Improving Weak Lensing Mass Map Reconstructions using Gaussian and Sparsity Priors: Application to DES SV

Jeffrey, N.; Abdalla, F. B.; Lahav, O.; Lanusse, F.; Starck, J. -L; Leonard, A.; Kirk, D.; Chang, C.; Baxter, E.; Kacprzak, T.; Seitz, S.; Vikram, V.; Whiteway, L.; Abbott, T. M. C.; Allam, S.; Avila, S.; Bertin, E.; Brooks, D.; Rosell, A. Carnero; Kind, M. Carrasco; Carretero, J.; Castander, F. J.; Crocce, M.; Cunha, C. E.; D’Andrea, C. B.; da Costa, L. N.; Davis, C.; De Vicente, J.; Desai, S.; Doel, P.; Eifler, T. F.; Evrard, A. E.; Flaugher, B.; Fosalba, P.; Frieman, J.; García-Bellido, J.; Gerdes, D. W.; Gruen, D.; Gruendl, R. A.; Gschwend, J.; Gutierrez, G.; Hartley, W. G.; Honscheid, K.; Hoyle, B.; James, D. J.; Jarvis, M.; Kuehn, K.; Lima, M.; Lin, H.; March, M.; Melchior, P.; Menanteau, F.; Miquel, R.; Plazas, A. A.; Reil, K.; Roodman, A.; Sanchez, E.; Scarpine, V.; Schubnell, M.; Sevilla-Noarbe, I.; Smith, M.; Soares-Santos, M.; Sobreira, F.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; Thomas, D.; Walker, A. R.

doi:10.1093/mnras/sty1252

Title: Improving Weak Lensing Mass Map Reconstructions using Gaussian and Sparsity Priors: Application to DES SV

Abstract

Mapping the underlying density field, including non-visible dark matter, using weak gravitational lensing measurements is now a standard tool in cosmology. Due to its importance to the science results of current and upcoming surveys, the quality of the convergence reconstruction methods should be well understood. We compare three methods: Kaiser–Squires (KS), Wiener filter, and Glimpse. Kaiser–Squires is a direct inversion, not accounting for survey masks or noise. The Wiener filter is well-motivated for Gaussian density fields in a Bayesian framework. Glimpse uses sparsity, aiming to reconstruct non-linearities in the density field. We compare these methods with several tests using public Dark Energy Survey (DES) Science Verification (SV) data and realistic DES simulations. The Wiener filter and Glimpse offer substantial improvements over smoothed Kaiser–Squires with a range of metrics. Both the Wiener filter and Glimpse convergence reconstructions show a 12 percent improvement in Pearson correlation with the underlying truth from simulations. To compare the mapping methods’ abilities to find mass peaks, we measure the difference between peak counts from simulated ΛCDM shear catalogues and catalogues with no mass fluctuations (a standard data vector when inferring cosmology from peak statistics); the maximum signal-to-noise of these peak statistics is increased by a factormore »« less

Authors:

^[1]; Abdalla, F. B. ^[2]; Lahav, O. ^[1]; Lanusse, F. ^[3]; Starck, J. -L ^[4]; Leonard, A. ^[1]; Kirk, D. ^[1]; Chang, C. ^[5]; Baxter, E. ^[6]; Kacprzak, T. ^[7]; Seitz, S. ^[8]; Vikram, V. ^[9]; Whiteway, L. ^[1]; Abbott, T. M. C. ^[10]; Allam, S. ^[11]; Avila, S. ^[12]; Bertin, E. ^[13]; Brooks, D. ^[1]; Rosell, A. Carnero ^[14]; Kind, M. Carrasco ^[15] more »

Department of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT, UK
Department of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT, UK; Department of Physics and Electronics, Rhodes University, PO Box 94, Grahamstown, 6140, South Africa
McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Laboratoire AIM, UMR CEA-CNRS-Paris 7, Irfu, SAp/SEDI, Service d’Astrophysique, CEA Saclay, 91191 Gif-sur-Yvette Cedex, France
Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
Department of Physics, ETH Zurich, Wolfgang-Pauli-Strasse 16, CH-8093 Zurich, Switzerland
Max Planck Institute for Extraterrestrial Physics, Giessenbachstrasse, 85748 Garching, Germany; Universitäts-Sternwarte, Fakultät für Physik, Ludwig-Maximilians Universität München, Scheinerstr. 1, 81679 München, Germany
Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, Casilla 603, La Serena, Chile
Fermi National Accelerator Laboratory, P. O. Box 500, Batavia, IL 60510, USA
Institute of Cosmology & Gravitation, University of Portsmouth, Portsmouth, PO1 3FX, UK; Instituto de Fisica Teorica UAM/CSIC, Universidad Autonoma de Madrid, 28049 Madrid, Spain
CNRS, UMR 7095, Institut d’Astrophysique de Paris, F-75014, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR 7095, Institut d’Astrophysique de Paris, F-75014, Paris, France
Laboratório Interinstitucional de e-Astronomia - LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil; Observatório Nacional, Rua Gal. José Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil
Department of Astronomy, University of Illinois at Urbana-Champaign, 1002 W. Green Street, Urbana, IL 61801, USA; National Center for Supercomputing Applications, 1205 West Clark St., Urbana, IL 61801, USA
Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, 08193 Bellaterra (Barcelona) Spain
Institut d’Estudis Espacials de Catalunya (IEEC), 08193 Barcelona, Spain; Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, 08193 Barcelona, Spain
Kavli Institute for Particle Astrophysics & Cosmology, P. O. Box 2450, Stanford University, Stanford, CA 94305, USA
Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
Department of Physics, IIT Hyderabad, Kandi, Telangana 502285, India
Department of Astronomy/Steward Observatory, 933 North Cherry Avenue, Tucson, AZ 85721-0065, USA; Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA
Department of Astronomy, University of Michigan, Ann Arbor, MI 48109, USA; Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
Fermi National Accelerator Laboratory, P. O. Box 500, Batavia, IL 60510, USA; Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA
Instituto de Fisica Teorica UAM/CSIC, Universidad Autonoma de Madrid, 28049 Madrid, Spain
Kavli Institute for Particle Astrophysics & Cosmology, P. O. Box 2450, Stanford University, Stanford, CA 94305, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
Department of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT, UK; Department of Physics, ETH Zurich, Wolfgang-Pauli-Strasse 16, CH-8093 Zurich, Switzerland
Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA; Department of Physics, The Ohio State University, Columbus, OH 43210, USA
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA
Australian Astronomical Observatory, North Ryde, NSW 2113, Australia
Departamento de Física Matemática, Instituto de Física, Universidade de São Paulo, CP 66318, São Paulo, SP, 05314-970, Brazil; Laboratório Interinstitucional de e-Astronomia - LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil
Department of Astrophysical Sciences, Princeton University, Peyton Hall, Princeton, NJ 08544, USA
Institució Catalana de Recerca i Estudis Avançats, E-08010 Barcelona, Spain; Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, 08193 Bellaterra (Barcelona) Spain
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA
SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
School of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ, UK
Fermi National Accelerator Laboratory, P. O. Box 500, Batavia, IL 60510, USA; Department of Physics, Brandeis University, Waltham, MA 02453, USA
Instituto de Física Gleb Wataghin, Universidade Estadual de Campinas, 13083-859, Campinas, SP, Brazil; Laboratório Interinstitucional de e-Astronomia - LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil
Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
National Center for Supercomputing Applications, 1205 West Clark St., Urbana, IL 61801, USA
Institute of Cosmology & Gravitation, University of Portsmouth, Portsmouth, PO1 3FX, UK

Publication Date:: 2018-05-15

Research Org.:: SLAC National Accelerator Lab., Menlo Park, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Contributing Org.:: DES Collaboration

OSTI Identifier:: 1437399

Alternate Identifier(s):: OSTI ID: 1468024

Report Number(s):: FERMILAB-PUB-18-001-PPD; DES-2017-0309; arXiv:1801.08945
Journal ID: ISSN 0035-8711; 1650958; TRN: US1900322

Grant/Contract Number:: AC02-07CH11359; AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Monthly Notices of the Royal Astronomical Society

Additional Journal Information:: Journal Volume: 479; Journal Issue: 3; Journal ID: ISSN 0035-8711

Publisher:: Royal Astronomical Society

Country of Publication:: United States

Language:: English

Subject:: 79 ASTRONOMY AND ASTROPHYSICS

Citation Formats


                    Jeffrey, N., Abdalla, F. B., Lahav, O., Lanusse, F., Starck, J. -L, Leonard, A., Kirk, D., Chang, C., Baxter, E., Kacprzak, T., Seitz, S., Vikram, V., Whiteway, L., Abbott, T. M. C., Allam, S., Avila, S., Bertin, E., Brooks, D., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Castander, F. J., Crocce, M., Cunha, C. E., D’Andrea, C. B., da Costa, L. N., Davis, C., De Vicente, J., Desai, S., Doel, P., Eifler, T. F., Evrard, A. E., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gerdes, D. W., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Honscheid, K., Hoyle, B., James, D. J., Jarvis, M., Kuehn, K., Lima, M., Lin, H., March, M., Melchior, P., Menanteau, F., Miquel, R., Plazas, A. A., Reil, K., Roodman, A., Sanchez, E., Scarpine, V., Schubnell, M., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., and Walker, A. R. Improving Weak Lensing Mass Map Reconstructions using Gaussian and Sparsity Priors: Application to DES SV.  United States: N. p., 2018. 
Web.  doi:10.1093/mnras/sty1252.

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                    Jeffrey, N., Abdalla, F. B., Lahav, O., Lanusse, F., Starck, J. -L, Leonard, A., Kirk, D., Chang, C., Baxter, E., Kacprzak, T., Seitz, S., Vikram, V., Whiteway, L., Abbott, T. M. C., Allam, S., Avila, S., Bertin, E., Brooks, D., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Castander, F. J., Crocce, M., Cunha, C. E., D’Andrea, C. B., da Costa, L. N., Davis, C., De Vicente, J., Desai, S., Doel, P., Eifler, T. F., Evrard, A. E., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gerdes, D. W., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Honscheid, K., Hoyle, B., James, D. J., Jarvis, M., Kuehn, K., Lima, M., Lin, H., March, M., Melchior, P., Menanteau, F., Miquel, R., Plazas, A. A., Reil, K., Roodman, A., Sanchez, E., Scarpine, V., Schubnell, M., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., & Walker, A. R. Improving Weak Lensing Mass Map Reconstructions using Gaussian and Sparsity Priors: Application to DES SV.  United States.  https://doi.org/10.1093/mnras/sty1252

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                    Jeffrey, N., Abdalla, F. B., Lahav, O., Lanusse, F., Starck, J. -L, Leonard, A., Kirk, D., Chang, C., Baxter, E., Kacprzak, T., Seitz, S., Vikram, V., Whiteway, L., Abbott, T. M. C., Allam, S., Avila, S., Bertin, E., Brooks, D., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Castander, F. J., Crocce, M., Cunha, C. E., D’Andrea, C. B., da Costa, L. N., Davis, C., De Vicente, J., Desai, S., Doel, P., Eifler, T. F., Evrard, A. E., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gerdes, D. W., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Honscheid, K., Hoyle, B., James, D. J., Jarvis, M., Kuehn, K., Lima, M., Lin, H., March, M., Melchior, P., Menanteau, F., Miquel, R., Plazas, A. A., Reil, K., Roodman, A., Sanchez, E., Scarpine, V., Schubnell, M., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., and Walker, A. R. Tue .  
"Improving Weak Lensing Mass Map Reconstructions using Gaussian and Sparsity Priors: Application to DES SV".  United States.  https://doi.org/10.1093/mnras/sty1252.  https://www.osti.gov/servlets/purl/1437399.

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@article{osti_1437399,

  title        = {Improving Weak Lensing Mass Map Reconstructions using Gaussian and Sparsity Priors: Application to DES SV},

  author       = {Jeffrey, N. and Abdalla, F. B. and Lahav, O. and Lanusse, F. and Starck, J. -L and Leonard, A. and Kirk, D. and Chang, C. and Baxter, E. and Kacprzak, T. and Seitz, S. and Vikram, V. and Whiteway, L. and Abbott, T. M. C. and Allam, S. and Avila, S. and Bertin, E. and Brooks, D. and Rosell, A. Carnero and Kind, M. Carrasco and Carretero, J. and Castander, F. J. and Crocce, M. and Cunha, C. E. and D’Andrea, C. B. and da Costa, L. N. and Davis, C. and De Vicente, J. and Desai, S. and Doel, P. and Eifler, T. F. and Evrard, A. E. and Flaugher, B. and Fosalba, P. and Frieman, J. and García-Bellido, J. and Gerdes, D. W. and Gruen, D. and Gruendl, R. A. and Gschwend, J. and Gutierrez, G. and Hartley, W. G. and Honscheid, K. and Hoyle, B. and James, D. J. and Jarvis, M. and Kuehn, K. and Lima, M. and Lin, H. and March, M. and Melchior, P. and Menanteau, F. and Miquel, R. and Plazas, A. A. and Reil, K. and Roodman, A. and Sanchez, E. and Scarpine, V. and Schubnell, M. and Sevilla-Noarbe, I. and Smith, M. and Soares-Santos, M. and Sobreira, F. and Suchyta, E. and Swanson, M. E. C. and Tarle, G. and Thomas, D. and Walker, A. R.},

  abstractNote = {Mapping the underlying density field, including non-visible dark matter, using weak gravitational lensing measurements is now a standard tool in cosmology. Due to its importance to the science results of current and upcoming surveys, the quality of the convergence reconstruction methods should be well understood. We compare three methods: Kaiser–Squires (KS), Wiener filter, and Glimpse. Kaiser–Squires is a direct inversion, not accounting for survey masks or noise. The Wiener filter is well-motivated for Gaussian density fields in a Bayesian framework. Glimpse uses sparsity, aiming to reconstruct non-linearities in the density field. We compare these methods with several tests using public Dark Energy Survey (DES) Science Verification (SV) data and realistic DES simulations. The Wiener filter and Glimpse offer substantial improvements over smoothed Kaiser–Squires with a range of metrics. Both the Wiener filter and Glimpse convergence reconstructions show a 12 percent improvement in Pearson correlation with the underlying truth from simulations. To compare the mapping methods’ abilities to find mass peaks, we measure the difference between peak counts from simulated ΛCDM shear catalogues and catalogues with no mass fluctuations (a standard data vector when inferring cosmology from peak statistics); the maximum signal-to-noise of these peak statistics is increased by a factor of 3.5 for the Wiener filter and 9 for Glimpse. With simulations, we measure the reconstruction of the harmonic phases; the phase residuals’ concentration is improved 17 percent by Glimpse and 18 percent by the Wiener filter. The correlationbetween reconstructions from data and foreground redMaPPer clusters is increased 18 percent by the Wiener filter and 32 percent by Glimpse.},

  doi          = {10.1093/mnras/sty1252},

  journal      = {Monthly Notices of the Royal Astronomical Society},

  number       = 3,

  volume       = 479,

  place        = {United States},

  year         = {2018},

  month        = {5}

}

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Works referencing / citing this record:

Deep learning dark matter map reconstructions from DES SV weak lensing data
journal, January 2020

Jeffrey, Niall; Lanusse, François; Lahav, Ofer
Monthly Notices of the Royal Astronomical Society, Vol. 492, Issue 4
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arXiv
DOI: 10.48550/arxiv.1908.00543

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Citation Formats

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Wide-Field Lensing Mass Maps from Dark Energy Survey Science Verification Data journal, July 2015

Weak gravitational flexion journal, January 2006

Wiener Reconstruction of the Large-Scale Structure journal, August 1995

A new finite-field mass reconstruction algorithm journal, August 2001

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Bayesian weak lensing tomography: Reconstructing the 3D large-scale distribution of matter with a lognormal prior journal, December 2017

Redshift distributions of galaxies in the Dark Energy Survey Science Verification shear catalogue and implications for weak lensing journal, August 2016

Wide-Field Lensing Mass Maps from Dark Energy Survey Science Verification Data journal, July 2015

Extended Limber approximation journal, December 2008

Cosmology with Minkowski functionals and moments of the weak lensing convergence field journal, December 2013

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Cosmology and Fundamental Physics with the Euclid Satellite journal, September 2013

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Cosmology and fundamental physics with the Euclid satellite text, January 2018

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Cosmology with Minkowski functionals and moments of the weak lensing convergence field text, January 2013

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CosmoSIS: modular cosmological parameter estimation text, January 2014

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Cosmology constraints from shear peak statistics in Dark Energy Survey Science Verification data text, January 2016

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journal, April 2016

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journal, May 1996

Weak gravitational lensing
journal, January 2001

Cosmology and fundamental physics with the Euclid satellite
journal, April 2018

Remark on “algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization”
journal, November 2011

Wide-field lensing mass maps from Dark Energy Survey science verification data: Methodology and detailed analysis
journal, July 2015

Matrix-free large-scale Bayesian inference in cosmology
journal, December 2014

KiDS-450: cosmological constraints from weak lensing peak statistics – I. Inference from analytical prediction of high signal-to-noise ratio convergence peaks
journal, November 2017

HEALPix: A Framework for High‐Resolution Discretization and Fast Analysis of Data Distributed on the Sphere
journal, April 2005

The dark Energy Camera
journal, October 2015

Cosmological constraints with weak-lensing peak counts and second-order statistics in a large-field survey
journal, March 2017

CFHTLenS: combined probe cosmological model comparison using 2D weak gravitational lensing
journal, February 2013

Cosmological parameters, shear maps and power spectra from CFHTLenS using Bayesian hierarchical inference
journal, December 2016

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journal, August 2016

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journal, February 1993

Planck 2015 results : XXIII. The thermal Sunyaev-Zeldovich effect-cosmic infrared background correlation
journal, September 2016

Efficient Wiener filtering without preconditioning
journal, January 2013

Planck 2015 results : XIII. Cosmological parameters
journal, September 2016

Dark Energy Survey Year 1 results: curved-sky weak lensing mass map
journal, January 2018

KiDS-450: cosmological constraints from weak-lensing peak statistics – II: Inference from shear peaks using N-body simulations
journal, October 2017

calclens: weak lensing simulations for large-area sky surveys and second-order effects in cosmic shear power spectra
journal, August 2013

Extended Limber approximation
journal, December 2008

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journal, June 2016

High resolution weak lensing mass mapping combining shear and flexion
journal, June 2016

Cosmology with Minkowski functionals and moments of the weak lensing convergence field
journal, December 2013

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journal, March 2015

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journal, June 2013

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journal, February 2010

The Undecimated Wavelet Decomposition and its Reconstruction
journal, February 2007

Galaxies in the Hubble Ultra Deep Field. I. Detection, Multiband Photometry, Photometric Redshifts, and Morphology
journal, July 2006

Bayesian Photometric Redshift Estimation
journal, June 2000

Testing the lognormality of the galaxy and weak lensing convergence distributions from Dark Energy Survey maps
journal, August 2016

A Limited Memory Algorithm for Bound Constrained Optimization
journal, September 1995

The Redmapper Galaxy Cluster Catalog from des Science Verification data
journal, May 2016

Probabilistic Cosmological Mass Mapping from Weak Lensing Shear
journal, April 2017

Bayesian weak lensing tomography: Reconstructing the 3D large-scale distribution of matter with a lognormal prior
journal, December 2017

Wiener Reconstruction of All-Sky Galaxy Surveys in Spherical Harmonics
journal, March 1994

KiDS+GAMA: cosmology constraints from a joint analysis of cosmic shear, galaxy–galaxy lensing, and angular clustering
journal, March 2018

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journal, December 1997

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journal, December 2015

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journal, February 2001

The scale of the problem: recovering images of reionization with Generalized Morphological Component Analysis
journal, November 2012

CosmoSIS: Modular cosmological parameter estimation
journal, September 2015

Cosmology with cosmic shear observations: a review
journal, July 2015

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journal, August 2016

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journal, October 2009

Wide-Field Lensing Mass Maps from Dark Energy Survey Science Verification Data
journal, July 2015

Weak gravitational flexion
journal, January 2006

Wiener Reconstruction of the Large-Scale Structure
journal, August 1995

A new finite-field mass reconstruction algorithm
journal, August 2001

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journal, January 2001

Bayesian weak lensing tomography: Reconstructing the 3D large-scale distribution of matter with a lognormal prior
journal, December 2017

Redshift distributions of galaxies in the Dark Energy Survey Science Verification shear catalogue and implications for weak lensing
journal, August 2016

Wide-Field Lensing Mass Maps from Dark Energy Survey Science Verification Data
journal, July 2015

Extended Limber approximation
journal, December 2008

Cosmology with Minkowski functionals and moments of the weak lensing convergence field
journal, December 2013

The Undecimated Wavelet Decomposition and its Reconstruction
journal, February 2007

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journal, July 2015

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journal, September 2013

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journal, January 2017

Planck 2015 results : X. Diffuse component separation: Foreground maps
journal, September 2016

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text, January 2018

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journal, September 2016

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journal, September 2016

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text, January 2009

Cosmology and fundamental physics with the Euclid satellite
text, January 2012

CALCLENS: Weak Lensing Simulations for Large-area Sky Surveys and Second-order Effects in Cosmic Shear Power Spectra
preprint, January 2012

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text, January 2012

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text, January 2013

Matrix-free Large Scale Bayesian inference in cosmology
text, January 2014

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text, January 2014

Planck 2015 results. XIII. Cosmological parameters
text, January 2015

Hierarchical Cosmic Shear Power Spectrum Inference
text, January 2015

The Dark Energy Survey: more than dark energy - an overview
text, January 2016

Galaxy bias from the Dark Energy Survey Science Verification data: combining galaxy density maps and weak lensing maps
text, January 2016

Hierarchical Bayesian inference of galaxy redshift distributions from photometric surveys
text, January 2016

Cosmology constraints from shear peak statistics in Dark Energy Survey Science Verification data
text, January 2016

Testing the lognormality of the galaxy and weak lensing convergence distributions from Dark Energy Survey maps
text, January 2016

Probabilistic Cosmological Mass Mapping from Weak Lensing Shear
text, January 2016

Cosmological constraints with weak lensing peak counts and second-order statistics in a large-field survey
text, January 2016