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

V Ajani; M Baldi; A Barthelemy; A Boyle; P Burger; VF Cardone; S Cheng; S Codis; C Giocoli; J Harnois-Deraps; 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; FJ Castander; M Castellano; S Cavuoti; A Cimatti; R Cledassou; G Congedo; CJ Conselice; L Conversi; L Corcione; F Courbin; M Cropper; A Da Silva; H Degaudenzi; AM 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 Kummel; T Kitching; M Kunz; H Kurki-Suonio; PB 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; SM Niemi; J Nightingale; T Nutma; C Padilla; S Paltani; K Pedersen; V Pettorino; G Polenta; M Poncet; LA 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; JL Starck; P Tallada-Crespi; AN Taylor; R Toledo-Moreo; F Torradeflot; I Tutusaus; EA 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 Gracia-Carpio; E Keihanen; V Lindholm; D Maino; N Mauri; C Neissner; M Schirmer; V Scottez; E Zucca; Y Akrami; C Baccigalupi; A Balaguera-Antolinez; M Ballardini; F Bernardeau; A Biviano; A Blanchard; S Borgani; AS Borlaff; C Burigana; R Cabanac; A Cappi; CS Carvalho; S Casas; G Castignani; T Castro; KC Chambers; AR Cooray; J Coupon; HM Courtois; S Davini; S de la Torre; G De Lucia; G Desprez; H Dole; JA Escartin; S Escoffier; I Ferrero; F Finelli; K Ganga; J Garcia-Bellido; K George; F Giacomini; G Gozaliasl; H Hildebrandt; AJ Munoz; B Joachimi; JJE Kajava; CC Kirkpatrick; L Legrand; A Loureiro; M Magliocchetti; R Maoli; S Marcin; M Martinelli; CJAP Martins; S Matthew; L Maurin; RB Metcalf; P Monaco; G Morgante; S Nadathur; AA Nucita; V Popa; D Potter; A Pourtsidou; M Pontinen; P Reimberg; AG Sanchez; Z Sakr; A Schneider; E Sefusatti; M Sereno; A Shulevski; AS Mancini; J Steinwagner; R Teyssier; J Valiviita; A Veropalumbo; M Viel; IA Zinchenko

doi:10.1051/0004-6361/202346017

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

V Ajani, M Baldi, A Barthelemy, A Boyle, P Burger, VF Cardone, S Cheng, S Codis, C Giocoli, J Harnois-Deraps, S Heydenreich, V Kansal, M Kilbinger, L Linke, C Llinares, N Martinet, C Parroni, A Peel, S Pires, L PorthI 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, FJ Castander, M Castellano, S Cavuoti, A Cimatti, R Cledassou, G Congedo, CJ Conselice, L Conversi, L Corcione, F Courbin, M Cropper, A Da Silva, H Degaudenzi, AM 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 Kummel, T Kitching, M Kunz, H Kurki-Suonio, PB 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, SM Niemi, J Nightingale, T Nutma, C Padilla, S Paltani, K Pedersen, V Pettorino, G Polenta, M Poncet, LA 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, JL Starck, P Tallada-Crespi, AN Taylor, R Toledo-Moreo, F Torradeflot, I Tutusaus, EA 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 Gracia-Carpio, E Keihanen, V Lindholm, D Maino, N Mauri, C Neissner, M Schirmer, V Scottez, E Zucca, Y Akrami, C Baccigalupi, A Balaguera-Antolinez, M Ballardini, F Bernardeau, A Biviano, A Blanchard, S Borgani, AS Borlaff, C Burigana, R Cabanac, A Cappi, CS Carvalho, S Casas, G Castignani, T Castro, KC Chambers, AR Cooray, J Coupon, HM Courtois, S Davini, S de la Torre, G De Lucia, G Desprez, H Dole, JA Escartin, S Escoffier, I Ferrero, F Finelli, K Ganga, J Garcia-Bellido, K George, F Giacomini, G Gozaliasl, H Hildebrandt, AJ Munoz, B Joachimi, JJE Kajava, CC Kirkpatrick, L Legrand, A Loureiro, M Magliocchetti, R Maoli, S Marcin, M Martinelli, CJAP Martins, S Matthew, L Maurin, RB Metcalf, P Monaco, G Morgante, S Nadathur, AA Nucita, V Popa, D Potter, A Pourtsidou, M Pontinen, P Reimberg, AG Sanchez, Z Sakr, A Schneider, E Sefusatti, M Sereno, A Shulevski, AS Mancini, J Steinwagner, R Teyssier, J Valiviita, A Veropalumbo, M Viel, IA Zinchenko

Not published at Aalto University

Research output: Contribution to journal › Article › Scientific › peer-review

13 Citations (Scopus)

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, 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.

Original language	English
Article number	A120
Number of pages	32
Journal	Astronomy & Astrophysics
Volume	675
DOIs	https://doi.org/10.1051/0004-6361/202346017
Publication status	Published - 7 Jul 2023
MoE publication type	A1 Journal article-refereed

Keywords

Cosmological parameters
Gravitational lensing: weak
Large-scale structure of Universe
Methods: statistical
Surveys

Access to Document

10.1051/0004-6361/202346017

Cite this

Ajani, V., Baldi, M., Barthelemy, A., Boyle, A., Burger, P., Cardone, VF., 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., ... Zinchenko, IA. (2023). Euclid preparation : XXVIII. Forecasts for ten different higher-order weak lensing statistics. Astronomy & Astrophysics, 675, Article A120. https://doi.org/10.1051/0004-6361/202346017

@article{cddd38e6552541a38c174e9ae6941a1a,

title = "Euclid preparation : XXVIII. Forecasts for ten different higher-order weak lensing statistics",

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, 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.",

keywords = "Cosmological parameters, Gravitational lensing: weak, Large-scale structure of Universe, Methods: statistical, Surveys",

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

year = "2023",

month = jul,

day = "7",

doi = "10.1051/0004-6361/202346017",

language = "English",

volume = "675",

journal = "Astronomy & Astrophysics",

issn = "0004-6361",

publisher = "EDP Sciences",

}

Ajani, V, Baldi, M, Barthelemy, A, Boyle, A, Burger, P, Cardone, VF, 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, FJ, Castellano, M, Cavuoti, S, Cimatti, A, Cledassou, R, Congedo, G, Conselice, CJ, Conversi, L, Corcione, L, Courbin, F, Cropper, M, Da Silva, A, Degaudenzi, H, Di Giorgio, AM, 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, PB, 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, SM, Nightingale, J, Nutma, T, Padilla, C, Paltani, S, Pedersen, K, Pettorino, V, Polenta, G, Poncet, M, Popa, LA, 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, JL, Tallada-Crespi, P, Taylor, AN, Toledo-Moreo, R, Torradeflot, F, Tutusaus, I, Valentijn, EA, 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, AS, Burigana, C, Cabanac, R, Cappi, A, Carvalho, CS, Casas, S, Castignani, G, Castro, T, Chambers, KC, Cooray, AR, Coupon, J, Courtois, HM, Davini, S, de la Torre, S, De Lucia, G, Desprez, G, Dole, H, Escartin, JA, Escoffier, S, Ferrero, I, Finelli, F, Ganga, K, Garcia-Bellido, J, George, K, Giacomini, F, Gozaliasl, G, Hildebrandt, H, Munoz, AJ, Joachimi, B, Kajava, JJE, Kirkpatrick, CC, Legrand, L, Loureiro, A, Magliocchetti, M, Maoli, R, Marcin, S, Martinelli, M, Martins, CJAP, Matthew, S, Maurin, L, Metcalf, RB, Monaco, P, Morgante, G, Nadathur, S, Nucita, AA, Popa, V, Potter, D, Pourtsidou, A, Pontinen, M, Reimberg, P, Sanchez, AG, Sakr, Z, Schneider, A, Sefusatti, E, Sereno, M, Shulevski, A, Mancini, AS, Steinwagner, J, Teyssier, R, Valiviita, J, Veropalumbo, A, Viel, M & Zinchenko, IA 2023, 'Euclid preparation : XXVIII. Forecasts for ten different higher-order weak lensing statistics', Astronomy & Astrophysics, vol. 675, A120. https://doi.org/10.1051/0004-6361/202346017

TY - JOUR

T1 - Euclid preparation : XXVIII. Forecasts for ten different higher-order weak lensing statistics

AU - Ajani, V

AU - Baldi, M

AU - Barthelemy, A

AU - Boyle, A

AU - Burger, P

AU - Cardone, VF

AU - Cheng, S

AU - Codis, S

AU - Giocoli, C

AU - Harnois-Deraps, J

AU - Heydenreich, S

AU - Kansal, V

AU - Kilbinger, M

AU - Linke, L

AU - Llinares, C

AU - Martinet, N

AU - Parroni, C

AU - Peel, A

AU - Pires, S

AU - Porth, L

AU - Tereno, I

AU - Uhlemann, C

AU - Vicinanza, M

AU - Vinciguerra, S

AU - Aghanim, N

AU - Auricchio, N

AU - Bonino, D

AU - Branchini, E

AU - Brescia, M

AU - Brinchmann, J

AU - Camera, S

AU - Capobianco, V

AU - Carbone, C

AU - Carretero, J

AU - Castander, FJ

AU - Castellano, M

AU - Cavuoti, S

AU - Cimatti, A

AU - Cledassou, R

AU - Congedo, G

AU - Conselice, CJ

AU - Conversi, L

AU - Corcione, L

AU - Courbin, F

AU - Cropper, M

AU - Da Silva, A

AU - Degaudenzi, H

AU - Di Giorgio, AM

AU - Dinis, J

AU - Douspis, M

AU - Dubath, F

AU - Dupac, X

AU - Farrens, S

AU - Ferriol, S

AU - Fosalba, P

AU - Frailis, M

AU - Franceschi, E

AU - Galeotta, S

AU - Garilli, B

AU - Gillis, B

AU - Grazian, A

AU - Grupp, F

AU - Hoekstra, H

AU - Holmes, W

AU - Hornstrup, A

AU - Hudelot, P

AU - Jahnke, K

AU - Jhabvala, M

AU - Kummel, M

AU - Kitching, T

AU - Kunz, M

AU - Kurki-Suonio, H

AU - Lilje, PB

AU - Lloro, I

AU - Maiorano, E

AU - Mansutti, O

AU - Marggraf, O

AU - Markovic, K

AU - Marulli, F

AU - Massey, R

AU - Mei, S

AU - Mellier, Y

AU - Meneghetti, M

AU - Moresco, M

AU - Moscardini, L

AU - Niemi, SM

AU - Nightingale, J

AU - Nutma, T

AU - Padilla, C

AU - Paltani, S

AU - Pedersen, K

AU - Pettorino, V

AU - Polenta, G

AU - Poncet, M

AU - Popa, LA

AU - Raison, F

AU - Renzi, A

AU - Rhodes, J

AU - Riccio, G

AU - Romelli, E

AU - Roncarelli, M

AU - Rossetti, E

AU - Saglia, R

AU - Sapone, D

AU - Sartoris, B

AU - Schneider, P

AU - Schrabback, T

AU - Secroun, A

AU - Seidel, G

AU - Serrano, S

AU - Sirignano, C

AU - Stanco, L

AU - Starck, JL

AU - Tallada-Crespi, P

AU - Taylor, AN

AU - Toledo-Moreo, R

AU - Torradeflot, F

AU - Tutusaus, I

AU - Valentijn, EA

AU - Valenziano, L

AU - Vassallo, T

AU - Wang, Y

AU - Weller, J

AU - Zamorani, G

AU - Zoubian, J

AU - Andreon, S

AU - Bardelli, S

AU - Boucaud, A

AU - Bozzo, E

AU - Colodro-Conde, C

AU - Di Ferdinando, D

AU - Fabbian, G

AU - Farina, M

AU - Gracia-Carpio, J

AU - Keihanen, E

AU - Lindholm, V

AU - Maino, D

AU - Mauri, N

AU - Neissner, C

AU - Schirmer, M

AU - Scottez, V

AU - Zucca, E

AU - Akrami, Y

AU - Baccigalupi, C

AU - Balaguera-Antolinez, A

AU - Ballardini, M

AU - Bernardeau, F

AU - Biviano, A

AU - Blanchard, A

AU - Borgani, S

AU - Borlaff, AS

AU - Burigana, C

AU - Cabanac, R

AU - Cappi, A

AU - Carvalho, CS

AU - Casas, S

AU - Castignani, G

AU - Castro, T

AU - Chambers, KC

AU - Cooray, AR

AU - Coupon, J

AU - Courtois, HM

AU - Davini, S

AU - de la Torre, S

AU - De Lucia, G

AU - Desprez, G

AU - Dole, H

AU - Escartin, JA

AU - Escoffier, S

AU - Ferrero, I

AU - Finelli, F

AU - Ganga, K

AU - Garcia-Bellido, J

AU - George, K

AU - Giacomini, F

AU - Gozaliasl, G

AU - Hildebrandt, H

AU - Munoz, AJ

AU - Joachimi, B

AU - Kajava, JJE

AU - Kirkpatrick, CC

AU - Legrand, L

AU - Loureiro, A

AU - Magliocchetti, M

AU - Maoli, R

AU - Marcin, S

AU - Martinelli, M

AU - Martins, CJAP

AU - Matthew, S

AU - Maurin, L

AU - Metcalf, RB

AU - Monaco, P

AU - Morgante, G

AU - Nadathur, S

AU - Nucita, AA

AU - Popa, V

AU - Potter, D

AU - Pourtsidou, A

AU - Pontinen, M

AU - Reimberg, P

AU - Sanchez, AG

AU - Sakr, Z

AU - Schneider, A

AU - Sefusatti, E

AU - Sereno, M

AU - Shulevski, A

AU - Mancini, AS

AU - Steinwagner, J

AU - Teyssier, R

AU - Valiviita, J

AU - Veropalumbo, A

AU - Viel, M

AU - Zinchenko, IA

PY - 2023/7/7

Y1 - 2023/7/7

N2 - 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.

AB - 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.

KW - Cosmological parameters

KW - Gravitational lensing: weak

KW - Large-scale structure of Universe

KW - Methods: statistical

KW - Surveys

UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=aalto_pure&SrcAuth=WosAPI&KeyUT=WOS:001030296600010&DestLinkType=FullRecord&DestApp=WOS

UR - http://www.scopus.com/inward/record.url?scp=85165537995&partnerID=8YFLogxK

U2 - 10.1051/0004-6361/202346017

DO - 10.1051/0004-6361/202346017

M3 - Article

SN - 0004-6361

VL - 675

JO - Astronomy & Astrophysics

JF - Astronomy & Astrophysics

M1 - A120

ER -

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

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