Category: Publications

All CosmoStat publications

Rethinking data-driven point spread function modeling with a differentiable optical model

Posted on by Jean-Luc Starck

 

Authors:

Tobias Liaudat, Jean-Luc Starck, Martin Kilbinger, Pierre-Antoine Frugier
Journal: Inverse Problems
Year: 2023
DOI:  
Download: ADS | arXiv

Abstract

In astronomy, upcoming space telescopes with wide-field optical instruments have a spatially varying point spread function (PSF). Specific scientific goals require a high-fidelity estimation of the PSF at target positions where no direct measurement of the PSF is provided. Even though observations of the PSF are available at some positions of the field of view (FOV), they are undersampled, noisy, and integrated into wavelength in the instrument's passband. PSF modeling represents a challenging ill-posed problem, as it requires building a model from these observations that can infer a super-resolved PSF at any wavelength and position in the FOV. Current data-driven PSF models can tackle spatial variations and super-resolution. However, they are not capable of capturing PSF chromatic variations. Our model, coined WaveDiff, proposes a paradigm shift in the data-driven modeling of the point spread function field of telescopes. We change the data-driven modeling space from the pixels to the wavefront by adding a differentiable optical forward model into the modeling framework. This change allows the transfer of a great deal of complexity from the instrumental response into the forward model. The proposed model relies on efficient automatic differentiation technology and modern stochastic first-order optimization techniques recently developed by the thriving machine-learning community. Our framework paves the way to building powerful, physically motivated models that do not require special calibration data. This paper demonstrates the WaveDiff model in a simplified setting of a space telescope. The proposed framework represents a performance breakthrough with respect to the existing state-of-the-art data-driven approach. The pixel reconstruction errors decrease six-fold at observation resolution and 44-fold for a 3x super-resolution. The ellipticity errors are reduced at least 20 times, and the size error is reduced more than 250 times. By only using noisy broad-band in-focus observations, we successfully capture the PSF chromatic variations due to diffraction. WaveDiff source code and examples associated with this paper are available at this link .

Deep Learning-based galaxy image deconvolution

Posted on by Jean-Luc Starck

 

Authors: Utsav Akhaury, Jean-Luc Starck, Pascale Jablonka, Frédéric Courbin, Kevin Michalewicz
Journal: A&A
Year: 2022
DOI:  
Download: ADS | arXiv

Abstract

With the onset of large-scale astronomical surveys capturing millions of images, there is an increasing need to develop fast and accurate deconvolution algorithms that generalize well to different images. A powerful and accessible deconvolution method would allow for the reconstruction of a cleaner estimation of the sky. The deconvolved images would be helpful to perform photometric measurements to help make progress in the fields of galaxy formation and evolution. We propose a new deconvolution method based on the Learnlet transform. Eventually, we investigate and compare the performance of different Unet architectures and Learnlet for image deconvolution in the astrophysical domain by following a two-step approach: a Tikhonov deconvolution with a closed-form solution, followed by post-processing with a neural network. To generate our training dataset, we extract HST cutouts from the CANDELS survey in the F606W filter (V-band) and corrupt these images to simulate their blurred-noisy versions. Our numerical results based on these simulations show a detailed comparison between the considered methods for different noise levels.

UNIONS: The impact of systematic errors on weak-lensing peak counts

UNIONS: The impact of systematic errors on weak-lensing peak counts

Posted on by Samuel Farrens

 

Authors: E. Ayçoberry, V. Ajani, A. Guinot, M. Kilbinger, V. Pettorino, S. Farrens, J.-L. Starck, R. Gavazzi, M. Hudson
Journal: A&A
Year: 2022
DOI:  
Download: ADS | arXiv

Abstract

Context. The Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) is an ongoing deep photometric multi-band survey of the Northern sky. As part of UNIONS, the Canada-France Imaging Survey (CFIS) provides r-band data which we use to study weak-lensing peak counts for cosmological inference.
Aims. We assess systematic effects for weak-lensing peak counts and their impact on cosmological parameters for the UNIONS survey. In particular, we present results on local calibration, metacalibration shear bias, baryonic feedback, the source galaxy redshift estimate, intrinsic alignment, and the cluster member dilution.

Methods. For each uncertainty and systematic effect, we describe our mitigation scheme and the impact on cosmological parameter constraints. We obtain constraints on cosmological parameters from MCMC using CFIS data and MassiveNuS N-body simulations as a model for peak counts statistics.
Results. Depending on the calibration (local versus global, and the inclusion of the residual multiplicative shear bias), the mean matter density parameter Ωm can shift up to −0.024 (−0.5σ). We also see that including baryonic corrections can shift Ωm by +0.027 (+0.5σ) with respect to the DM-only simulations. Reducing the impact of the intrinsic alignment and cluster member dilution through signal-to-noise cuts can lead to a shift in Ωm of +0.027 (+0.5σ). Finally, with a mean redshift uncertainty of ∆z ̄ = 0.03, we see that the shift of Ωm (+0.001 which corresponds to +0.02σ) is not significant.

Conclusions. This paper investigates for the first time with UNIONS weak-lensing data and peak counts the impact of systematic effects. The value of Ωm is the most impacted and can shift up to ∼ 0.03 which corresponds to 0.5σ depending on the choices for each systematics. We expect constraints to become more reliable with future (larger) data catalogues, for which the current pipeline will provide a starting point. The code used to obtain the results is available in the following Github repository.

ShapePipe: a new shape measurement pipeline and weak-lensing application to UNIONS/CFIS data

ShapePipe: a new shape measurement pipeline and weak-lensing application to UNIONS/CFIS data

Posted on by Samuel Farrens

 

Authors: A. Guinot, M. Kilbinger, S. Farrens, A. Peel, A. Pujol, M. Schmitz, J.-L. Starck, T. Erben, R. Gavazzi, S. Gwyn, M. Hudson,  H. Hiledebrandt, T. Liaudat , et. al
Journal: A&A
Year: 2022
DOI:  
Download: ADS | arXiv

Abstract

UNIONS is an ongoing collaboration that will provide the largest deep photometric survey of the Northern sky in four optical bands to date. As part of this collaboration, CFIS is taking r-band data with an average seeing of 0.65 arcsec, which is complete to magnitude 24.5 and thus ideal for weak-lensing studies. We perform the first weak-lensing analysis of CFIS r-band data over an area spanning 1700 deg2 of the sky. We create a catalogue with measured shapes for 40 million galaxies, corresponding to an effective density of 6.8 galaxies per square arcminute, and demonstrate a low level of systematic biases. This work serves as the basis for further cosmological studies using the full UNIONS survey of 4800 deg2 when completed. Here we present ShapePipe, a newly developed weak-lensing pipeline. This pipeline makes use of state-of-the-art methods such as Ngmix for accurate galaxy shape measurement. Shear calibration is performed with metacalibration. We carry out extensive validation tests on the Point Spread Function (PSF), and on the galaxy shapes. In addition, we create realistic image simulations to validate the estimated shear. We quantify the PSF model accuracy and show that the level of systematics is low as measured by the PSF residuals. Their effect on the shear two-point correlation function is sub-dominant compared to the cosmological contribution on angular scales <100 arcmin. The additive shear bias is below 5x10−4, and the residual multiplicative shear bias is at most 10−3 as measured on image simulations. Using COSEBIs we show that there are no significant B-modes present in second-order shear statistics. We present convergence maps and see clear correlations of the E-mode with known cluster positions. We measure the stacked tangential shear profile around Planck clusters at a significance higher than 4σ.

ShapePipe: A modular weak-lensing processing and analysis pipeline

ShapePipe: A modular weak-lensing processing and analysis pipeline

Posted on by Samuel Farrens

 

Authors: S. Farrens, A. Guinot, M. Kilbinger, T. Liaudat , L. Baumont, X. Jimenez, A. Peel , A. Pujol , M. Schmitz, J.-L. Starck, and A. Z. Vitorelli
Journal: A&A
Year: 2022
DOI: 10.1051/0004-6361/202243970
Download: ADS | arXiv

Abstract

We present the first public release of ShapePipe, an open-source and modular weak-lensing measurement, analysis, and validation pipeline written in Python. We describe the design of the software and justify the choices made. We provide a brief description of all the modules currently available and summarise how the pipeline has been applied to real Ultraviolet Near-Infrared Optical Northern Survey data. Finally, we mention plans for future applications and development. The code and accompanying documentation are publicly available on GitHub.

AMICO galaxy clusters in KiDS-DR3: measurement of the halo bias and power spectrum normalization from a stacked weak lensing analysis

AMICO galaxy clusters in KiDS-DR3: measurement of the halo bias and power spectrum normalization from a stacked weak lensing analysis

Posted on by Samuel Farrens

 

Authors: L. Ingoglia, G. Covone, M. Sereno, ..., S. Farrens, et al.
Journal: MNRAS
Year: 2022
DOI: 10.1093/mnras/stac046
Download: ADS | arXiv

Abstract

Galaxy clusters are biased tracers of the underlying matter density field. At very large radii beyond about 10 Mpc/\textit{h}, the shear profile shows evidence of a second-halo term. This is related to the correlated matter distribution around galaxy clusters and proportional to the so-called halo bias. We present an observational analysis of the halo bias-mass relation based on the AMICO galaxy cluster catalog, comprising around 7000 candidates detected in the third release of the KiDS survey. We split the cluster sample into 14 redshift-richness bins and derive the halo bias and the virial mass in each bin by means of a stacked weak lensing analysis. The observed halo bias-mass relation and the theoretical predictions based on the \\Lambda\CDM standard cosmological model show an agreement within \2\sigma\. The mean measurements of bias and mass over the full catalog give \M_{200c} = (4.9 \pm 0.3) \times 10^{13} M_{\odot}/\textit{h}\ and \b_h \sigma_8^2 = 1.2 \pm 0.1\. With the additional prior of a bias-mass relation from numerical simulations, we constrain the normalization of the power spectrum with a fixed matter density \\Omega_m = 0.3\, finding \\sigma_8 = 0.63 \pm 0.10\.

Early dark energy in the pre- and post-recombination epochs

Early dark energy in the pre- and post-recombination epochs

Posted on by Valeria Pettorino

Early dark energy in the pre- and postrecombination epochs

 

Authors:

  Adrià Gómez-Valent, Ziyang Zheng, Luca Amendola, Valeria Pettorino, Christof Wetterich
Journal:
PRD
Year: 07/2021
Download: PRD | Arxiv

Abstract

Dark energy could play a role at redshifts z≫O(1). Many quintessence models possess scaling or attractor solutions where the fraction of dark energy follows the dominant component in previous epochs of the Universe’s expansion, or phase transitions may happen close to the time of matter-radiation equality. A non-negligible early dark energy (EDE) fraction around matter-radiation equality could contribute to alleviate the well-known H0 tension. In this work, we constrain the fraction of EDE using two approaches: first, we use a fluid parameterization that mimics the plateaux of the dominant components in the past. An alternative tomographic approach constrains the EDE density in binned redshift intervals. The latter allows us to reconstruct the evolution of Ωde(z) before and after the decoupling of the cosmic microwave background (CMB) photons. We have employed Planck data 2018, the Pantheon compilation of supernovae of Type Ia (SNIa), data on galaxy clustering, the prior on the absolute magnitude of SNIa by SH0ES, and weak lensing data from KiDS+VIKING−450 and DES-Y1. When we use a minimal parameterization mimicking the background plateaux, EDE has only a small impact on current cosmological tensions. We show how the constraints on the EDE fraction weaken considerably when its sound speed is allowed to vary. By means of our binned analysis we put very tight constraints on the EDE fraction around the CMB decoupling time, ≲0.4% at 2σ c.l. We confirm previous results that a significant EDE fraction in the radiation-dominated epoch loosens the H0 tension, but tends to worsen the tension for σ8. A subsequent presence of EDE in the matter-dominated era helps to alleviate this issue. When both the SH0ES prior and weak lensing data are considered in the fitting analysis in combination with data from CMB, SNIa and baryon acoustic oscillations, the EDE fractions are constrained to be ≲2.6% in the radiation-dominated epoch and ≲1.5% in the redshift range z∈(100,1000) at 2σ c.l. The two tensions remain with a statistical significance of ∼2–3σ c.l. 

Press release (in Italian) by MEDIA INAF is available here.

 

Deep transfer learning for blended source identification in galaxy survey data

Deep transfer learning for blended source identification in galaxy survey data

Posted on by Samuel Farrens

 

Authors: S. Farrens, A. Lacan, A. Guinot, A. Z. Vitorelli
Journal: A&A
Year: 2022
DOI: 10.1051/0004-6361/202141166
Download: ADS | arXiv

Abstract

We present BlendHunter, a proof-of-concept deep-transfer-learning-based approach for the automated and robust identification of blended sources in galaxy survey data. We take the VGG-16 network with pre-trained convolutional layers and train the fully connected layers on parametric models of COSMOS images. We test the efficacy of the transfer learning by taking the weights learned on the parametric models and using them to identify blends in more realistic Canada-France Imaging Survey (CFIS)-like images. We compare the performance of this method to SEP (a Python implementation of SExtractor) as a function of noise level and the separation between sources. We find that BlendHunter outperforms SEP by ~ 15% in terms of classification accuracy for close blends (< 10 pixel separation between sources) regardless of the noise level used for training. Additionally, the method provides consistent results to SEP for distant blends (>10 pixel separation between sources) provided the network is trained on data with noise that has a relatively close standard deviation to that of the target images. The code and data have been made publicly available to ensure the reproducibility of the results.

NC-PDNet: a Density-Compensated Unrolled Network for 2D and 3D non-Cartesian MRI Reconstruction

Posted on by Jean-Luc Starck

Deep Learning has become a very promising avenue for magnetic resonance image (MRI) reconstruction. In this work, we explore the potential of unrolled networks for the non-Cartesian acquisition setting. We design the NC-PDNet, the first density-compensated unrolled network and validate the need for its key components via an ablation study. Moreover, we conduct some generalizability experiments to test our network in out-of-distribution settings, for example training on knee data and validating on brain data. The results show that the NC-PDNet outperforms the baseline models visually and quantitatively in the 2D settings. Additionally, in the 3D settings, it outperforms them visually. In particular, in the 2D multi-coil acquisition scenario, the NC-PDNet provides up to a 1.2 dB improvement in peak signal-to-noise ratio (PSNR) over baseline networks, while also allowing a gain of at least 1 dB in PSNR in generalization settings. We provide the opensource implementation of our network, and in particular the Non-uniform Fourier Transform in TensorFlow, tested on 2D multi-coil and 3D data.

Reference: Z. Ramzi,  Chaithya G.R.,  J.-L. Starck and P. Ciuciu “NC-PDNet: a Density-Compensated Unrolled Network for 2D and 3D non-Cartesian MRI Reconstruction”.

This conference paper presents an adaptation of unrolled networks to the challenging setup of Non-Cartesian MRI Reconstruction. It also introduces the implementation of the Non-Uniform Fast Fourier Transform in TensorFlow: tfkbnufft.
It has been accepted at ISBI 2021.

Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction

Posted on by Jean-Luc Starck

Deep neural networks have recently been thoroughly investigated as a powerful tool for MRI reconstruction. There is a lack of research, however, regarding their use for a specific setting of MRI, namely non-Cartesian acquisitions. In this work, we introduce a novel kind of deep neural networks to tackle this problem, namely density compensated unrolled neural networks, which rely on Density Compensation to correct the uneven weighting of the k-space. We assess their efficiency on the publicly available fastMRI dataset, and perform a small ablation study. Our results show that the density-compensated unrolled neural networks outperform the different baselines, and that all parts of the design are needed. We also open source our code, in particular a Non-Uniform Fast Fourier transform for TensorFlow.

Reference: Z. Ramzi,  J.-L. Starck and P. Ciuciu “Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction”.

This conference paper presents an adaptation of unrolled networks to the challenging setup of Non-Cartesian MRI Reconstruction. It also introduces the implementation of the Non-Uniform Fast Fourier Transform in TensorFlow: tfkbnufft.
It has been accepted at ISBI 2021.