Euclid Preparation XXIX: Forecasts for 10 different higher-order weak lensing statistics

Euclid Collaboration,V. Ajani,M. Baldi,A. Barthelemy,A. Boyle,P. Burger, V. F. Cardone,S. Cheng, S. Codis,C. Giocoli,J. Harnois-Déraps, S. Heydenreich,V. Kansal,M. Kilbinger,L. Linke, C. Llinares,N. Martinet, C. Parroni,A. Peel,S. Pires, L. Porth, I. Tereno,C. Uhlemann, M. Vicinanza, S. Vinciguerra,N. Aghanim,N. Auricchio, D. Bonino,E. Branchini,M. Brescia,J. Brinchmann,S. Camera, V. Capobianco,C. Carbone,J. Carretero, F. J. Castander,M. Castellano,S. Cavuoti, A. Cimatti, R. Cledassou,G. Congedo,C. J. Conselice, L. Conversi,L. Corcione,F. Courbin, M. Cropper,A. Da Silva,H. Degaudenzi,A. M. Di Giorgio, J. Dinis, M. Douspis, F. Dubath,X. Dupac,S. Farrens, S. Ferriol,P. Fosalba, M. Frailis, E. Franceschi,S. Galeotta,B. Garilli,B. Gillis, A. Grazian, F. Grupp,H. Hoekstra, W. Holmes,A. Hornstrup, P. Hudelot,K. Jahnke, M. Jhabvala,M. Kümmel,T. Kitching,M. Kunz,H. Kurki-Suonio, P. B. Lilje,I. Lloro,E. Maiorano,O. Mansutti,O. Marggraf,K. Markovic,F. Marulli,R. Massey, S. Mei, Y. Mellier,M. Meneghetti,M. Moresco,L. Moscardini, S. -M. Niemi,J. Nightingale, T. Nutma, C. Padilla,S. Paltani, K. Pedersen,V. Pettorino, G. Polenta, M. Poncet, L. A. Popa, F. Raison,A. Renzi, J. Rhodes, G. Riccio, E. Romelli,M. Roncarelli, E. Rossetti, R. Saglia,D. Sapone, B. Sartoris, P. Schneider,T. Schrabback, A. Secroun,G. Seidel, S. Serrano,C. Sirignano, L. Stanco,J. -L. Starck, P. Tallada-Crespí,A. N. Taylor, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus,E. A. Valentijn, L. Valenziano,T. Vassallo, Y. Wang,J. Weller, G. Zamorani, J. Zoubian,S. Andreon,S. Bardelli, A. Boucaud,E. Bozzo, C. Colodro-Conde,D. Di Ferdinando,G. Fabbian, M. Farina, J. Graciá-Carpio, E. Keihänen, V. Lindholm, D. Maino,N. Mauri, C. Neissner, M. Schirmer,V. Scottez, E. Zucca,Y. Akrami,C. Baccigalupi, A. Balaguera-Antolínez, M. Ballardini,F. Bernardeau, A. Biviano,A. Blanchard,S. Borgani,A. S. Borlaff, C. Burigana,R. Cabanac,A. Cappi, C. S. Carvalho,S. Casas,G. Castignani,T. Castro,K. C. Chambers,A. R. Cooray,J. Coupon,H. M. Courtois,S. Davini,S. de la Torre, G. De Lucia,G. Desprez,H. Dole,J. A. Escartin,S. Escoffier,I. Ferrero,F. Finelli, K. Ganga, J. Garcia-Bellido,K. George, F. Giacomini,G. Gozaliasl,H. Hildebrandt, A. JIMENEZ MU\{N}OZ,B. Joachimi,J. J. E. Kajava, C. C. Kirkpatrick,L. Legrand,A. Loureiro, M. Magliocchetti,R. Maoli,S. Marcin, M. Martinelli, C. J. A. P. Martins, S. Matthew,L. Maurin,R. B. Metcalf,P. Monaco, G. Morgante,S. Nadathur,A. A. Nucita, V. Popa,D. Potter,A. Pourtsidou,M. Pöntinen,P. Reimberg,A. G. Sánchez, Z. Sakr,A. Schneider,E. Sefusatti, M. Sereno,A. Shulevski,A. Spurio Mancini, J. Steinwagner,R. Teyssier,J. Valiviita,A. Veropalumbo,M. Viel,I. A. Zinchenko

arxiv(2023)

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摘要
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 $10$ different HOS on a common set of $Euclid$-like mocks, derived from N-body simulations. In this first paper of the HOWLS series we compute the non-tomographic ($\Omega_{\rm m}$, $\sigma_8$) Fisher information for one-point probability distribution function, peak counts, Minkowski functionals, Betti numbers, persistent homology Betti numbers and heatmap, and scattering transform coefficients, and 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 $2$ 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.
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euclid preparation xxix,forecasts,higher-order
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