Increasing the Spectral Resolution of Broadband Astronomical Imaging

最新一代的望远镜和仪器正在打开一扇新的窗口，让我们了解多变的瞬变宇宙。即将到来的时域调查将收集大部分夜空的数千个观测年代。但这些数据提供了超越时间序列研究的机会，该项目将致力于实现更高分辨率形状测量和改进星系特性推断的承诺。初步的工作已经显示出未来成功的希望。应用数学、计算机科学和天文学的学生将联手应对真实数据和微妙影响的挑战。多历元图像和视频分析可以说是当今数据科学中发展最快的主题。本研究创建的图像和方法对于激励和训练学生是理想的。衍生的视觉表现也是一种有效的公共宣传工具。超越瞬态和可变宇宙的科学首先通过重叠、重复曝光的过采样来实现，这可以产生更高的空间分辨率和形状测量，从而改进弱透镜、宇宙学和星系形态学研究。不太明显的是，在宽通带内引入多个有效子带将显著改善星系性质的推断和光度红移的估计。这项研究将致力于实现这些机会所需的新方法，通过(1)评估这些想法在实际情况下的局限性，包括大型天气巡天望远镜（LSST）的光度红移估计；(2)开发最优、高效的重建方法；(3)实现生产质量代码，从多历元天文观测中生成超分辨率超彩色图像。建立在可靠统计数据基础上的方法已经在单色环境中取得了成功，但多色环境将需要对大气中的差色折射（DCR）进行详细的建模。这将需要一种新的方法来利用由于DCR而导致的源方向上与颜色相关的细微变化。部分创建的教育材料将公开提供Python的Jupyter笔记本。科学数据的彩色渲染图将被开发成实时的Jupyter笔记本，并在剪刀服务器上提供给所有人，只需使用浏览器就可以查看和修改。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。

The latest-generation telescopes and instruments are opening a new window on the variable and transient universe. Forthcoming time-domain surveys will collect thousands of observational epochs for most of the night sky. But these data provide opportunities beyond the study of time series, and this project will work towards realizing the promise of higher resolution shape measurement and improving the inference of galaxy properties. Preliminary work has already shown the promise of future success. Students in applied mathematics, computer science and astronomy will join forces to tackle the challenges of real data and subtle effects. Multi-epoch image and video analysis is arguably the fastest growing topic in data science today. The images and methods created in this study are ideal for inspiring and training students. The derived visual representations are also an efficient vehicle for public outreach.The science beyond the transient and variable cosmos comes firstly by oversampling in overlapping, repeated exposures, which can produce higher spatial resolution and shape measurements, thus improving weak lensing, cosmology and galaxy morphology studies. Less obviously, introducing multiple effective sub-bands within a broad passband will significantly improve the inference of galaxy properties and the estimation of photometric redshifts. This study will work towards the new methods needed to realize these opportunities, by (1) evaluating the limitations of these ideas in real situations, including photometric redshift estimation for the Large Synoptic Survey Telescope (LSST); (2) developing optimal, efficient methods for reconstruction; and (3) implementing production-quality code to make super-resolution hyper-color images from multi-epoch astronomical observations. The approach of building on robust statistics has already been successful in a monochrome setting, but multi-color will need detailed modeling of differential chromatic refraction (DCR) in the atmosphere. This will require a new method to leverage the color-dependent subtle shifts in source directions due to DCR. Part of the created educational material will be publicly available Jupyter notebooks in Python. Colorful renderings of scientific data will be developed as live Jupyter notebooks and made available on the SciServer for all to view and to modify, using just a browser.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.