CDS&E: A Modern Toolkit to Enhance the Scientific Productivity of Optical Survey Data

CDS

• 批准号: 2307070
• 负责人: Michelle Ntampaka
• 金额: $ 45.69万
• 依托单位: Johns Hopkins University
• 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: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2307070&HistoricalAwards=false
• 关键词: CDS&E; Modern; Toolkit; Enhance; Scientific

The modern cosmological model describes how tiny density fluctuations in the early Universe evolved into today's cosmic web of galaxies and dark matter. Though the model successfully describes much of our present-day large-scale structure, it also has unexplained tensions. For example, early- and late-time observations make conflicting measurements of sigma-8, a parameter that describes the clumpiness of the Universe. Large optical cosmological surveys are ushering in a golden age for data-driven cosmology that can address this tension: the Rubin Observatory and the Dark Energy Spectroscopic Instrument will soon provide exquisitely detailed maps of the sky, making it possible to explore cosmology's fingerprints on large-scale structure. These modern observations warrant being analyzed with equally modern data science methods, and this research program will support a team of scientists at Johns Hopkins University to develop understandable machine learning tools that can interpret upcoming surveys and address the sigma-8 tension. To engage the next generation in astronomy, this program will also support the development of play-based STEM lessons that teach early elementary school students about light, shadows, moon phases, and eclipses through stories and play. The kindergarten lesson plans that are developed through this program will be made publicly available to astronomers and educators. The decadal survey’s Pathways to Discovery identified the crucial role that machine learning (ML) could play in the next decade, leading to transformative discoveries from the decade’s rich, upcoming data sets. While ML has historically been touted as a black box that can generate order-of-magnitude improvements at the cost of interpretability, this does not need to be the case – modern techniques are making it possible to develop ML tools that improve results while still being understandable and leading to physical discoveries. This research program will develop a toolkit of understandable ML methods for interpreting detailed optical galaxy surveys from the Rubin Observatory and the Dark Energy Spectroscopic Instrument to explore the sigma-8 tension. This research will 1) produce a statistical census of cosmological information at small scales, probing techniques for describing how sub-Mpc structures correlate with the underlying cosmological model, 2) produce a low-scatter galaxy cluster dynamical mass proxy using symbolic regression to provide a closed-form, complementary framework for quantifying the abundance of massive clusters at low redshift, and 3) develop a deep learning approach for estimating galaxy cluster ellipticity, a major source of systematic error in weak lensing cosmological analyses.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.

现代宇宙学模型描述了早期宇宙中微小的密度波动如何演变成今天的星系和暗物质宇宙网。虽然这个模型成功地描述了我们今天的大规模结构，但它也有无法解释的张力。例如，早期和晚期的观测对sigma-8的测量结果相互矛盾，sigma-8是一个描述宇宙团块的参数。大型光学宇宙学调查正在迎来一个可以解决这种紧张关系的数据驱动宇宙学的黄金时代：鲁宾天文台和暗能量光谱仪将很快提供精美详细的天空地图，使探索宇宙学在大尺度结构上的指纹成为可能。这些现代观测结果值得用同样现代的数据科学方法进行分析，这项研究计划将支持约翰霍普金斯大学的一个科学家团队开发可理解的机器学习工具，这些工具可以解释即将到来的调查并解决sigma-8紧张局势。为了让下一代参与天文学，该计划还将支持开发基于游戏的STEM课程，通过故事和游戏向小学生教授光，阴影，月相和日食。通过该计划制定的幼儿园课程计划将向天文学家和教育工作者公开。十年期调查的发现之路确定了机器学习（ML）在未来十年中可能发挥的关键作用，从而从十年丰富的即将到来的数据集中带来变革性的发现。虽然ML在历史上一直被吹捧为一个黑盒子，可以以可解释性为代价来产生数量级的改进，但事实并非如此-现代技术正在使开发ML工具成为可能，这些工具可以改进结果，同时仍然可以理解并导致物理发现。该研究计划将开发一个可理解的ML方法工具包，用于解释鲁宾天文台和暗能量光谱仪的详细光学星系调查，以探索sigma-8张力。这项研究将1）在小尺度上产生宇宙学信息的统计普查，探索描述亚Mpc结构如何与基础宇宙学模型相关的技术，2）使用符号回归产生低散射星系团动力学质量代理，以提供一个封闭形式的补充框架，用于量化低红移的大质量星系团的丰度，以及3）开发一种深度学习方法来估计星系团椭圆率，这是弱透镜宇宙学分析中系统误差的主要来源。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。