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

Journal Article · Tue May 15 04:00:00 UTC 2018 · Mon.Not.Roy.Astron.Soc.

DOI: https://doi.org/10.1093/mnras/sty1252 · OSTI ID:1437399

Jeffrey, N.  ^[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]; Carnero Rosell, A. ^[14]; Carrasco Kind, M. ^[15] ; Carretero, J. ^[16]; Castander, F. J. ^[17]; Crocce, M. ^[17]; Cunha, C. E. ^[18]; D'Andrea, C. B. ^[6]; da Costa, L. N. ^[14]; Davis, C. ^[18]; De Vicente, J. ^[19]; Desai, S.  ^[20]; Doel, P. ^[1]; Eifler, T. F. ^[21]; Evrard, A. E. ^[22]; Flaugher, B.  ^[11]; Fosalba, P. ^[17]; Frieman, J.  ^[23]; García-Bellido, J.  ^[24]; Gerdes, D. W. ^[22]; Gruen, D. ^[25]; Gruendl, R. A. ^[15]; Gschwend, J. ^[14]; Gutierrez, G.  ^[11]; Hartley, W. G.  ^[26]; Honscheid, K. ^[27]; Hoyle, B. ^[8]; James, D. J. ^[28]; Jarvis, M. ^[6]; Kuehn, K. ^[29]; Lima, M. ^[30]; Lin, H.  ^[11]; March, M. ^[6]; Melchior, P. ^[31]; Menanteau, F. ^[15]; Miquel, R. ^[32]; Plazas, A. A. ^[33]; Reil, K. ^[34]; Roodman, A. ^[25]; Sanchez, E.  ^[19]; Scarpine, V. ^[11]; Schubnell, M. ^[22]; Sevilla-Noarbe, I.  ^[19]; Smith, M. ^[35]; Soares-Santos, M.  ^[36]; Sobreira, F. ^[37]; Suchyta, E. ^[38]; Swanson, M. E.C. ^[39]; Tarle, G. ^[22]; Thomas, D. ^[40]; Walker, A. R. ^[10]

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1. University Coll. London
2. University Coll. London; Rhodes U.
3. Carnegie Mellon U.
4. AIM, Saclay
5. Chicago U., KICP
6. Pennsylvania U.
7. Zurich, ETH
8. Garching, Max Planck Inst., MPE; Munich U.
9. Argonne (main)
10. Cerro-Tololo InterAmerican Obs.
11. Fermilab
12. Portsmouth U., ICG; Madrid, IFT
13. Paris, Inst. Astrophys.
14. LIneA, Rio de Janeiro; Rio de Janeiro Observ.
15. Illinois U., Urbana, Astron. Dept.; NCSA, Urbana
16. Barcelona, IFAE
17. Barcelona, IEEC; ICE, Bellaterra
18. KIPAC, Menlo Park
19. Madrid, CIEMAT
20. Indian Inst. Tech., Hyderabad
21. Arizona U., Astron. Dept. - Steward Observ.; Caltech, JPL
22. Michigan U.
23. Fermilab; Chicago U., KICP
24. Madrid, IFT
25. KIPAC, Menlo Park; SLAC
26. University Coll. London; Zurich, ETH
27. Ohio State U., CCAPP; Ohio State U.
28. Harvard-Smithsonian Ctr. Astrophys.
29. Australian Astron. Observ.
30. Sao Paulo U.; LIneA, Rio de Janeiro
31. Princeton U., Astrophys. Sci. Dept.
32. ICREA, Barcelona; Barcelona, IFAE
33. Caltech, JPL
34. SLAC
35. Southampton U.
36. Fermilab; Brandeis U.
37. Campinas State U.; LIneA, Rio de Janeiro
38. Oak Ridge
39. NCSA, Urbana
40. Portsmouth U., ICG

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 per cent 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 per cent by Glimpse and 18 per cent by the Wiener filter. The correlationbetween reconstructions from data and foreground redMaPPer clusters is increased 18 per cent by the Wiener filter and 32 per cent by Glimpse.

• Research Organization: Ohio State U.; Michigan U.; Garching, Max Planck Inst., MPE; Harvard-Smithsonian Ctr. Astrophys.; ICREA, Barcelona; Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); LIneA, Rio de Janeiro; Madrid, CIEMAT; Barcelona, IFAE; Madrid, IFT; Carnegie Mellon U.; AIM, Saclay; Portsmouth U., ICG; Campinas State U.; Indian Inst. Tech., Hyderabad; Australian Astron. Observ.; Southampton U.; Zurich, ETH; Rio de Janeiro Observ.; Ohio State U., CCAPP; SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Barcelona, IEEC; Chicago U., KICP; Illinois U., Urbana, Astron. Dept.; Princeton U., Astrophys. Sci. Dept.; Paris, Inst. Astrophys.; Cerro-Tololo InterAmerican Obs.; Munich U.; Argonne National Laboratory (ANL), Argonne, IL (United States); ICE, Bellaterra; Sao Paulo U.; Arizona U., Astron. Dept. - Steward Observ.; NCSA, Urbana; University Coll. London; Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Pennsylvania U.; Brandeis U.; Caltech, JPL; Rhodes U.
• Sponsoring Organization: US Department of Energy
• Grant/Contract Number: AC02-07CH11359
• OSTI ID: 1437399
• Report Number(s): FERMILAB-PUB-18-001-PPD; DES-2017-0309; oai:inspirehep.net:1650958; arXiv:1801.08945
• Journal Information: Mon.Not.Roy.Astron.Soc., Journal Name: Mon.Not.Roy.Astron.Soc. Journal Issue: 3 Vol. 479
• Country of Publication: United States
• Language: English