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欧几里得准备。红移空间中银河群二阶关联函数建模

Euclid preparation. Galaxy 2-point correlation function modelling in redshift space

Euclid Collaboration, M. Kärcher, M. -A. Breton, S. de la Torre, A. Veropalumbo, A. Eggemeier, M. Crocce, E. Sefusatti, E. Sarpa, R. E. Angulo, B. Camacho Queve… 展开作者

Euclid Collaboration, M. Kärcher, M. -A. Breton, S. de la Torre, A. Veropalumbo, A. Eggemeier, M. Crocce, E. Sefusatti, E. Sarpa, R. E. Angulo, B. Camacho Quevedo, L. Castiblanco, E. Castorina, A. Chudaykin, V. Desjacques, A. Farina, G. Gambardella, M. Guidi, D. Linde, F. Marulli, A. Moradinezhad Dizgah, M. Moresco, C. Moretti, K. Pardede, A. Pezzotta, M. Pellejero Ibañez, C. Porciani, A. Pugno, M. Zennaro, N. Aghanim, B. Altieri, L. Amendola, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, S. Bardelli, A. Biviano, E. Branchini, M. Brescia, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, H. Degaudenzi, G. De Lucia, H. Dole, F. Dubath, X. Dupac, S. Dusini, A. Ealet, S. Escoffier, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, P. Fosalba, N. Fourmanoit, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, J. Gracia-Carpio, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, R. J. Massey, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, L. Moscardini, C. Neissner, S. -M. Niemi, C. Padilla, F. Pasian, J. A. Peacock, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, F. Raison, G. Riccio, E. Romelli, M. Roncarelli, C. Rosset, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, S. Serrano, P. Simon, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, J. Valiviita, T. Vassallo, Y. Wang, J. Weller, G. Zamorani, F. M. Zerbi, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, A. Boucaud, E. Bozzo, C. Burigana, R. Cabanac, M. Calabrese, A. Cappi, T. Castro, J. A. Escartin Vigo, L. Gabarra, J. García-Bellido, V. Gautard, J. Macias-Perez, R. Maoli, J. Martín-Fleitas, M. Maturi, N. Mauri, R. B. Metcalf, P. Monaco, M. Pöntinen, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Tucci, M. Viel, M. Wiesmann, Y. Akrami, I. T. Andika, G. Angora, M. Archidiacono, F. Atrio-Barandela, E. Aubourg, L. Bazzanini, J. Bel, D. Bertacca, M. Bethermin, F. Beutler, L. Blot, M. Bonici, S. Borgani, M. L. Brown, S. Bruton, A. Calabro, F. Caro, C. S. Carvalho, F. Cogato, S. Conseil, A. R. Cooray, S. Davini, G. Desprez, A. Díaz-Sánchez, S. Di Domizio, J. M. Diego, V. Duret, M. Y. Elkhashab, A. Enia, Y. Fang, A. G. Ferrari, P. G. Ferreira, A. Finoguenov, A. Fontana, F. Fontanot, A. Franco, K. Ganga, T. Gasparetto, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, A. Gruppuso, C. M. Gutierrez, A. Hall, H. Hildebrandt, J. Hjorth, S. Joudaki, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, J. Kim, C. C. Kirkpatrick, S. Kruk, M. Lattanzi, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, T. I. Liaudat, M. Magliocchetti, A. Manjón-García, F. Mannucci, C. J. A. P. Martins, L. Maurin, M. Migliaccio, M. Miluzio, A. Montoro, G. Morgante, S. Nadathur, K. Naidoo, P. Natoli, A. Navarro-Alsina, S. Nesseris, L. Pagano, D. Paoletti, F. Passalacqua, K. Paterson, L. Patrizii, R. Paviot, A. Pisani, D. Potter, G. W. Pratt, S. Quai, M. Radovich, K. Rojas, W. Roster, S. Sacquegna, M. Sahlén, D. B. Sanders, A. Schneider, D. Sciotti, E. Sellentin, L. C. Smith, J. G. Sorce, K. Tanidis, C. Tao, F. Tarsitano, G. Testera, R. Teyssier, S. Tosi, A. Troja, A. Venhola, D. Vergani, F. Vernizzi, G. Verza, S. Vinciguerra, N. A. Walton, A. H. Wright

AI总结:

本文比较了不同模型在红移空间中银河群二阶关联函数的建模效果,发现VDG_∞和CLEFT模型在精度和无偏性方面表现优异,适合欧几里得任务的需求。

摘要:

欧几里得卫星将对数以万计的发射线星系进行光谱红移测量。在第四阶段调查的背景下,星系三维聚类在提供宇宙学约束中起着关键作用。在本文中,我们对红移空间中银河群二阶关联函数(2PCF)的多极矩进行了模型比较。我们测试了最先进的模型,特别是大尺度结构的有效场论(EFT),一种基于速度差生成函数(VDG_∞)的模型,以及不同变种的拉格朗日扰动理论(LPT)模型,如卷积LPT(CLPT)及其有效场论扩展(CLEFT)。我们分析了Flagship 1模拟中的前三个偶数多极矩,该模拟包含四个快照,红移z∈{0.9,1.2,1.5,1.8}。我们研究了模板拟合和全形状方法,并发现使用模板拟合方法时,只有VDG_∞模型能够在不偏置恢复参数的情况下达到最小拟合尺度s_min=20h⁻¹Mpc在z=0.9时。事实上,EFT模型在s_min=30h⁻¹Mpc时已经不准确。相反,在全形状分析中,CLEFT和VDG_∞模型表现相似,但只有CLEFT模型可以达到s_min=20h⁻¹Mpc,而VDG_∞模型在最低红移时可以无偏到s_min=25h⁻¹Mpc。总体而言,为了达到欧几里得所需的精度,非扰动建模,如VDG_∞或CLEFT模型中的建模应被考虑。在z=1.8时,CLPT模型足以高精度描述数据。这种比较选择了在理想条件下表现最佳的基础模型,并为欧几里得数据在配置空间中的最优分析奠定了基础。

英文摘要:

The Euclid satellite will measure spectroscopic redshifts for tens of millions of emission-line galaxies. In the context of Stage-IV surveys, the 3-dimensional clustering of galaxies plays a key role in providing cosmological constraints. In this paper, we conduct a model comparison for the multipole moments of the galaxy 2-point correlation function (2PCF) in redshift space. We test state-of-the-art models, in particular the effective field theory of large-scale structure (EFT), one based on the velocity difference generating function (VDG$_{\infty}$), and different variants of Lagrangian perturbation theory (LPT) models, such as convolutional LPT (CLPT) and its effective-field-theory extension (CLEFT). We analyse the first three even multipoles of the 2PCF in the Flagship 1 simulation, which consists of four snapshots at $z\in\{0.9,1.2,1.5,1.8\}$. We study both template-fitting and full-shape approaches and find that with the template-fitting approach, only the VDG$_{\infty}$ model is able to reach a minimum fitting scale of $s_{\rm min}=20\,h^{-1}\,{\rm Mpc}$ at $z=0.9$ without biasing the recovered parameters. Indeed, the EFT model becomes inaccurate already at $s_{\rm min}=30\,h^{-1}\,{\rm Mpc}$. Conversely, in the full-shape analysis, the CLEFT and VDG$_{\infty}$ models perform similarly well, but only the CLEFT model can reach $s_{\rm min}=20\,h^{-1}\,{\rm Mpc}$ while the VDG$_{\infty}$ model is unbiased down to $s_{\rm min}=25\,h^{-1}\,{\rm Mpc}$ at the lowest redshift. Overall, in order to achieve the accuracy required by Euclid, non-perturbative modelling such as in the VDG$_{\infty}$ or CLEFT models should be considered. At $z=1.8$, the CLPT model is sufficient to describe the data with high figure of merit. This comparison selects baseline models that perform best in ideal conditions and sets the stage for an optimal analysis of Euclid data in configuration space.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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