Category: Weak Lensing Publications

Publications related to weak gravitational lensing research

Euclid preparation: VI. Verifying the Performance of Cosmic Shear Experiments

Euclid preparation: VI. Verifying the Performance of Cosmic Shear Experiments

Posted on by Samuel Farrens

Euclid preparation: VI. Verifying the Performance of Cosmic Shear Experiments

Authors: Euclid Collaboration, P. Paykari, ..., S. Farrens, M. Kilbinger, V. Pettorino, S. Pires, J.-L. Starck, F. Sureau, et al.
Journal: Astronomy and Astrophysics
Year: 2020
DOI:  10.1051/0004-6361/201936980
Download:

ADS | arXiv

 

Abstract

Our aim is to quantify the impact of systematic effects on the inference of cosmological parameters from cosmic shear. We present an end-to-end approach that introduces sources of bias in a modelled weak lensing survey on a galaxy-by-galaxy level. Residual biases are propagated through a pipeline from galaxy properties (one end) through to cosmic shear power spectra and cosmological parameter estimates (the other end), to quantify how imperfect knowledge of the pipeline changes the maximum likelihood values of dark energy parameters. We quantify the impact of an imperfect correction for charge transfer inefficiency (CTI) and modelling uncertainties of the point spread function (PSF) for Euclid, and find that the biases introduced can be corrected to acceptable levels.

Constraining neutrino masses with weak-lensing multiscale peak counts

Constraining neutrino masses with weak-lensing multiscale peak counts

Posted on by Valeria Pettorino

Constraining neutrino masses with weak-lensing multiscale peak counts

Massive neutrinos influence the background evolution of the Universe as well as the growth of structure. Being able to model this effect and constrain the sum of their masses is one of the key challenges in modern cosmology. Weak-lensing cosmological constraints will also soon reach higher levels of precision with next-generation surveys like LSST, WFIRST and Euclid. In this context, we use the MassiveNus simulations to derive constraints on the sum of neutrino masses Mν , the present- day total matter density Ωm, and the primordial power spectrum normalization As in a tomographic setting. We measure the lensing power spectrum as second-order statistics along with peak counts as higher-order statistics on lensing convergence maps generated from the simulations. We investigate the impact of multi-scale filtering approaches on cosmological parameters by employing a starlet (wavelet) filter and a concatenation of Gaussian filters. In both cases peak counts perform better than the power spectrum on the set of parameters [Mν, Ωm, As] respectively by 63%, 40% and 72% when using a starlet filter and by 70%, 40% and 77% when using a multi-scale Gaussian. More importantly, we show that when using a multi-scale approach, joining power spectrum and peaks does not add any relevant information over considering just the peaks alone. While both multi-scale filters behave similarly, we find that with the starlet filter the majority of the information in the data covariance matrix is encoded in the diagonal elements; this can be an advantage when inverting the matrix, speeding up the numerical implementation. For the starlet case, we further identify the minimum resolution required to obtain constraints comparable to those achievable with the full wavelet decomposition and we show that the information contained in the coarse-scale map cannot be neglected.

Reference: Virginia Ajani, Austin Peel, Valeria Pettorino, Jean-Luc Starck, Zack Li, Jia Liu,  2020. More details in the paper

The first Deep Learning reconstruction of dark matter maps from weak lensing observational data

The first Deep Learning reconstruction of dark matter maps from weak lensing observational data

Posted on by Jean-Luc Starck

DeepMass: The first Deep Learning reconstruction of dark matter maps from weak lensing observational data (DES SV weak lensing data)

DeepMass

 This is the first reconstruction of dark matter maps from weak lensing observational data using deep learning. We train a convolution neural network (CNN) with a Unet based architecture on over 3.6 x 10^5 simulated data realisations with non-Gaussian shape noise and with cosmological parameters varying over a broad prior distribution.  Our DeepMass method is substantially more accurate than existing mass-mapping methods. With a validation set of 8000 simulated DES SV data realisations, compared to Wiener filtering with a fixed power spectrum, the DeepMass method improved the mean-square-error (MSE) by 11 per cent. With N-body simulated MICE mock data, we show that Wiener filtering with the optimal known power spectrum still gives a worse MSE than our generalised method with no input cosmological parameters; we show that the improvement is driven by the non-linear structures in the convergence. With higher galaxy density in future weak lensing data unveiling more non-linear scales, it is likely that deep learning will be a leading approach for mass mapping with Euclid and LSST.

Reference 1:  N. Jeffrey, F.  Lanusse, O. Lahav, J.-L. Starck,  "Learning dark matter map reconstructions from DES SV weak lensing data", Monthly Notices of the Royal Astronomical Society, in press, 2019.

 

The impact of baryonic physics and massive neutrinos on weak lensing peak statistics

The impact of baryonic physics and massive neutrinos on weak lensing peak statistics

Posted on by Samuel Farrens

The impact of baryonic physics and massive neutrinos on weak lensing peak statistics

 

Authors: M. Fong, M. Choi, V. Catlett, B. Lee, A. Peel, R. Bowyer,  L. J. King, I. G. McCarthy
Journal: MNRAS
Year: 2019
Download: ADS | arXiv

Abstract

We study the impact of baryonic processes and massive neutrinos on weak lensing peak statistics that can be used to constrain cosmological parameters. We use the BAHAMAS suite of cosmological simulations, which self-consistently include baryonic processes and the effect of massive neutrino free-streaming on the evolution of structure formation. We construct synthetic weak lensing catalogues by ray-tracing through light-cones, and use the aperture mass statistic for the analysis. The peaks detected on the maps reflect the cumulative signal from massive bound objects and general large-scale structure. We present the first study of weak lensing peaks in simulations that include both baryonic physics and massive neutrinos (summed neutrino mass Mν = 0.06, 0.12, 0.24, and 0.48 eV assuming normal hierarchy), so that the uncertainty due to physics beyond the gravity of dark matter can be factored into constraints on cosmological models. Assuming a fiducial model of baryonic physics, we also investigate the correlation between peaks and massive haloes, over a range of summed neutrino mass values. As higher neutrino mass tends to suppress the formation of massive structures in the Universe, the halo mass function and lensing peak counts are therefore modified as a function of Mν. Over most of the S/N range, the impact of fiducial baryonic physics is greater (less) than neutrinos for 0.06 and 0.12 (0.24 and 0.48) eV models. Both baryonic physics and massive neutrinos should be accounted for when deriving cosmological parameters from weak lensing observations.

Euclid: Non-parametric point spread function field recovery through interpolation on a Graph Laplacian

Euclid: Non-parametric point spread function field recovery through interpolation on a Graph Laplacian

Posted on by Jean-Luc Starck

 

Authors: M.A. Schmitz, J.-L. Starck, F. Ngole Mboula, N. Auricchio, J. Brinchmann, R.I. Vito Capobianco, R. Clédassou, L. Conversi, L. Corcione, N. Fourmanoit, M. Frailis, B. Garilli, F. Hormuth, D. Hu, H. Israel, S. Kermiche, T. D. Kitching, B. Kubik, M. Kunz, S. Ligori, P.B. Lilje, I. Lloro, O. Mansutti, O. Marggraf, R.J. Massey, F. Pasian, V. Pettorino, F. Raison, J.D. Rhodes, M. Roncarelli, R.P. Saglia, P. Schneider, S. Serrano, A.N. Taylor, R. Toledo-Moreo, L. Valenziano, C. Vuerli, J. Zoubian
Journal: submitted to A&A
Year: 2019
Download:  arXiv

 

Abstract

Context. Future weak lensing surveys, such as the Euclid mission, will attempt to measure the shapes of billions of galaxies in order to derive cosmological information. These surveys will attain very low levels of statistical error and systematic errors must be extremely well controlled. In particular, the point spread function (PSF) must be estimated using stars in the field, and recovered with high accuracy.
Aims. This paper's contributions are twofold. First, we take steps toward a non-parametric method to address the issue of recovering the PSF field, namely that of finding the correct PSF at the position of any galaxy in the field, applicable to Euclid. Our approach relies solely on the data, as opposed to parametric methods that make use of our knowledge of the instrument. Second, we study the impact of imperfect PSF models on the shape measurement of galaxies themselves, and whether common assumptions about this impact hold true in a Euclid scenario.
Methods. We use the recently proposed Resolved Components Analysis approach to deal with the undersampling of observed star images. We then estimate the PSF at the positions of galaxies by interpolation on a set of graphs that contain information relative to its spatial variations. We compare our approach to PSFEx, then quantify the impact of PSF recovery errors on galaxy shape measurements through image simulations.
Results. Our approach yields an improvement over PSFEx in terms of PSF model and on observed galaxy shape errors, though it is at present not sufficient to reach the required Euclid accuracy. We also find that different shape measurement approaches can react differently to the same PSF modelling errors.

Distinguishing standard and modified gravity cosmologies with machine learning

Distinguishing standard and modified gravity cosmologies with machine learning

Posted on by Samuel Farrens

Distinguishing standard and modified gravity cosmologies with machine learning

 

Authors: A. Peel, F. Lalande, J.-L. Starck, V. Pettorino, J. Merten,  C. Giocoli, M. Meneghetti,  M. Baldi
Journal: PRD
Year: 2019
Download: ADS | arXiv

Abstract

We present a convolutional neural network to classify distinct cosmological scenarios based on the statistically similar weak-lensing maps they generate. Modified gravity (MG) models that include massive neutrinos can mimic the standard concordance model (ΛCDM) in terms of Gaussian weak-lensing observables. An inability to distinguish viable models that are based on different physics potentially limits a deeper understanding of the fundamental nature of cosmic acceleration. For a fixed redshift of sources, we demonstrate that a machine learning network trained on simulated convergence maps can discriminate between such models better than conventional higher-order statistics. Results improve further when multiple source redshifts are combined. To accelerate training, we implement a novel data compression strategy that incorporates our prior knowledge of the morphology of typical convergence map features. Our method fully distinguishes ΛCDM from its most similar MG model on noise-free data, and it correctly identifies among the MG models with at least 80% accuracy when using the full redshift information. Adding noise lowers the correct classification rate of all models, but the neural network still significantly outperforms the peak statistics used in a previous analysis.

On the dissection of degenerate cosmologies with machine learning

On the dissection of degenerate cosmologies with machine learning

Posted on by Samuel Farrens

On the dissection of degenerate cosmologies with machine learning

 

Authors: J. Merten,  C. Giocoli, M. Baldi, M. Meneghetti, A. Peel, F. Lalande, J.-L. Starck, V. Pettorino
Journal: MNRAS
Year: 2019
Download: ADS | arXiv

Abstract

Based on the DUSTGRAIN-pathfinder suite of simulations, we investigate observational degeneracies between nine models of modified gravity and massive neutrinos. Three types of machine learning techniques are tested for their ability to discriminate lensing convergence maps by extracting dimensional reduced representations of the data. Classical map descriptors such as the power spectrum, peak counts and Minkowski functionals are combined into a joint feature vector and compared to the descriptors and statistics that are common to the field of digital image processing. To learn new features directly from the data we use a Convolutional Neural Network (CNN). For the mapping between feature vectors and the predictions of their underlying model, we implement two different classifiers; one based on a nearest-neighbour search and one that is based on a fully connected neural network. We find that the neural network provides a much more robust classification than the nearest-neighbour approach and that the CNN provides the most discriminating representation of the data. It achieves the cleanest separation between the different models and the highest classification success rate of 59% for a single source redshift. Once we perform a tomographic CNN analysis, the total classification accuracy increases significantly to 76% with no observational degeneracies remaining. Visualising the filter responses of the CNN at different network depths provides us with the unique opportunity to learn from very complex models and to understand better why they perform so well.

Cosmological parameters from weak cosmological lensing

Cosmological parameters from weak cosmological lensing

Posted on by Martin Kilbinger

 

Authors: M. Kilbinger
Journal:  
Year: 07/2018
Download: ADS| Arxiv

Abstract

In this manuscript of the habilitation à diriger des recherches (HDR), the author presents some of his work over the last ten years. The main topic of this thesis is cosmic shear, the distortion of images of distant galaxies due to weak gravitational lensing by the large-scale structure in the Universe. Cosmic shear has become a powerful probe into the nature of dark matter and the origin of the current accelerated expansion of the Universe. Over the last years, cosmic shear has evolved into a reliable and robust cosmological probe, providing measurements of the expansion history of the Universe and the growth of its structure.
I review the principles of weak gravitational lensing and show how cosmic shear is interpreted in a cosmological context. Then I give an overview of weak-lensing measurements, and present observational results from the Canada-France Hawai'i Lensing Survey (CFHTLenS), as well as the implications for cosmology. I conclude with an outlook on the various future surveys and missions, for which cosmic shear is one of the main science drivers, and discuss promising new weak cosmological lensing techniques for future observations.

 

A highly precise shape-noise-free shear bias estimator

A highly precise shape-noise-free shear bias estimator

Posted on by Samuel Farrens

 

Authors: A. Pujol, M. Kilbinger, F. Sureau & J. Bobin
Journal:  
Year: 06/2018
Download: ADS| Arxiv

Abstract

We present a new method to estimate shear measurement bias in image simulations that significantly improves its precision with respect to the state-of-the-art methods. This method is based on measuring the shear response for individual images. We generate sheared versions of the same image to measure how the shape measurement changes with the changes in the shear, so that we obtain a shear response for each original image, as well as its additive bias. Using the exact same noise realizations for each sheared version allows us to obtain an exact estimation of its shear response. The estimated shear bias of a sample of galaxies comes from the measured averages of the shear response and individual additive bias. The precision of this method supposes an improvement with respect to previous methods since our method is not affected by shape noise. As a consequence, the method does not require shape noise cancellation for a precise estimation of shear bias. The method can be easily applied to many applications such as shear measurement validation and calibration, reducing the number of necessary simulated images by a few orders of magnitude to achieve the same precision requirements.

Breaking degeneracies in modified gravity with higher (than 2nd) order weak-lensing statistics

Breaking degeneracies in modified gravity with higher (than 2nd) order weak-lensing statistics

Posted on by Samuel Farrens

Breaking degeneracies in modified gravity with higher (than 2nd) order weak-lensing statistics

 

Authors: A. Peel, V. Pettorino, C. Giocoli, J.-L. Starck, M. Baldi
Journal: A&A
Year: 2018
Download: ADS | arXiv

Abstract

General relativity (GR) has been well tested up to solar system scales, but it is much less certain that standard gravity remains an accurate description on the largest, that is, cosmological, scales. Many extensions to GR have been studied that are not yet ruled out by the data, including by that of the recent direct gravitational wave detections. Degeneracies among the standard model (ΛCDM) and modified gravity (MG) models, as well as among different MG parameters, must be addressed in order to best exploit information from current and future surveys and to unveil the nature of dark energy. We propose various higher-order statistics in the weak-lensing signal as a new set of observables able to break degeneracies between massive neutrinos and MG parameters. We have tested our methodology on so-called f(R) models, which constitute a class of viable models that can explain the accelerated universal expansion by a modification of the fundamental gravitational interaction. We have explored a range of these models that still fit current observations at the background and linear level, and we show using numerical simulations that certain models which include massive neutrinos are able to mimic ΛCDM in terms of the 3D power spectrum of matter density fluctuations. We find that depending on the redshift and angular scale of observation, non-Gaussian information accessed by higher-order weak-lensing statistics can be used to break the degeneracy between f(R) models and ΛCDM. In particular, peak counts computed in aperture mass maps outperform third- and fourth-order moments.