Category: publications_fs

F. Sureau’s publications

Euclid: The reduced shear approximation and magnification bias for Stage IV cosmic shear experiments

Euclid: The reduced shear approximation and magnification bias for Stage IV cosmic shear experiments

Posted on by Valeria Pettorino

Euclid: The reduced shear approximation and magnification bias for Stage IV cosmic shear experiments

Authors: A.C. Deshpande, ..., S. Casas, M. Kilbinger, V. Pettorino, S. Pires, J.-L. Starck, F. Sureau, et al.
Journal: Astronomy and Astrophysics
Year: 2020
DOI:  10.1051/0004-6361/201937323
Download:

ADS | arXiv

 

Abstract

Stage IV weak lensing experiments will offer more than an order of magnitude leap in precision. We must therefore ensure that our analyses remain accurate in this new era. Accordingly, previously ignored systematic effects must be addressed. In this work, we evaluate the impact of the reduced shear approximation and magnification bias, on the information obtained from the angular power spectrum. To first-order, the statistics of reduced shear, a combination of shear and convergence, are taken to be equal to those of shear. However, this approximation can induce a bias in the cosmological parameters that can no longer be neglected. A separate bias arises from the statistics of shear being altered by the preferential selection of galaxies and the dilution of their surface densities, in high-magnification regions. The corrections for these systematic effects take similar forms, allowing them to be treated together. We calculated the impact of neglecting these effects on the cosmological parameters that would be determined from Euclid, using cosmic shear tomography. To do so, we employed the Fisher matrix formalism, and included the impact of the super-sample covariance. We also demonstrate how the reduced shear correction can be calculated using a lognormal field forward modelling approach. These effects cause significant biases in Omega_m, sigma_8, n_s, Omega_DE, w_0, and w_a of -0.53 sigma, 0.43 sigma, -0.34 sigma, 1.36 sigma, -0.68 sigma, and 1.21 sigma, respectively. We then show that these lensing biases interact with another systematic: the intrinsic alignment of galaxies. Accordingly, we develop the formalism for an intrinsic alignment-enhanced lensing bias correction. Applying this to Euclid, we find that the additional terms introduced by this correction are sub-dominant.

Euclid: The selection of quiescent and star-forming galaxies using observed colours

Euclid: The selection of quiescent and star-forming galaxies using observed colours

Posted on by Valeria Pettorino

Euclid: The selection of quiescent and star-forming galaxies using observed colours

Authors: L. Bisigello, ..., V. Pettorino, S. Pires, F. Sureau, et al.
Journal: MNRAS
Year: 2020
DOI:  10.1093/mnras/staa885
Download:

ADS | arXiv

 

Abstract

The Euclid mission will observe well over a billion galaxies out to z∼6 and beyond. This will offer an unrivalled opportunity to investigate several key questions for understanding galaxy formation and evolution. The first step for many of these studies will be the selection of a sample of quiescent and star-forming galaxies, as is often done in the literature by using well known colour techniques such as the `UVJ' diagram. However, given the limited number of filters available for the Euclid telescope, the recovery of such rest-frame colours will be challenging. We therefore investigate the use of observed Euclid colours, on their own and together with ground-based u-band observations, for selecting quiescent and star-forming galaxies. The most efficient colour combination, among the ones tested in this work, consists of the (u-VIS) and (VIS-J) colours. We find that this combination allows users to select a sample of quiescent galaxies complete to above ∼70% and with less than 15% contamination at redshifts in the range 0.75<z<1. For galaxies at high-z or without the u-band complementary observations, the (VIS-Y) and (J-H) colours represent a valid alternative, with >65% completeness level and contamination below 20% at 1<z<2 for finding quiescent galaxies. In comparison, the sample of quiescent galaxies selected with the traditional UVJ technique is only ∼20% complete at z<3, when recovering the rest-frame colours using mock Euclid observations. This shows that our new methodology is the most suitable one when only Euclid bands, along with u-band imaging, are available.

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.

Euclid preparation. V. Predicted yield of redshift 7 < z < 9 quasars from the wide survey

Euclid preparation. V. Predicted yield of redshift 7 < z < 9 quasars from the wide survey

Posted on by Samuel Farrens

Euclid preparation: V. Predicted yield of redshift 7

Authors: Euclid Collaboration, R. Barnett, ..., S. Farrens, M. Kilbinger, V. Pettorino, F. Sureau, et al.
Journal: Astronomy and Astrophysics
Year: 2019
DOI:  10.1051/0004-6361/201936427
Download:

ADS | arXiv

 

Abstract

We provide predictions of the yield of 7<z<9 quasars from the Euclid wide survey, updating the calculation presented in the Euclid Red Book in several ways. We account for revisions to the Euclid near-infrared filter wavelengths; we adopt steeper rates of decline of the quasar luminosity function (QLF; Φ) with redshift, Φ∝10k(z−6), k=−0.72, and a further steeper rate of decline, k=−0.92; we use better models of the contaminating populations (MLT dwarfs and compact early-type galaxies); and we use an improved Bayesian selection method, compared to the colour cuts used for the Red Book calculation, allowing the identification of fainter quasars, down to JAB∼23. Quasars at z>8 may be selected from Euclid OYJH photometry alone, but selection over the redshift interval 7<z<8 is greatly improved by the addition of z-band data from, e.g., Pan-STARRS and LSST. We calculate predicted quasar yields for the assumed values of the rate of decline of the QLF beyond z=6. For the case that the decline of the QLF accelerates beyond z=6, with k=−0.92, Euclid should nevertheless find over 100 quasars with 7.0<z<7.5, and ∼25 quasars beyond the current record of z=7.5, including ∼8 beyond z=8.0. The first Euclid quasars at z>7.5 should be found in the DR1 data release, expected in 2024. It will be possible to determine the bright-end slope of the QLF, 7<z<8, M1450<−25, using 8m class telescopes to confirm candidates, but follow-up with JWST or E-ELT will be required to measure the faint-end slope. Contamination of the candidate lists is predicted to be modest even at JAB∼23. The precision with which k can be determined over 7<z<8 depends on the value of k, but assuming k=−0.72 it can be measured to a 1 sigma uncertainty of 0.07.

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.

Shear measurement bias: dependencies on methods, simulation parameters and measured parameters

Shear measurement bias: dependencies on methods, simulation parameters and measured parameters

Posted on by Samuel Farrens

 

Authors: A. Pujol, F. Sureau, J. Bobin et al.
Journal: A&A
Year: 06/2017
Download: ADS| Arxiv

Abstract

We present a study of the dependencies of shear and ellipticity bias on simulation (input) and measured (output) parameters, noise, PSF anisotropy, pixel size and the model bias coming from two different and independent shape estimators. We use simulated images from Galsim based on the GREAT3 control-space-constant branch and we measure ellipticity and shear bias from a model-fitting method (gFIT) and a moment-based method (KSB). We show the bias dependencies found on input and output parameters for both methods and we identify the main dependencies and causes. We find consistent results between the two methods (given the precision of the analysis) and important dependencies on orientation and morphology properties such as flux, size and ellipticity. We show cases where shear bias and ellipticity bias behave very different for the two methods due to the different nature of these measurements. We also show that noise and pixelization play an important role on the bias dependences on the output properties. We find a large model bias for galaxies consisting of a bulge and a disk with different ellipticities or orientations. We also see an important coupling between several properties on the bias dependences. Because of this we need to study several properties simultaneously in order to properly understand the nature of shear bias.

Unsupervised feature learning for galaxy SEDs with denoising autoencoders

Unsupervised feature learning for galaxy SEDs with denoising autoencoders

Posted on by Jean-Luc Starck

 

Authors: Frontera-Pons, J., Sureau, F., Bobin, J. and Le Floc'h E.
Journal: Astronomy & Astrophysics
Year: 2017
Download: ADS | arXiv

Abstract

With the increasing number of deep multi-wavelength galaxy surveys, the spectral energy distribution (SED) of galaxies has become an invaluable tool for studying the formation of their structures and their evolution. In this context, standard analysis relies on simple spectro-photometric selection criteria based on a few SED colors. If this fully supervised classification already yielded clear achievements, it is not optimal to extract relevant information from the data. In this article, we propose to employ very recent advances in machine learning, and more precisely in feature learning, to derive a data-driven diagram. We show that the proposed approach based on denoising autoencoders recovers the bi-modality in the galaxy population in an unsupervised manner, without using any prior knowledge on galaxy SED classification. This technique has been compared to principal component analysis (PCA) and to standard color/color representations. In addition, preliminary results illustrate that this enables the capturing of extra physically meaningful information, such as redshift dependence, galaxy mass evolution and variation over the specific star formation rate. PCA also results in an unsupervised representation with physical properties, such as mass and sSFR, although this representation separates out less other characteristics (bimodality, redshift evolution) than denoising autoencoders.

CMB reconstruction from the WMAP and Planck PR2 data

CMB reconstruction from the WMAP and Planck PR2 data

Posted on by Jean-Luc Starck

 

Authors:  J. Bobin, F. Sureau and J. -L. Starck
Journal: A&A
Year: 2015
Download: ADS | arXiv

Abstract

In this article, we describe a new estimate of the Cosmic Microwave Background (CMB) intensity map reconstructed by a joint analysis of the full Planck 2015 data (PR2) and WMAP nine-years. It provides more than a mere update of the CMB map introduced in (Bobin et al. 2014b) since it benefits from an improvement of the component separation method L-GMCA (Local-Generalized Morphological Component Analysis) that allows the efficient separation of correlated components (Bobin et al. 2015). Based on the most recent CMB data, we further confirm previous results (Bobin et al. 2014b) showing that the proposed CMB map estimate exhibits appealing characteristics for astrophysical and cosmological applications: i) it is a full sky map that did not require any inpainting or interpolation post-processing, ii) foreground contamination is showed to be very low even on the galactic center, iii) it does not exhibit any detectable trace of thermal SZ contamination. We show that its power spectrum is in good agreement with the Planck PR2 official theoretical best-fit power spectrum. Finally, following the principle of reproducible research, we provide the codes to reproduce the L-GMCA, which makes it the only reproducible CMB map.