Enabling KARMMA as a Tool of Precision Cosmology

让 KARMMA 成为精密宇宙学的工具

• 批准号: 2306667
• 负责人: EDUARDO ROZO
• 金额: $ 49.78万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2306667&HistoricalAwards=false
• 关键词: Enabling; KARMMA; Tool; Precision; Cosmology

Gravity bends light. Consequently, adding mass in front of an image will distort the image. Measuring these distortions allow us to determine the distribution of mass that was added. The Rubin Observatory Legacy Survey of Space and Time (LSST) will use this gravitational lensing effect to measure the distribution of dark matter in the Universe, with the goal of better understanding what drives the accelerated expansion of our Universe. Traditionally, this type of analysis is done by calculating summary statistics: think of taking a map of the matter density, and summarizing all that information by, say, the average distance between peaks. This “compression” is done by necessity: predicting summary statistics is “easy”, whereas predicting dark matter maps is hard. Scientists at the University of Arizona seek to combine machine learning techniques with recent developments to extract cosmological information from the mass maps themselves. Doing so will ensure that the cosmological information contained in the data will be extracted in an optimal way. As part of this project, the PI will act as a volunteer faculty member in the newly developed TIMESTEP research apprenticeship program at the University of Arizona, training undergraduates on research and coding skills that will improve their ability to secure summer research internships, be it REU programs or in industry.The standard approach for extracting cosmological information from weak lensing survey data relies on the shear correlation function. However, the fact that the matter density field today is non-gaussian renders this approach sub-optimal: if the information contained in its non-gaussian features could be extracted, the cosmological constraints from cosmic shear experiments could be improved by a factor of two or more. Fields-based inference has emerged as the obvious choice for realizing this goal. The team proposes to build on the success of the KARMMA mass mapping algorithm to develop the first practical and accurate field-based inference framework. This work will overcome the need to run dark matter simulations by using approximate methods for modeling non-linear growth. Specifically, the plan is to: 1) extend the KARMMA framework to enable tomographic mass map and cosmological sampling; and 2) train a convolutional neural network to perturb the KARMMA lognormal maps into simulation-quality maps. Efficient sampling of the posterior will be achieved through the use of Hamiltonian Markov Chains. The full inference framework will then be validated using simulated data sets.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.

引力使光弯曲。因此，在图像前面添加质量会使图像失真。 测量这些扭曲使我们能够确定添加的质量分布。鲁宾天文台遗产空间和时间调查（LSST）将利用这种引力透镜效应来测量宇宙中暗物质的分布，目的是更好地了解是什么推动了我们宇宙的加速膨胀。传统上，这种类型的分析是通过计算汇总统计来完成的：考虑一下物质密度的地图，并通过峰值之间的平均距离来汇总所有信息。 这种“压缩”是必要的：预测概括统计是“容易的”，而预测暗物质地图是困难的。亚利桑那大学的科学家们试图将联合收割机机器学习技术与最近的发展相结合，从质量图中提取宇宙学信息。这样做将确保以最佳方式提取数据中包含的宇宙学信息。作为该项目的一部分，PI将在亚利桑那大学新开发的TIMESTEP研究学徒计划中担任志愿教师，对本科生进行研究和编码技能培训，这将提高他们获得夏季研究实习的能力，无论是REU项目还是工业。从弱透镜调查数据中提取宇宙学信息的标准方法依赖于剪切相关函数。然而，事实上，今天的物质密度场是非高斯的，这使得这种方法不是最佳的：如果包含在其非高斯特征中的信息可以被提取出来，那么来自宇宙剪切实验的宇宙学约束可以改善两倍或更多。基于场的推理已经成为实现这一目标的明显选择。该团队建议在KARMMA质量映射算法的成功基础上开发第一个实用且准确的基于场的推理框架。这项工作将克服需要运行暗物质模拟使用近似方法建模非线性增长。具体来说，该计划是：1）扩展KARMMA框架，以实现断层质量图和宇宙学采样; 2）训练卷积神经网络，将KARMMA对数正态图扰动为模拟质量图。后验的有效采样将通过使用哈密顿马尔可夫链来实现。该奖项反映了NSF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。