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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 different mass map reconstruction methods: Kaiser-Squires (KS), Wiener filter, and GLIMPSE. KS is a direct inversion method, taking no account of survey masks or noise. The Wiener filter is well motivated for Gaussian density fields in a Bayesian framework. The GLIMPSE method uses sparsity, with the aim of reconstructing non-linearities in the density field. We compare these methods with a series of tests on the public Dark Energy Survey (DES) Science Verification (SV) data and on realistic DES simulations. The Wiener filter and GLIMPSE methods offer substantial improvement on the standard smoothed KS with a range of metrics. For both the Wiener filter and GLIMPSE convergence reconstructions we present a 12% 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 {\Lambda}CDM shear catalogues and catalogues with no mass fluctuations. This is amore » standard data vector when inferring cosmology from peak statistics. The maximum signal-to-noise value of these peak statistic data vectors was increased by a factor of 3.5 for the Wiener filter and by a factor of 9 using GLIMPSE. With simulations we measure the reconstruction of the harmonic phases, showing that the concentration of the phase residuals is improved 17% by GLIMPSE and 18% by the Wiener filter. We show that the correlation between the reconstructions from data and the foreground redMaPPer clusters is increased 18% by the Wiener filter and 32% by GLIMPSE. [Abridged]« less

Authors:
ORCiD logo [1];  [2];  [1];  [3];  [4];  [1];  [1];  [5];  [6];  [7];  [8];  [9];  [1];  [10];  [11];  [12];  [13];  [1];  [14];  [15] more »;  [16];  [17];  [17];  [18];  [6];  [14];  [18];  [19];  [20];  [1];  [21];  [22];  [11];  [17];  [23];  [24];  [22];  [25];  [15];  [14];  [11];  [26];  [27];  [8];  [28];  [6];  [29];  [30];  [11];  [6];  [31];  [15];  [32];  [33];  [34];  [25];  [19];  [11];  [35];  [19];  [36];  [37];  [38];  [39];  [40];  [35];  [41];  [10] « less
  1. Department of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT, UK
  2. 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
  3. McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
  4. Laboratoire AIM, UMR CEA-CNRS-Paris 7, Irfu, SAp/SEDI, Service d’Astrophysique, CEA Saclay, 91191 Gif-sur-Yvette Cedex, France
  5. Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA
  6. Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
  7. Department of Physics, ETH Zurich, Wolfgang-Pauli-Strasse 16, CH-8093 Zurich, Switzerland
  8. Max Planck Institute for Extraterrestrial Physics, Giessenbachstrasse, 85748 Garching, Germany; Universitäts-Sternwarte, Fakultät für Physik, Ludwig-Maximilians Universität München, Scheinerstr. 1, 81679 München, Germany
  9. Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
  10. Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, Casilla 603, La Serena, Chile
  11. Fermi National Accelerator Laboratory, P. O. Box 500, Batavia, IL 60510, USA
  12. Institute of Cosmology & Gravitation, University of Portsmouth, Portsmouth, PO1 3FX, UK; Instituto de Fisica Teorica UAM/CSIC, Universidad Autonoma de Madrid, 28049 Madrid, Spain
  13. 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
  14. 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
  15. 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
  16. Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, 08193 Bellaterra (Barcelona) Spain
  17. 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
  18. Kavli Institute for Particle Astrophysics & Cosmology, P. O. Box 2450, Stanford University, Stanford, CA 94305, USA
  19. Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
  20. Department of Physics, IIT Hyderabad, Kandi, Telangana 502285, India
  21. 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
  22. Department of Astronomy, University of Michigan, Ann Arbor, MI 48109, USA; Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
  23. Fermi National Accelerator Laboratory, P. O. Box 500, Batavia, IL 60510, USA; Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA
  24. Instituto de Fisica Teorica UAM/CSIC, Universidad Autonoma de Madrid, 28049 Madrid, Spain
  25. 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
  26. 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
  27. 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
  28. Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA
  29. Australian Astronomical Observatory, North Ryde, NSW 2113, Australia
  30. 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
  31. Department of Astrophysical Sciences, Princeton University, Peyton Hall, Princeton, NJ 08544, USA
  32. 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
  33. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA
  34. SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
  35. Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
  36. School of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ, UK
  37. Fermi National Accelerator Laboratory, P. O. Box 500, Batavia, IL 60510, USA; Department of Physics, Brandeis University, Waltham, MA 02453, USA
  38. 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
  39. Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
  40. National Center for Supercomputing Applications, 1205 West Clark St., Urbana, IL 61801, USA
  41. Institute of Cosmology & Gravitation, University of Portsmouth, Portsmouth, PO1 3FX, UK
Publication Date:
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. https://doi.org/10.1093/mnras/sty1252.
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
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.
@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 different mass map reconstruction methods: Kaiser-Squires (KS), Wiener filter, and GLIMPSE. KS is a direct inversion method, taking no account of survey masks or noise. The Wiener filter is well motivated for Gaussian density fields in a Bayesian framework. The GLIMPSE method uses sparsity, with the aim of reconstructing non-linearities in the density field. We compare these methods with a series of tests on the public Dark Energy Survey (DES) Science Verification (SV) data and on realistic DES simulations. The Wiener filter and GLIMPSE methods offer substantial improvement on the standard smoothed KS with a range of metrics. For both the Wiener filter and GLIMPSE convergence reconstructions we present a 12% 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 {\Lambda}CDM shear catalogues and catalogues with no mass fluctuations. This is a standard data vector when inferring cosmology from peak statistics. The maximum signal-to-noise value of these peak statistic data vectors was increased by a factor of 3.5 for the Wiener filter and by a factor of 9 using GLIMPSE. With simulations we measure the reconstruction of the harmonic phases, showing that the concentration of the phase residuals is improved 17% by GLIMPSE and 18% by the Wiener filter. We show that the correlation between the reconstructions from data and the foreground redMaPPer clusters is increased 18% by the Wiener filter and 32% by GLIMPSE. [Abridged]},
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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