Euclid: Covariance of weak lensing pseudo-Cl estimates: Calculation, comparison to simulations, and dependence on survey geometry

An accurate covariance matrix is essential for obtaining reliable cosmological results when using a Gaussian likelihood. In this paper we study the covariance of pseudo- C ℓ estimates of tomographic cosmic shear power spectra. Using two existing publicly available codes in combination, we calculate...

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Veröffentlicht in:Astronomy and astrophysics (Berlin) 2022, Vol.660
Hauptverfasser: Upham, Robin E, Brown, M.L, Whittaker, L, Amara, A, Auricchio, N, Bonino, D, Branchini, E, Brescia, Massimo, Brinchmann, J, Capobianco, V, Carbone, C, Carretero, J, Castellano, M, Cavuoti, Stefano, Cimatti, A, Cledassou, R, Congedo, G, Conversi, L, Copin, Y, Corcione, L, Cropper, M, Da Silva, A, Degaudenzi, H, Douspis, M, Dubath, F, Duncan, C.A.J, Dupac, X, Dusini, S, Ealet, A, Farrens, S, Ferriol, S, Fosalba, P, Frailis, M, Franceschi, E, Fumana, M, Garilli, B, Gillis, B, Giocoli, C, Grupp, F, Haugan, Stein Vidar Hagfors, Hoekstra, H, Holmes, W, Hormuth, F, Jahnke, K, Kermiche, S, Kiessling, A, Kilbinger, M, Kitching, T, Kümmel, M, Kunz, M, Kurki-Suonio, Hannu, Ligori, S, Lilje, Per Barth, Lloro, I, Marggraf, O, Markovic, K, Marulli, F, Meneghetti, M, Meylan, G, Moresco, M, Munari, E, Niemi, S.M, Padilla, C, Paltani, S, Pasian, F, Pedersen, K, Pettorino, V, Pires, S, Raison, F, Rhodes, J, Rossetti, E, Saglia, R, Sartoris, B, Schneider, P, Secroun, A, Seidel, G, Sirignano, C, Sirri, G, Stanco, L, Starck, J.-L, Tallada-Crespí, P, Tavagnacco, D, Taylor, A.N, Tereno, I, Toledo-Moreo, R, Torradeflot, F, Valenziano, L, Wang, Y, Zamorani, G, Zoubian, J, Andreon, S, Baldi, M, Camera, S, Cardone, V.F, Fabbian, G, Polenta, G, Joachimi, B, Hall, A, Loureiro, A, Sellentin, E
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container_title Astronomy and astrophysics (Berlin)
container_volume 660
creator Upham, Robin E
Brown, M.L
Whittaker, L
Amara, A
Auricchio, N
Bonino, D
Branchini, E
Brescia, Massimo
Brinchmann, J
Capobianco, V
Carbone, C
Carretero, J
Castellano, M
Cavuoti, Stefano
Cimatti, A
Cledassou, R
Congedo, G
Conversi, L
Copin, Y
Corcione, L
Cropper, M
Da Silva, A
Degaudenzi, H
Douspis, M
Dubath, F
Duncan, C.A.J
Dupac, X
Dusini, S
Ealet, A
Farrens, S
Ferriol, S
Fosalba, P
Frailis, M
Franceschi, E
Fumana, M
Garilli, B
Gillis, B
Giocoli, C
Grupp, F
Haugan, Stein Vidar Hagfors
Hoekstra, H
Holmes, W
Hormuth, F
Jahnke, K
Kermiche, S
Kiessling, A
Kilbinger, M
Kitching, T
Kümmel, M
Kunz, M
Kurki-Suonio, Hannu
Ligori, S
Lilje, Per Barth
Lloro, I
Marggraf, O
Markovic, K
Marulli, F
Meneghetti, M
Meylan, G
Moresco, M
Munari, E
Niemi, S.M
Padilla, C
Paltani, S
Pasian, F
Pedersen, K
Pettorino, V
Pires, S
Raison, F
Rhodes, J
Rossetti, E
Saglia, R
Sartoris, B
Schneider, P
Secroun, A
Seidel, G
Sirignano, C
Sirri, G
Stanco, L
Starck, J.-L
Tallada-Crespí, P
Tavagnacco, D
Taylor, A.N
Tereno, I
Toledo-Moreo, R
Torradeflot, F
Valenziano, L
Wang, Y
Zamorani, G
Zoubian, J
Andreon, S
Baldi, M
Camera, S
Cardone, V.F
Fabbian, G
Polenta, G
Joachimi, B
Hall, A
Loureiro, A
Sellentin, E
description An accurate covariance matrix is essential for obtaining reliable cosmological results when using a Gaussian likelihood. In this paper we study the covariance of pseudo- C ℓ estimates of tomographic cosmic shear power spectra. Using two existing publicly available codes in combination, we calculate the full covariance matrix, including mode-coupling contributions arising from both partial sky coverage and non-linear structure growth. For three different sky masks, we compare the theoretical covariance matrix to that estimated from publicly available N -body weak lensing simulations, finding good agreement. We find that as a more extreme sky cut is applied, a corresponding increase in both Gaussian off-diagonal covariance and non-Gaussian super-sample covariance is observed in both theory and simulations, in accordance with expectations. Studying the different contributions to the covariance in detail, we find that the Gaussian covariance dominates along the main diagonal and the closest off-diagonals, but farther away from the main diagonal the super-sample covariance is dominant. Forming mock constraints in parameters that describe matter clustering and dark energy, we find that neglecting non-Gaussian contributions to the covariance can lead to underestimating the true size of confidence regions by up to 70 per cent. The dominant non-Gaussian covariance component is the super-sample covariance, but neglecting the smaller connected non-Gaussian covariance can still lead to the underestimation of uncertainties by 10–20 per cent. A real cosmological analysis will require marginalisation over many nuisance parameters, which will decrease the relative importance of all cosmological contributions to the covariance, so these values should be taken as upper limits on the importance of each component.
doi_str_mv 10.1051/0004-6361/202142908
format Article
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In this paper we study the covariance of pseudo- C ℓ estimates of tomographic cosmic shear power spectra. Using two existing publicly available codes in combination, we calculate the full covariance matrix, including mode-coupling contributions arising from both partial sky coverage and non-linear structure growth. For three different sky masks, we compare the theoretical covariance matrix to that estimated from publicly available N -body weak lensing simulations, finding good agreement. We find that as a more extreme sky cut is applied, a corresponding increase in both Gaussian off-diagonal covariance and non-Gaussian super-sample covariance is observed in both theory and simulations, in accordance with expectations. Studying the different contributions to the covariance in detail, we find that the Gaussian covariance dominates along the main diagonal and the closest off-diagonals, but farther away from the main diagonal the super-sample covariance is dominant. Forming mock constraints in parameters that describe matter clustering and dark energy, we find that neglecting non-Gaussian contributions to the covariance can lead to underestimating the true size of confidence regions by up to 70 per cent. The dominant non-Gaussian covariance component is the super-sample covariance, but neglecting the smaller connected non-Gaussian covariance can still lead to the underestimation of uncertainties by 10–20 per cent. 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M</creatorcontrib><creatorcontrib>Kitching, T</creatorcontrib><creatorcontrib>Kümmel, M</creatorcontrib><creatorcontrib>Kunz, M</creatorcontrib><creatorcontrib>Kurki-Suonio, Hannu</creatorcontrib><creatorcontrib>Ligori, S</creatorcontrib><creatorcontrib>Lilje, Per Barth</creatorcontrib><creatorcontrib>Lloro, I</creatorcontrib><creatorcontrib>Marggraf, O</creatorcontrib><creatorcontrib>Markovic, K</creatorcontrib><creatorcontrib>Marulli, F</creatorcontrib><creatorcontrib>Meneghetti, M</creatorcontrib><creatorcontrib>Meylan, G</creatorcontrib><creatorcontrib>Moresco, M</creatorcontrib><creatorcontrib>Munari, E</creatorcontrib><creatorcontrib>Niemi, S.M</creatorcontrib><creatorcontrib>Padilla, C</creatorcontrib><creatorcontrib>Paltani, S</creatorcontrib><creatorcontrib>Pasian, F</creatorcontrib><creatorcontrib>Pedersen, K</creatorcontrib><creatorcontrib>Pettorino, V</creatorcontrib><creatorcontrib>Pires, S</creatorcontrib><creatorcontrib>Raison, F</creatorcontrib><creatorcontrib>Rhodes, J</creatorcontrib><creatorcontrib>Rossetti, E</creatorcontrib><creatorcontrib>Saglia, R</creatorcontrib><creatorcontrib>Sartoris, B</creatorcontrib><creatorcontrib>Schneider, P</creatorcontrib><creatorcontrib>Secroun, A</creatorcontrib><creatorcontrib>Seidel, G</creatorcontrib><creatorcontrib>Sirignano, C</creatorcontrib><creatorcontrib>Sirri, G</creatorcontrib><creatorcontrib>Stanco, L</creatorcontrib><creatorcontrib>Starck, J.-L</creatorcontrib><creatorcontrib>Tallada-Crespí, P</creatorcontrib><creatorcontrib>Tavagnacco, D</creatorcontrib><creatorcontrib>Taylor, A.N</creatorcontrib><creatorcontrib>Tereno, I</creatorcontrib><creatorcontrib>Toledo-Moreo, R</creatorcontrib><creatorcontrib>Torradeflot, F</creatorcontrib><creatorcontrib>Valenziano, L</creatorcontrib><creatorcontrib>Wang, Y</creatorcontrib><creatorcontrib>Zamorani, G</creatorcontrib><creatorcontrib>Zoubian, J</creatorcontrib><creatorcontrib>Andreon, S</creatorcontrib><creatorcontrib>Baldi, M</creatorcontrib><creatorcontrib>Camera, S</creatorcontrib><creatorcontrib>Cardone, V.F</creatorcontrib><creatorcontrib>Fabbian, G</creatorcontrib><creatorcontrib>Polenta, G</creatorcontrib><creatorcontrib>Joachimi, B</creatorcontrib><creatorcontrib>Hall, A</creatorcontrib><creatorcontrib>Loureiro, A</creatorcontrib><creatorcontrib>Sellentin, E</creatorcontrib><title>Euclid: Covariance of weak lensing pseudo-Cl estimates: Calculation, comparison to simulations, and dependence on survey geometry</title><title>Astronomy and astrophysics (Berlin)</title><description>An accurate covariance matrix is essential for obtaining reliable cosmological results when using a Gaussian likelihood. In this paper we study the covariance of pseudo- C ℓ estimates of tomographic cosmic shear power spectra. Using two existing publicly available codes in combination, we calculate the full covariance matrix, including mode-coupling contributions arising from both partial sky coverage and non-linear structure growth. For three different sky masks, we compare the theoretical covariance matrix to that estimated from publicly available N -body weak lensing simulations, finding good agreement. We find that as a more extreme sky cut is applied, a corresponding increase in both Gaussian off-diagonal covariance and non-Gaussian super-sample covariance is observed in both theory and simulations, in accordance with expectations. Studying the different contributions to the covariance in detail, we find that the Gaussian covariance dominates along the main diagonal and the closest off-diagonals, but farther away from the main diagonal the super-sample covariance is dominant. Forming mock constraints in parameters that describe matter clustering and dark energy, we find that neglecting non-Gaussian contributions to the covariance can lead to underestimating the true size of confidence regions by up to 70 per cent. The dominant non-Gaussian covariance component is the super-sample covariance, but neglecting the smaller connected non-Gaussian covariance can still lead to the underestimation of uncertainties by 10–20 per cent. A real cosmological analysis will require marginalisation over many nuisance parameters, which will decrease the relative importance of all cosmological contributions to the covariance, so these values should be taken as upper limits on the importance of each component.</description><subject>Astrophysics</subject><subject>Instrumentation and 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F</au><au>Meneghetti, M</au><au>Meylan, G</au><au>Moresco, M</au><au>Munari, E</au><au>Niemi, S.M</au><au>Padilla, C</au><au>Paltani, S</au><au>Pasian, F</au><au>Pedersen, K</au><au>Pettorino, V</au><au>Pires, S</au><au>Raison, F</au><au>Rhodes, J</au><au>Rossetti, E</au><au>Saglia, R</au><au>Sartoris, B</au><au>Schneider, P</au><au>Secroun, A</au><au>Seidel, G</au><au>Sirignano, C</au><au>Sirri, G</au><au>Stanco, L</au><au>Starck, J.-L</au><au>Tallada-Crespí, P</au><au>Tavagnacco, D</au><au>Taylor, A.N</au><au>Tereno, I</au><au>Toledo-Moreo, R</au><au>Torradeflot, F</au><au>Valenziano, L</au><au>Wang, Y</au><au>Zamorani, G</au><au>Zoubian, J</au><au>Andreon, S</au><au>Baldi, M</au><au>Camera, S</au><au>Cardone, V.F</au><au>Fabbian, G</au><au>Polenta, G</au><au>Joachimi, B</au><au>Hall, A</au><au>Loureiro, A</au><au>Sellentin, E</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Euclid: Covariance of weak lensing pseudo-Cl estimates: Calculation, comparison to simulations, and dependence on survey geometry</atitle><jtitle>Astronomy and astrophysics (Berlin)</jtitle><date>2022</date><risdate>2022</risdate><volume>660</volume><issn>0004-6361</issn><eissn>1432-0746</eissn><eissn>1432-0756</eissn><abstract>An accurate covariance matrix is essential for obtaining reliable cosmological results when using a Gaussian likelihood. In this paper we study the covariance of pseudo- C ℓ estimates of tomographic cosmic shear power spectra. Using two existing publicly available codes in combination, we calculate the full covariance matrix, including mode-coupling contributions arising from both partial sky coverage and non-linear structure growth. For three different sky masks, we compare the theoretical covariance matrix to that estimated from publicly available N -body weak lensing simulations, finding good agreement. We find that as a more extreme sky cut is applied, a corresponding increase in both Gaussian off-diagonal covariance and non-Gaussian super-sample covariance is observed in both theory and simulations, in accordance with expectations. Studying the different contributions to the covariance in detail, we find that the Gaussian covariance dominates along the main diagonal and the closest off-diagonals, but farther away from the main diagonal the super-sample covariance is dominant. Forming mock constraints in parameters that describe matter clustering and dark energy, we find that neglecting non-Gaussian contributions to the covariance can lead to underestimating the true size of confidence regions by up to 70 per cent. The dominant non-Gaussian covariance component is the super-sample covariance, but neglecting the smaller connected non-Gaussian covariance can still lead to the underestimation of uncertainties by 10–20 per cent. A real cosmological analysis will require marginalisation over many nuisance parameters, which will decrease the relative importance of all cosmological contributions to the covariance, so these values should be taken as upper limits on the importance of each component.</abstract><pub>EDP Sciences</pub><doi>10.1051/0004-6361/202142908</doi><orcidid>https://orcid.org/0000-0002-0808-6908</orcidid><orcidid>https://orcid.org/0000-0002-0641-3231</orcidid><orcidid>https://orcid.org/0000-0002-2318-301X</orcidid><orcidid>https://orcid.org/0000-0003-1225-7084</orcidid><orcidid>https://orcid.org/0000-0002-7616-7136</orcidid><orcidid>https://orcid.org/0000-0002-8108-9179</orcidid><orcidid>https://orcid.org/0000-0002-4485-8549</orcidid><orcidid>https://orcid.org/0000-0002-5094-2245</orcidid><orcidid>https://orcid.org/0000-0003-3399-3574</orcidid><orcidid>https://orcid.org/0000-0001-9875-8263</orcidid><orcidid>https://orcid.org/0000-0002-9594-9387</orcidid><orcidid>https://orcid.org/0000-0001-7455-8750</orcidid><orcidid>https://orcid.org/0000-0003-2508-0046</orcidid><orcidid>https://orcid.org/0000-0002-8850-0303</orcidid><orcidid>https://orcid.org/0000-0003-1962-9805</orcidid><orcidid>https://orcid.org/0000-0002-4409-5633</orcidid><orcidid>https://orcid.org/0000-0003-0378-7032</orcidid><orcidid>https://orcid.org/0000-0003-4359-8797</orcidid><orcidid>https://orcid.org/0000-0002-9590-7961</orcidid><orcidid>https://orcid.org/0000-0001-9513-7138</orcidid><orcidid>https://orcid.org/0000-0002-5317-7518</orcidid><orcidid>https://orcid.org/0000-0002-1510-5214</orcidid><orcidid>https://orcid.org/0000-0003-3804-2137</orcidid><orcidid>https://orcid.org/0000-0001-7951-0166</orcidid><orcidid>https://orcid.org/0000-0003-4145-1943</orcidid><oa>free_for_read</oa></addata></record>
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subjects Astrophysics
Instrumentation and Detectors
Physics
title Euclid: Covariance of weak lensing pseudo-Cl estimates: Calculation, comparison to simulations, and dependence on survey geometry
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