parametric and learnable features with a differentiable forward optical model. This parameterisation enables phase retrieval from in-focus observations by exploiting the spatial variation of the PSF across the field of view (FOV). The original version of WaveDiff achieves outstanding PSF recovery results in pixel space; however, the recovered WFE is far from the ground truth, with a relative error of around $30 \%$. In this paper, we present a new optimisation scenario that bridges WaveDiff's parametric and non-parametric components through wavefront feature projection, yielding a substantial improvement in WFE recovery and making WaveDiff the first demonstrated method to combine wide-field WFE recovery, in-focus-only polychromatic observations, and non-parametric wavefront features in a single framework. We show that incorporating wavefront projections and increasing the number of optimisation cycles enables WaveDiff to recover the WFE with an error of approximately $3 \%$ using only noisy, undersampled, in-focus observations. This represents a tenfold improvement over the original model while further reducing the pixel-space error. The code to reproduce the results of this article is publicly available at https://github.com/tobias-liaudat/wf-psf/tree/v1.4.0

astro-ph.IM↗

arXiv2026

Cosmology with Intensity Mapping via Statistics Beyond the Power Spectrum in the SKAO Era

The cosmological distribution of neutral hydrogen (HI) during the post-reionization era is highly non-Gaussian due to the underlying non-linear structure formation, complex galaxy biasing, and potential primordial non-Gaussianity. One needs higher-order (beyond two-point) statistics to maximally extract the non-Gaussian information out of the 21-cm intensity maps. This chapter summarizes the potential of several higher-order statistics, including voxel intensity distribution, emission line stacking, probability density functions, $\ell_1$-norm, bispectrum, and various marked statistics. Additionally, image-based morphological descriptors, such as the Largest Cluster Statistic, local dimensions, and Minkowski functionals, etc., can potentially characterize the morphology and geometry of the cosmic web encoded in the 21-cm intensity maps. This chapter presents forecasts of the detectability of these higher-order statistics in the context of the future SKAO observations. These forecasts incorporate instrumental noise, observational effects, and, in some cases, foreground removal in their analyses. With its unprecedented sensitivity, the future SKAO 21-cm observations will enable us to measure these higher-order statistics more precisely, possibly helping to break degeneracies between astrophysical and cosmological parameters, and maximizing the science outcome from these surveys.

astro-ph.CO↗

arXiv2026

Methodological Frontiers in 21-cm Intensity Mapping: the Treatment of Systematics and Foreground Contamination

The distribution of neutral hydrogen (HI) in the post-reionization universe traces the cosmic large-scale structure and therefore serves as a powerful cosmological probe. An efficient way to measure its distribution over wide sky areas and redshift ranges is through single-dish intensity mapping, which exploits the autocorrelation signal of each dish in a telescope array while scanning the same sky patch. Thanks to its broad frequency coverage and technical capabilities, SKA-Mid will enable measurements of the integrated 21 cm emission from HI up to redshift $z\sim3$, making single-dish intensity mapping a key observable for probing dark matter and dark energy. Isolating the faint 21 cm cosmological signal without introducing biases is, however, challenging. The 21 cm signal is several orders of magnitude weaker than the astrophysical foregrounds, and its analysis is further affected by instrumental systematics. Overcoming these difficulties requires detailed modelling together with continuous improvements and innovations in data-analysis techniques. Over the past decade, the international community has developed and tested new methods to address current observational challenges and prepare for forthcoming SKA-Mid observations. This chapter reviews recent advances in map-making and component-separation techniques, with particular emphasis on telescope-specific systematics such as beam response and correlated noise. We focus on results obtained in controlled simulation environments, providing a valuable framework for assessing the strengths and limitations of different approaches. Developing robust algorithms capable of accurately handling instrumental effects and sky-model uncertainties is a crucial step toward fully exploiting the cosmological potential of HI intensity-mapping surveys in the SKA Observatory era.

astro-ph.CO↗

arXiv2026

argosim: a Python package for radio interferometric simulations

In this paper, we present argosim, a Python package for simulating radio interferometric observations. The argosim package is modular, lightweight and compatible with all major operating systems. Its computational backend is written in JAX, which allows for greatly accelerated performance as well as the advantage of being fully differentiable. We detail the main argosim modules and describe how to use them to generate an observation, from the antenna positions to the cleaned image. The package is a fully open-source project, and its code is publicly available on GitHub.

astro-ph.IM↗

arXiv2026

Beyond power spectrum to unveil systematics on HI intensity maps

HI intensity mapping is a promising technique to probe large-scale structure, traditionally analyzed via two-point statistics such as the angular power spectrum. This technique has proven very powerful but may miss key non-Gaussian information present in the signal. We extend the starlet l1-norm, a multi-scale higher-order statistic previously applied to weak lensing maps, to the brightness temperature fluctuations of the HI density field. The HI signal is highly non-Gaussian at late times (z < 1) due to nonlinear structure growth, motivating the use of advanced summary statistics. We simulated full-sky HI lognormal brightness temperature maps using CAMB and GLASS, generating 10,000 realizations with associated cosmological parameters. We extracted both the starlet l1-norm and angular power spectrum from these maps. Using the JaxILI framework, we performed neural density estimation for implicit likelihood inference. The analysis considered simulated maps incorporating realistic noise and telescope beam, capturing the impact of observational effects on parameter inference. In this work, we focus on the redshift range 0.4 < z < 0.45, chosen to match the interval already targeted by existing MeerKLASS observations. The starlet l1-norm significantly outperforms the angular power spectrum in constraining cosmological parameters, achieving almost a 3x improvement in the figure of merit relative to the angular power spectrum by capturing non-Gaussian features missed by two-point statistics. Moreover, our results suggest that the starlet l1-norm is robust to several of the systematic effects included in our simulations. Our findings highlight the potential of multi-scale higher-order statistics such as the starlet l1-norm to enhance cosmological inference from future HI intensity mapping surveys.

astro-ph.CO↗

arXiv2026

A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping

Upcoming stage-IV surveys such as Euclid and Rubin will deliver vast amounts of high-precision data, opening new opportunities to constrain cosmological models with unprecedented accuracy. A key step in this process is the reconstruction of the dark matter distribution from noisy weak-lensing shear measurements. Current deep-learning-based mass-mapping methods achieve high reconstruction accuracy, but either require retraining a model for each new observed sky region (limiting practicality) or rely on slow Markov chain Monte Carlo sampling. Efficient exploitation of future survey data therefore calls for a new method that is accurate, flexible, and fast at inference. In addition, an uncertainty quantification with coverage guarantees is essential for a reliable cosmological parameter estimation. We introduce PnPMass, a plug-and-play approach for weak-lensing mass mapping. The algorithm produces point estimates by alternating between a gradient descent step with a carefully chosen data fidelity term and a denoising step implemented with a single deep-learning model trained on simulated data corrupted by Gaussian white noise. We also propose a fast sampling-free uncertainty quantification scheme based on moment networks, with calibrated error bars obtained through conformal prediction to ensure coverage guarantees. Finally, we benchmark PnPMass against model-driven and data-driven mass-mapping techniques. PnPMass achieves a performance close to that of the currently best deep-learning methods while offering fast inference. It converges in just a few iterations, and it requires only a single training phase, regardless of the noise covariance of the observations. It therefore combines flexibility, efficiency, and reconstruction accuracy while delivering tighter error bars than existing approaches, making it well suited for upcoming weak-lensing surveys.

astro-ph.CO↗

arXiv2026

Deep and Sparse Denoising Benchmarks for Spectral Data Cubes of High-z Galaxies: From Simulations to ALMA observations

Beyond cosmic noon, galaxies appear as faint whispers amid noise, yet this epoch is key to understanding massive galaxy assembly. ALMA's sensitivity to cold dust and [C II] emission allows us to probe their interstellar medium, but faint signals make robust denoising essential. We evaluate and benchmark denoising strategies including Principal Component Analysis, Independent Component Analysis, sparse unsupervised representations: iterative soft thresholding with 2D-1D wavelets, and supervised deep learning with a 3D U-Net, to identify techniques that suppress noise while preserving flux and morphology across peak SNRs of 2.5-8, applied to (i) synthetic spectral cubes of rotating toy disk galaxies, (ii) synthetic [C II] IFU cubes from FIRE simulations, and (iii) ALMA [C II] observations of CRISTAL galaxies and W2246-0526. Performance is assessed via RMSE, conservation of flux and spectra, noise reduction, and SNR improvement of the central galaxy. For synthetic cubes: PCA and ICA provide marginal improvement; IST reduces noise effectively at moderate SNRs but can suppress emission at low SNRs; and the U-Net outperforms IST, though it can produce quantifiable hallucinations at lower-SNRs. For moderate-SNR observations (ALMA-CRISTAL), U-Net and IST achieve comparable performance, conserving >91% flux and increasing SNR by >6. However, for observations with complex morphologies absent in the training set (W2246), the U-Net underperforms relative to IST, recovering ~80% flux, while IST robustly conserves flux and improves SNR by ~3, highlighting generalisation challenges and the need for physically-motivated training priors. We conclude that IST is a robust unsupervised denoiser for moderate-SNR data, and a synthetically trained U-Net generalises effectively to real data, dependent on training priors. This framework offers a pathway for transferable denoising for ALMA, VLT/MUSE, and JWST.

astro-ph.GA↗

arXiv2025

CHEM: Estimating and Understanding Hallucinations in Deep Learning for Image Processing

Deep learning-based methods have recently achieved significant success in image reconstruction problems. However, challenges have emerged, as these methods may generate unrealistic artifacts or hallucinations, which can interfere with analysis in safety-critical scenarios. This paper introduces a framework for quantifying and characterizing hallucinated artifacts in image reconstruction models. The proposed method, termed the Conformal Hallucination Estimation Metric (CHEM), enables the identification of hallucination-prone regions in model predictions. It leverages wavelet and shearlet representations to localize such regions at the level of image features, and uses conformalized quantile regression to assess hallucination levels in a distribution-free manner. A theoretical analysis is provided, characterizing the sensitivity of CHEM to hallucinated artifacts and its relationship to the mean squared error. Building on these insights and adopting a viewpoint grounded in approximation theory, we investigate why U-shaped networks, widely used architectures for image reconstruction, tend to hallucination-prone predictions. We assess the effectiveness of the proposed approach on astronomical image deconvolution using the CANDELS dataset with architectures such as U-Net, SwinUNet, and Learnlets, and on natural image super-resolution using the DIV2K dataset with models such as DRUNet, Unfolded DRS, RAM, and DPS.

cs.CV↗

arXiv2025

Foreground removal in HI 21 cm intensity mapping under frequency-dependent beam distortions

Neutral hydrogen (HI) intensity mapping with single-dish experiments is a powerful approach for probing cosmology in the post-reionization epoch. However, the presence of bright foregrounds over four orders of magnitude stronger than the HI signal makes its extraction highly challenging. While all methods perform well when assuming a Gaussian beam degraded to the worst resolution, most of them degrade significantly in the presence of a more realistic beam model. In this work, we investigate the performance of SDecGMCA. This method extends DecGMCA to spherical data, combining sparse component separation with beam deconvolution. Our goal is to evaluate this method in comparison with established foreground removal techniques, assessing its ability to recover the cosmological HI signal from single-dish intensity mapping observations under varying beam conditions. We use simulated HI signal and foregrounds, covering the frequency ranges relevant to MeerKAT and SKA-Mid. The foreground removal techniques tested fall into two main categories: model-fitting methods (polynomial and parametric) and blind source separation methods (PCA, ICA, GMCA, and SDecGMCA). Their effectiveness is evaluated based on the recovery of the HI angular and frequency power spectra under progressively more realistic beam conditions. While all methods perform adequately under a uniform degraded beam, SDecGMCA remains robust when frequency-dependent beam distortions are introduced. In the oscillating beam case, SDecGMCA suppresses the spurious spectral peak at $k_\nu \sim 0.3$ and achieves $\lesssim 5\%$ accuracy at intermediate angular scales ($10 < \ell < 200$), outperforming other methods. Beam inversion, however, remains intrinsically unstable beyond $\ell \sim 200$, setting a practical limit on the method.

astro-ph.CO↗

arXiv2025

Generative modeling of convergence maps based on predicted one-point statistics

Context: Weak gravitational lensing is a key cosmological probe for current and future large-scale surveys. While power spectra are commonly used for analyses, they fail to capture non-Gaussian information from nonlinear structure formation, necessitating higher-order statistics and methods for efficient map generation. Aims: To develop an emulator that generates accurate convergence maps directly from an input power spectrum and wavelet l1-norm without relying on computationally intensive simulations. Methods: We use either numerical or theoretical predictions to construct convergence maps by iteratively adjusting wavelet coefficients to match target marginal distributions and their inter-scale dependencies, incorporating higher-order statistical information. Results: The resulting kappa maps accurately reproduce the input power spectrum and exhibit higher-order statistical properties consistent with the input predictions, providing an efficient tool for weak lensing analyses.

astro-ph.CO↗

arXiv2025

Impact of weak-lensing mass-mapping algorithms on cosmology inference

Weak-lensing mass-mapping algorithms, which reconstruct the convergence field from galaxy shear measurements, are crucial for extracting higher-order statistics to constrain cosmological parameters. However, only limited research has explored whether the choice of mass-mapping algorithm affects the inference of cosmological parameters from weak-lensing higher-order statistics. This study aims to evaluate the impact of different mass-mapping algorithms on the inference of cosmological parameters measured with weak-lensing peak counts. We employ Kaiser-Squires, inpainting Kaiser-Squires, and MCALens mass-mapping algorithms to reconstruct the convergence field from simulated weak-lensing data. Using these maps, we compute the peak counts and wavelet peak counts as data vectors and perform Bayesian analysis with MCMC sampling to estimate posterior distributions of cosmological parameters. Our results indicate that the choice of mass-mapping algorithm significantly affects the constraints on cosmological parameters, with the MCALens method improving constraints by up to 157$\%$ compared to the standard Kaiser-Squires method. This improvement arises from MCALens' ability to better capture small-scale structures. In contrast, inpainting Kaiser-Squires yields constraints similar to Kaiser-Squires, indicating a limited benefit from inpainting for cosmological parameter estimation with peaks. The accuracy of mass-mapping algorithms is thus critical for cosmological inference from weak-lensing data. Advanced algorithms like MCALens, which offer superior reconstruction of the convergence field, can substantially enhance the precision of cosmological parameter estimates. These findings underscore the importance of selecting appropriate mass-mapping techniques in weak-lensing studies to fully exploit the potential of higher-order statistics for cosmological research.

astro-ph.CO↗

arXiv2025

Joint multiband deconvolution for Euclid and Vera C. Rubin images

With the advent of surveys like Euclid and Vera C. Rubin, astrophysicists will have access to both deep, high-resolution images and multiband images. However, these two types are not simultaneously available in any single dataset. It is therefore vital to devise image deconvolution algorithms that exploit the best of both worlds and that can jointly analyze datasets spanning a range of resolutions and wavelengths. In this work we introduce a novel multiband deconvolution technique aimed at improving the resolution of ground-based astronomical images by leveraging higher-resolution space-based observations. The method capitalizes on the fortunate fact that the Rubin $r$, $i$, and $z$ bands lie within the Euclid VIS band. The algorithm jointly de-convolves all the data to convert the $r$-, $i$-, and $z$-band Rubin images to the resolution of Euclid by leveraging the correlations between the different bands. We also investigate the performance of deep-learning-based denoising with DRUNet to further improve the results. We illustrate the effectiveness of our method in terms of resolution and morphology recovery, flux preservation, and generalization to different noise levels. This approach extends beyond the specific Euclid-Rubin combination, offering a versatile solution to improving the resolution of ground-based images in multiple photometric bands by jointly using any space-based images with overlapping filters.

astro-ph.IM↗

arXiv2025

Optimal Neural Summarisation for Full-Field Weak Lensing Cosmological Implicit Inference

Traditionally, weak lensing cosmological surveys have been analyzed using summary statistics motivated by their analytically tractable likelihoods, or by their ability to access higher-order information, at the cost of requiring Simulation-Based Inference (SBI) approaches. While informative, these statistics are neither designed nor guaranteed to be statistically sufficient. With the rise of deep learning, it becomes possible to create summary statistics optimized to extract the full data information. We compare different neural summarization strategies proposed in the weak lensing literature, to assess which loss functions lead to theoretically optimal summary statistics to perform full-field inference. In doing so, we aim to provide guidelines and insights to the community to help guide future neural-based inference analyses. We design an experimental setup to isolate the impact of the loss function used to train neural networks. We have developed the sbi_lens JAX package, which implements an automatically differentiable lognormal wCDM LSST-Y10 weak lensing simulator. The explicit full-field posterior obtained using the Hamiltonian Monte Carlo sampler gives us a ground truth to which to compare different compression strategies. We provide theoretical insight into the loss functions used in the literature and show that some do not necessarily lead to sufficient statistics (e.g. Mean Square Error (MSE)), while those motivated by information theory (e.g. Variational Mutual Information Maximization (VMIM)) can. Our numerical experiments confirm these insights and show, in our simulated wCDM scenario, that the Figure of Merit (FoM) of an analysis using neural summaries optimized under VMIM achieves 100% of the reference Omega_c - sigma_8 full-field FoM, while an analysis using neural summaries trained under MSE achieves only 81% of the same reference FoM.

astro-ph.CO↗

arXiv2025

Breaking the degeneracy in stellar spectral classification from single wide-band images

The spectral energy distribution (SED) of observed stars in wide-field images is crucial for chromatic point spread function (PSF) modelling methods, which use unresolved stars as integrated spectral samples of the PSF across the field of view. This is particularly important for weak gravitational lensing studies, where precise PSF modelling is essential to get accurate shear measurements. Previous research has demonstrated that the SED of stars can be inferred from low-resolution observations using machine-learning classification algorithms. However, a degeneracy exists between the PSF size, which can vary significantly across the field of view, and the spectral type of stars, leading to strong limitations of such methods. We propose a new SED classification method that incorporates stellar spectral information by using a preliminary PSF model, thereby breaking this degeneracy and enhancing the classification accuracy. Our method involves calculating a set of similarity features between an observed star and a preliminary PSF model at different wavelengths and applying a support vector machine to these similarity features to classify the observed star into a specific stellar class. The proposed approach achieves a 91\% top-two accuracy, surpassing machine-learning methods that do not consider the spectral variation of the PSF. Additionally, we examined the impact of PSF modelling errors on the spectral classification accuracy.

astro-ph.IM↗

arXiv2025

Distribution-free uncertainty quantification for inverse problems: application to weak lensing mass mapping

In inverse problems, distribution-free uncertainty quantification (UQ) aims to obtain error bars with coverage guarantees that are independent of any prior assumptions about the data distribution. In the context of mass mapping, uncertainties could lead to errors that affects our understanding of the underlying mass distribution, or could propagate to cosmological parameter estimation, thereby impacting the precision and reliability of cosmological models. Current surveys, such as Euclid or Rubin, will provide new weak lensing datasets of very high quality. Accurately quantifying uncertainties in mass maps is therefore critical to perform reliable cosmological parameter inference. In this paper, we extend the conformalized quantile regression (CQR) algorithm, initially proposed for scalar regression, to inverse problems. We compare our approach with another distribution-free approach based on risk-controlling prediction sets (RCPS). Both methods are based on a calibration dataset, and offer finite-sample coverage guarantees that are independent of the data distribution. Furthermore, they are applicable to any mass mapping method, including blackbox predictors. In our experiments, we apply UQ on three mass-mapping method: the Kaiser-Squires inversion, iterative Wiener filtering, and the MCALens algorithm. Our experiments reveal that RCPS tends to produce overconservative confidence bounds with small calibration sets, whereas CQR is designed to avoid this issue. Although the expected miscoverage rate is guaranteed to stay below a user-prescribed threshold regardless of the mass mapping method, selecting an appropriate reconstruction algorithm remains crucial for obtaining accurate estimates, especially around peak-like structures, which are particularly important for inferring cosmological parameters. Additionally, the choice of mass mapping method influences the size of the error bars.

astro-ph.CO↗

arXiv2024

Theoretical wavelet $\ell_1$-norm from one-point PDF prediction

Weak gravitational lensing, resulting from the bending of light due to the presence of matter along the line of sight, is a potent tool for exploring large-scale structures, particularly in quantifying non-Gaussianities. It stands as a pivotal objective for upcoming surveys. In the realm of current and forthcoming full-sky weak-lensing surveys, the convergence maps, representing a line-of-sight integration of the matter density field up to the source redshift, facilitate field-level inference, providing an advantageous avenue for cosmological exploration. Traditional two-point statistics fall short of capturing non-Gaussianities, necessitating the use of higher-order statistics to extract this crucial information. Among the various higher-order statistics available, the wavelet $\ell_1$-norm has proven its efficiency in inferring cosmology (Ajani et al.2021). However, the lack of a robust theoretical framework mandates reliance on simulations, demanding substantial resources and time. Our novel approach introduces a theoretical prediction of the wavelet $\ell_1$-norm for weak lensing convergence maps, grounded in the principles of Large-Deviation theory. We present, for the first time, a theoretical prediction of the wavelet $\ell_1$-norm for convergence maps, derived from the theoretical prediction of their one-point probability distribution. Additionally, we explore the cosmological dependence of this prediction and validate the results on simulations. A comparison of our predicted wavelet $\ell_1$-norm with simulations demonstrates a high level of accuracy in the weakly non-linear regime. Moreover, we show its ability to capture cosmological dependence, paving the way for a more robust and efficient parameter inference process.

astro-ph.CO↗

arXiv2024

Ground-based image deconvolution with Swin Transformer UNet

As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the spatial resolution of these images. By successfully recovering clean and high-resolution images from these surveys, the objective is to deepen the understanding of galaxy formation and evolution through accurate photometric measurements. We introduce a two-step deconvolution framework using a Swin Transformer architecture. Our study reveals that the deep learning-based solution introduces a bias, constraining the scope of scientific analysis. To address this limitation, we propose a novel third step relying on the active coefficients in the sparsity wavelet framework. We conducted a performance comparison between our deep learning-based method and Firedec, a classical deconvolution algorithm, based on an analysis of a subset of the EDisCS cluster samples. We demonstrate the advantage of our method in terms of resolution recovery, generalisation to different noise properties, and computational efficiency. The analysis of this cluster sample not only allowed us to assess the efficiency of our method, but it also enabled us to quantify the number of clumps within these galaxies in relation to their disc colour. This robust technique that we propose holds promise for identifying structures in the distant universe through ground-based images.

astro-ph.IM↗