Collaborative Research: DeepCMB: New Measurements of the Cosmic Microwave Background with Deep Learning

研究宇宙微波背景(CMB)使我们能够测试早期宇宙的膨胀模型、跨越宇宙时间的大尺度结构的形成以及粒子物理的标准模型。尽管下一代望远镜将以新的精度水平观测CMB，但主要挑战将是减少系统的不确定性。该项目将开发新的分析技术，以从即将到来的高度灵敏的CMB实验中获益。芝加哥地区现有的伙伴关系将被用来培训来自代表性不足背景的学生在计算和数据科学方面的能力。在宇宙学和数据科学方面，指导博士后研究人员和研究生将被用来为研究环境中代表性不足的群体开发机会和技能。这项工作涉及创建深度神经网络，以执行高信噪比信息提取，实现对r(张量标量比)的改进限制，并增加在较高红移和较低质量处发现的星系团的数量。这项研究将使用模拟数据和南极望远镜(SPT)的数据：1)产生一个可扩展的框架，用于快速模拟模拟CMB数据集；2)使用神经网络来执行银河系和银河系外前景的清理和去中心化；3)使用深度学习分类和回归来补充现有的星系团发现算法。为CMB应用开发的工具将对其他科学和深度学习科学本身产生交叉影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Studying the cosmic microwave background (CMB) allows us to test models of inflation in the early universe, the formation of large-scale structures across cosmic time, and the standard model of particle physics. Although next-generation telescopes will observe the CMB at new levels of precision, the primary challenge will be to reduce systematic uncertainties. This project will develop new analysis techniques that can reap the benefit of these forthcoming highly sensitive CMB experiments. Existing partnerships in the Chicago area will be leveraged to train students from underrepresented backgrounds in computation and data science. Mentoring postdoctoral researchers and graduate students in cosmology and data science will be used to develop opportunities and skillsets for underrepresented groups in research environments.The work involves creating deep neural networks to perform high signal-to-noise extraction of information, to enable improved limits on r, the tensor-to-scalar ratio, and to increase the number of detected galaxy clusters at higher redshifts and lower masses. The study will use both mock data and data from the South Pole Telescope (SPT) to: 1) produce an extensible framework for the fast simulation of mock CMB data sets; 2) use neural networks to perform galactic and extragalactic foreground cleaning and delensing; 3) use deep learning classfication and regression to complement existing galaxy clusterfinding algorithms. Tools developed for this CMB application will have cross-cutting effects on other sciences, and on the science of deep learning itself.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.