Galaxy intrinsic alignments: A new window into structure in the Universe

星系内在排列:了解宇宙结构的新窗口

基本信息

  • 批准号:
    2206563
  • 负责人:
  • 金额:
    $ 40.11万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-09-01 至 2025-08-31
  • 项目状态:
    未结题

项目摘要

Observations of galaxies can be used to map out structure in the Universe and probe some of the greatest mysteries in physics, including “dark matter” and “dark energy.” Upcoming efforts that involve new big telescopes and large collaborations of scientists will observe billions of galaxies. To harness these impressive data sets, it is essential to understand how galaxy properties, especially their shapes, relate to their surrounding environment. Scientists at Northeastern University will use state-of-the-art observations as well as theoretical computer simulations to develop the understanding of galaxy shapes. These advances will enable more accurate studies of the underlying cosmological model, a key goal of the scientific community. Through research opportunities and targeted mentoring, this project will also advance undergraduate training in physics and astronomy, with a particular focus on first-generation, undocumented, and low-income (FUNL) students. Galaxy “intrinsic alignments” (IA) are an important astrophysical effect in which the intrinsic shapes of galaxies are correlated with large-scale structure in the Universe. This effect impacts analyses that involve weak gravitational lensing and galaxy clustering, including planned key studies using data from the upcoming Vera C. Rubin Observatory. IA must be sufficiently well understood to allow tests of the cosmological model that are both accurate and precise. This project will significantly improve the understanding of IA through a coherent program of targeted observations and novel, efficient simulation techniques. Using the final data set of the Dark Energy Survey, the team will employ several strategies designed to probe IA in different regimes, unified through a single analysis framework. Informed by these measurements, simulated galaxies with realistic IA will be produced from gravity-only simulations, avoiding the computational challenges of hydrodynamic simulations, and allowing the production of mock galaxy catalogs in large cosmological volumes and with a wide range of IA properties. The insights from these efforts will enable a substantial improvement in the ability to mitigate IA in future analyses. In addition, the team will use their results and simulation capabilities to develop IA as a probe of astrophysics and cosmology with next-generation data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
对星系的观测可以用来绘制宇宙的结构,并探索物理学中一些最大的谜团,包括“暗物质”和“暗能量”。未来的努力将涉及新的大型望远镜和科学家的大型合作,将观测数十亿个星系。为了利用这些令人印象深刻的数据集,有必要了解星系的特性,特别是它们的形状,与周围环境的关系。东北大学的科学家将使用最先进的观测和理论计算机模拟来发展对星系形状的理解。这些进步将使人们能够更准确地研究潜在的宇宙模型,这是科学界的一个关键目标。通过研究机会和有针对性的指导,该项目还将推进物理学和天文学的本科培训,特别关注第一代无证低收入学生。星系“本征排列”(IA)是一种重要的天体物理效应,其中星系的本征形状与宇宙中的大尺度结构相关。这种效应影响了涉及弱引力透镜和星系群集的分析,包括计划使用即将到来的Vera C. Rubin天文台数据的关键研究。IA必须被充分地理解,以允许对宇宙模型进行既准确又精确的测试。该项目将通过有针对性的观测和新颖、高效的模拟技术,显著提高对IA的理解。利用暗能量调查的最终数据集,该团队将采用几种策略来探测不同制度下的IA,通过一个单一的分析框架进行统一。根据这些测量结果,具有真实IA的模拟星系将从重力模拟中产生,避免了流体动力学模拟的计算挑战,并允许在大的宇宙体积中产生模拟星系目录,并具有广泛的IA特性。从这些工作中获得的见解将大大提高在未来分析中减轻IA的能力。此外,该团队将利用他们的结果和模拟能力,将IA开发为具有下一代数据的天体物理学和宇宙学探测器。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
An empirical approach to model selection: weak lensing and intrinsic alignments
模型选择的实证方法:弱透镜效应和内在对准
The Dark Energy Survey Year 3 and eBOSS: constraining galaxy intrinsic alignments across luminosity and colour space
第三年暗能量调查和 eBOSS:限制星系在亮度和色彩空间上的内在排列
  • DOI:
    10.1093/mnras/stad2013
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    4.8
  • 作者:
    Samuroff, S.;Mandelbaum, R.;Blazek, J.;Campos, A.;MacCrann, N.;Zacharegkas, G.;Amon, A.;Prat, J.;Singh, S.;Elvin-Poole, J.
  • 通讯作者:
    Elvin-Poole, J.
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Jonathan Blazek其他文献

Learning Galaxy Intrinsic Alignment Correlations
学习星系内在排列相关性
  • DOI:
    10.48550/arxiv.2404.13702
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Sneh Pandya;Yuanyuan Yang;N. V. Alfen;Jonathan Blazek;Robin Walters
  • 通讯作者:
    Robin Walters

Jonathan Blazek的其他文献

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