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arXiv 2602.11279astro-ph.COastro-ph.GA
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SPT-3G D1: 利用SPT-3G和普朗克调查数据制作Compton-y地图

SPT-3G D1: Compton-$y$ maps using data from the SPT-3G and Planck surveys

Stanford University(斯坦福大学);SLAC National Accelerator Laboratory(SLAC国家加速器实验室);California Institute of Technology(加州理工学院);National Center for Supercomputing Applications(国家超级计算应用中心);Fermi National Accelerator Laboratory(费米国家加速器实验室);University of Chicago(芝加哥大学)
A. S. Maniyar, F. Bianchini, W. L. K. Wu, S. Raghunathan, A. J. Anderson, B. Ansarinejad, M. Archipley, L. Balkenhol, D. R. Barron, P. S. Barry, K. Benabed, A. … 展开作者

A. S. Maniyar, F. Bianchini, W. L. K. Wu, S. Raghunathan, A. J. Anderson, B. Ansarinejad, M. Archipley, L. Balkenhol, D. R. Barron, P. S. Barry, K. Benabed, A. N. Bender, B. A. Benson, L. E. Bleem, S. Bocquet, F. R. Bouchet, L. Bryant, E. Camphuis, M. G. Campitiello, J. E. Carlstrom, J. Carron, C. L. Chang, P. Chaubal, P. M. Chichura, A. Chokshi, T. -L. Chou, A. Coerver, T. M. Crawford, C. Daley, T. de Haan, K. R. Dibert, M. A. Dobbs, M. Doohan, A. Doussot, D. Dutcher, W. Everett, C. Feng, K. R. Ferguson, N. C. Ferree, K. Fichman, A. Foster, S. Galli, A. E. Gambrel, A. K. Gao, R. W. Gardner, F. Ge, N. Goeckner-Wald, R. Gualtieri, F. Guidi, S. Guns, N. W. Halverson, E. Hivon, A. Y. Q. Ho, G. P. Holder, W. L. Holzapfel, J. C. Hood, A. Hryciuk, N. Huang, T. Jhaveri, F. Kéruzoré, A. R. Khalife, L. Knox, M. Korman, K. Kornoelje, C. -L. Kuo, K. Levy, Y. Li, A. E. Lowitz, C. Lu, G. P. Lynch, T. J. Maccarone, E. S. Martsen, F. Menanteau, M. Millea, J. Montgomery, Y. Nakato, T. Natoli, G. I. Noble, Y. Omori, A. Ouellette, Z. Pan, P. Paschos, K. A. Phadke, A. W. Pollak, K. Prabhu, W. Quan, M. Rahimi, A. Rahlin, C. L. Reichardt, M. Rouble, J. E. Ruhl, E. Schiappucci, A. C. Silva Oliveira, A. Simpson, J. A. Sobrin, A. A. Stark, J. Stephen, C. Tandoi, B. Thorne, C. Trendafilova, C. Umilta, J. D. Vieira, A. G. Vieregg, A. Vitrier, Y. Wan, N. Whitehorn, M. R. Young, J. A. Zebrowski

AI总结:

SPT-3G与普朗克数据结合,利用线性组合方法生成高分辨率Compton-y地图,验证其在大尺度结构研究中的应用价值。

摘要:

我们利用南天极望远镜(SPT)第三代相机SPT-3G两年(2019-2020)的观测数据,结合普朗克卫星数据,制作了热Sunyaev-Zel'dovich(tSZ)Compton-y参数地图。通过线性组合(LC)流水线,我们获得了一系列重建结果,探索了统计灵敏度与抑制天体物理污染之间的不同权衡,包括最小方差、宇宙微波背景去投影和尘埃红外背景(CIB)去投影的y-地图。我们通过不同的统计技术验证这些地图,如自功率谱和交叉功率谱以及大尺度结构踪迹的堆叠在星系团位置。这些测试用于理解噪声与天体物理残留(如CIB)之间的平衡,以及不同地图中tSZ信号的恢复。例如,对星系团位置的堆叠结果证实了在约1500平方度SPT-3G调查区域中恢复的tSZ信号的稳健性。这里产生的高分辨率、低噪声地图为未来研究提供了重要的宇宙学工具,包括Compton-y地图功率谱测量、与其他大尺度结构踪迹的交叉相关、详细建模星系团压力剖面以及研究宇宙中宇宙射线的热力学状态。

英文摘要:

We present thermal Sunyaev-Zel'dovich (tSZ) Compton-$y$ parameter maps constructed from two years (2019-2020) of observations with the South Pole Telescope (SPT) third-generation camera, SPT-3G, combined with data from the Planck satellite. Using a linear combination (LC) pipeline, we obtain a suite of reconstructions that explore different trade-offs between statistical sensitivity and suppression of astrophysical contaminants, including minimum-variance, CMB-deprojected, and CIB-deprojected $y$-maps. We validate these maps through different statistical techniques such as auto- and cross-power spectra with large-scale structure tracers as well as stacking on cluster locations. These tests are used to understand the balance between noise and astrophysical foreground residuals (such as the CIB) in combination with the recovery of the tSZ signal for different maps. For example, results from stacking at the location of clusters confirm the robustness of the recovered tSZ signal over the $\sim 1500\: {\rm deg}^2$ SPT-3G survey field used in this analysis. The high-resolution and low-noise maps produced here provide an important cosmological tool for future studies, including measurements of the Compton-$y$ map power spectrum, cross-correlations with other tracers of the large-scale structure, detailed modeling of cluster pressure profiles, and study of the thermodynamic state of the baryons in the Universe.

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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