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欧几里得:通过光度聚类和剪切与宇宙红外背景的互相关来推断恒星形成历史

Euclid: Inferring star-formation history via cross-correlations of photometric clustering and shear with the cosmic infrared background

J. Han, S. Camera, M. Migliaccio, G. Fabbian, F. Pace, B. Jego, B. Altieri, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, S. Bardelli, P. Battaglia, A. Biviano, E. Branchini, M. Brescia, 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, M. Cropper, H. Degaudenzi, S. de la Torre, G. De Lucia, H. Dole, F. Dubath, X. Dupac, S. Dusini, S. Escoffier, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, P. Fosalba, S. Fotopoulou, N. Fourmanoit, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, B. Gillis, C. Giocoli, J. Gracia-Carpio, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, B. Joachimi, S. Kermiche, A. Kiessling, B. Kubik, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, M. Magliocchetti, G. Mainetti, O. Mansutti, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. J. Massey, E. Medinaceli, S. Mei, M. Meneghetti, E. Merlin, G. Meylan, P. Monaco, A. Mora, M. Moresco, C. Moretti, L. Moscardini, C. Neissner, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, A. Pezzotta, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, P. Schneider, T. Schrabback, M. Scodeggio, A. Secroun, E. Sihvola, P. Simon, C. Sirignano, G. Sirri, L. Stanco, P. Tallada-Crespí, A. N. Taylor, H. I. Teplitz, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, J. Valiviita, T. Vassallo, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, F. M. Zerbi, E. Zucca, S. Borgani, A. Loureiro, M. Sereno, M. Viel

arXiv 2607.19290首次发表:更新:

AI 中文总结

研究通过基于欧几里得任务数据进行预测性层析互相关分析,利用特定模板测量偏差加权恒星形成率密度,相比之前工作,能收紧对其约束,在更深红移范围提供更多测量,改善对HOD参数约束并打破参数简并。

AI 中文摘要

宇宙红外背景由恒星形成活动加热的尘埃产生,编码了宇宙时间长河中恒星形成分布的综合历史。分解不同红移星系对观测到的宇宙红外背景(CIB)图的贡献,是探测宇宙时间内大规模恒星形成历史的有力工具。为此,我们基于欧几里得任务调查规格,利用模拟的光度星系聚类和弱引力透镜数据,结合普朗克CIB观测,进行预测性层析互相关分析。该分析采用由晕占据分布模型构建的模板,测量作为红移函数的偏差加权恒星形成率密度〈bρSFRD〉。我们基于这些模拟给出〈bρSFRD〉约束的预测,并使用费舍尔信息矩阵方法量化预期的约束能力。与先前工作使用相同数据集的结果相比,我们发现欧几里得将把所有红移区间内〈bρSFRD〉的约束收紧约2倍,并在更深的红移范围内提供更多测量。这导致对所有晕占据分布(HOD)参数的约束得到改善,通常将边缘化不确定性降低约2.5倍,同时还打破了参数简并。

英文摘要

The cosmic infrared background, sourced by dust heated by star-forming activity, encodes the integrated history of the star-formation distribution over a large swathe of cosmic time. Decomposing the contribution from galaxies at different redshifts to the observed CIB maps provides a powerful tool for probing the large-scale star-formation history across cosmic times. To this end, we perform a forecast tomographic cross-correlation analysis using simulated photometric galaxy clustering and weak lensing data based on the Euclid mission survey specifications, combined with Planck CIB observations. The analysis adopts templates constructed from the halo occupation distribution model to measure the bias-weighted star-formation-rate density, $\langle b ρ_{\rm SFRD}\rangle$, as a function of redshift. We present forecasts for the $\langle b ρ_{\rm SFRD}\rangle$ constraints based on these simulations and quantify the expected constraining power using Fisher information matrix methods. Compared with results obtained using the same dataset as in previous works, we find that Euclid will tighten the constraints on $\langle b ρ_{\rm SFRD}\rangle$ across all redshift bins by a factor of about $2$ and provide more measurements over a deeper redshift range. This leads to improved constraints on all HOD parameters, typically reducing the marginalized uncertainties by a factor of about $2.5$, while also breaking parameter degeneracies.

Comments15 pages, 7 figures, on behalf of the Euclid Consortium

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