July
2023
•
2023A&A...675A.120E
Authors
•
Euclid Collaboration
•
Ajani, V.
•
Baldi, M.
•
Barthelemy, A.
•
Boyle, A.
•
Burger, P.
•
Cardone, V. F.
•
Cheng, S.
•
Codis, S.
•
Giocoli, C.
•
Harnois-Déraps, J.
•
Heydenreich, S.
•
Kansal, V.
•
Kilbinger, M.
•
Linke, L.
•
Llinares, C.
•
Martinet, N.
•
Parroni, C.
•
Peel, A.
•
Pires, S.
•
Porth, L.
•
Tereno, I.
•
Uhlemann, C.
•
Vicinanza, M.
•
Vinciguerra, S.
•
Aghanim, N.
•
Auricchio, N.
•
Bonino, D.
•
Branchini, E.
•
Brescia, M.
•
Brinchmann, J.
•
Camera, S.
•
Capobianco, V.
•
Carbone, C.
•
Carretero, J.
•
Castander, F. J.
•
Castellano, M.
•
Cavuoti, S.
•
Cimatti, A.
•
Cledassou, R.
•
Congedo, G.
•
Conselice, C. J.
•
Conversi, L.
•
Corcione, L.
•
Courbin, F.
•
Cropper, M.
•
Da Silva, A.
•
Degaudenzi, H.
•
Di Giorgio, A. M.
•
Dinis, J.
•
Douspis, M.
•
Dubath, F.
•
Dupac, X.
•
Farrens, S.
•
Ferriol, S.
•
Fosalba, P.
•
Frailis, M.
•
Franceschi, E.
•
Galeotta, S.
•
Garilli, B.
•
Gillis, B.
•
Grazian, A.
•
Grupp, F.
•
Hoekstra, H.
•
Holmes, W.
•
Hornstrup, A.
•
Hudelot, P.
•
Jahnke, K.
•
Jhabvala, M.
•
Kümmel, M.
•
Kitching, T.
•
Kunz, M.
•
Kurki-Suonio, H.
•
Lilje, P. B.
•
Lloro, I.
•
Maiorano, E.
•
Mansutti, O.
•
Marggraf, O.
•
Markovic, K.
•
Marulli, F.
•
Massey, R.
•
Mei, S.
•
Mellier, Y.
•
Meneghetti, M.
•
Moresco, M.
•
Moscardini, L.
•
Niemi, S. -M.
•
Nightingale, J.
•
Nutma, T.
•
Padilla, C.
•
Paltani, S.
•
Pedersen, K.
•
Pettorino, V.
•
Polenta, G.
•
Poncet, M.
•
Popa, L. A.
•
Raison, F.
•
Renzi, A.
•
Rhodes, J.
•
Riccio, G.
•
Romelli, E.
•
Roncarelli, M.
•
Rossetti, E.
•
Saglia, R.
•
Sapone, D.
•
Sartoris, B.
•
Schneider, P.
•
Schrabback, T.
•
Secroun, A.
•
Seidel, G.
•
Serrano, S.
•
Sirignano, C.
•
Stanco, L.
•
Starck, J. -L.
•
Tallada-Crespí, P.
•
Taylor, A. N.
•
Toledo-Moreo, R.
•
Torradeflot, F.
•
Tutusaus, I.
•
Valentijn, E. A.
•
Valenziano, L.
•
Vassallo, T.
•
Wang, Y.
•
Weller, J.
•
Zamorani, G.
•
Zoubian, J.
•
Andreon, S.
•
Bardelli, S.
•
Boucaud, A.
•
Bozzo, E.
•
Colodro-Conde, C.
•
Di Ferdinando, D.
•
Fabbian, G.
•
Farina, M.
•
Graciá-Carpio, J.
•
Keihänen, E.
•
Lindholm, V.
•
Maino, D.
•
Mauri, N.
•
Neissner, C.
•
Schirmer, M.
•
Scottez, V.
•
Zucca, E.
•
Akrami, Y.
•
Baccigalupi, C.
•
Balaguera-Antolínez, A.
•
Ballardini, M.
•
Bernardeau, F.
•
Biviano, A.
•
Blanchard, A.
•
Borgani, S.
•
Borlaff, A. S.
•
Burigana, C.
•
Cabanac, R.
•
Cappi, A.
•
Carvalho, C. S.
•
Casas, S.
•
Castignani, G.
•
Castro, T.
•
Chambers, K. C.
•
Cooray, A. R.
•
Coupon, J.
•
Courtois, H. M.
•
Davini, S.
•
de la Torre, S.
•
De Lucia, G.
•
Desprez, G.
•
Dole, H.
•
Escartin, J. A.
•
Escoffier, S.
•
Ferrero, I.
•
Finelli, F.
•
Ganga, K.
•
Garcia-Bellido, J.
•
George, K.
•
Giacomini, F.
•
Gozaliasl, G.
•
Hildebrandt, H.
•
Jimenez Muñoz, A.
•
Joachimi, B.
•
Kajava, J. J. E.
•
Kirkpatrick, C. C.
•
Legrand, L.
•
Loureiro, A.
•
Magliocchetti, M.
•
Maoli, R.
•
Marcin, S.
•
Martinelli, M.
•
Martins, C. J. A. P.
•
Matthew, S.
•
Maurin, L.
•
Metcalf, R. B.
•
Monaco, P.
•
Morgante, G.
•
Nadathur, S.
•
Nucita, A. A.
•
Popa, V.
•
Potter, D.
•
Pourtsidou, A.
•
Pöntinen, M.
•
Reimberg, P.
•
Sánchez, A. G.
•
Sakr, Z.
•
Schneider, A.
•
Sefusatti, E.
•
Sereno, M.
•
Shulevski, A.
•
Spurio Mancini, A.
•
Steinwagner, J.
•
Teyssier, R.
•
Valiviita, J.
•
Veropalumbo, A.
•
Viel, M.
•
Zinchenko, I. A.
Abstract
•
Recent cosmic shear studies have shown that higher-order statistics (HOS) developed by independent teams now outperform standard two-point estimators in terms of statistical precision thanks to their sensitivity to the non-Gaussian features of large-scale structure. The aim of the Higher-Order Weak Lensing Statistics (HOWLS) project is to assess, compare, and combine the constraining power of ten different HOS on a common set of Euclid-like mocks, derived from N-body simulations. In this first paper of the HOWLS series, we computed the nontomographic (Ωm, σ8) Fisher information for the one-point probability distribution function, peak counts, Minkowski functionals, Betti numbers, persistent homology Betti numbers and heatmap, and scattering transform coefficients, and we compare them to the shear and convergence two-point correlation functions in the absence of any systematic bias. We also include forecasts for three implementations of higher-order moments, but these cannot be robustly interpreted as the Gaussian likelihood assumption breaks down for these statistics. Taken individually, we find that each HOS outperforms the two-point statistics by a factor of around two in the precision of the forecasts with some variations across statistics and cosmological parameters. When combining all the HOS, this increases to a 4.5 times improvement, highlighting the immense potential of HOS for cosmic shear cosmological analyses with Euclid. The data used in this analysis are publicly released with the paper.
Links
