Euclid preparation: XXVIII. Forecasts for ten different higher-order weak lensing statistics

Ajani, V.; Baldi, M.; Barthelemy, A.; Boyle, A.; Burger, P.; Cardone, V. F.; Cheng, S.; Codis, S.; Giocoli, C.; Harnois-Deraps, 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.; Kummel, 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-Crespi, 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.; Gracia-Carpio, J.; Keihanen, E.; Lindholm, V.; Maino, D.; Mauri, N.; Neissner, C.; Schirmer, M.; Scottez, V.; Zucca, E.; Akrami, Y.; Baccigalupi, C.; Balaguera-Antolinez, 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 Munoz, 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.; Pontinen, M.; Reimberg, P.; Sanchez, 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.

doi:10.1051/0004-6361/202346017

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, σ 8) 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.