CDI-Type I: A Unified Probabilistic Model of Astronomical Imaging

概述：在过去的十年里，天体物理界已经在广泛的波长通道上产生了一拍字节的成像数据，并计划在未来产生一千倍的数据。与此同时，计算机科学机器学习社区已经开发出强大的方法来从大型、异质的数据集中可扩展地提取知识。这个项目是为世界上任何望远镜拍摄的每一幅数字天文图像的每一个像素建立一个模型--一个详细的定量解释--包括那些来自业余爱好者和业余爱好者的图像。技术描述：所提出的模型是一个合理的近似概率模型，广泛使用了非参数贝叶斯方法。该模型本质上是分层次的，较高层捕捉恒星和星系之间的规律性，较低层将准确地模拟图像形成过程，纳入所有各种噪声过程。在给定输入数据的情况下，模型的内部参数将包含可能的最佳天文星表；当前的天文星表既不是使用分层概率推理建立的，也不是从所有可用数据的联合建立的。这个星表将使科学成为可能，就像之前所有的天文星表一样：它将包含每一颗恒星的位置、亮度、温度、视差和自行，以及每个星系的位置、强度和形态，即使是在数据收集中有证据但在任何单独的图像中没有(充分)证据的来源。该模型的其他内部参数将包含对所有图像生成硬件的校准属性的定量描述。所有这些产品将有助于改进和扩展现有的天体物理学软件和服务，用于校准和自动数据处理。广泛影响：该项目在公民科学领域提供了独特的机会，因为它利用业余和业余天文爱好者拍摄的图像来创造天文知识，为他们提供与专业天文学家完全相同的贡献方式。该项目将两个不同的领域--机器学习和天文学--结合在一起，因此有可能创建一个新的“推理天文学”子领域。该项目为博士后、研究生和本科生研究人员提供了以研究为基础的高级跨学科培训的更多机会。为该项目创建的所有代码将在开源许可下发布，所有模型组件、参数和其他内部结构将通过项目网站免费传播给更广泛的研究社区：http://Astrometry.net/和http://thetractor.org/。

Overview: The astrophysics community has produced a Petabyte of of imaging data in a wide range of wavelength channels in the last decade, and is planning to produce a thousand times more in the next.At the same time, the computer science machine learning community has developed powerful methods for extracting knowledge scalably from large, heterogenous data sets. This project is to construct a model--a detailed quantitative explanation--for every pixel of every digital astronomical image ever taken by any telescope in the world, including those from amateurs and hobbyists.Technical description: The proposed model is a justified approximate probabilistic model, making extensive use of non-parametric Bayesian methods. The model will be hierarchical in nature, with the higher layers capturing regularities among stars and galaxies, and the lower layers will accurately model the image formation process, incorporating all the various noise processes. The internal parameters of the model will contain the best possible astronomical catalog given the input data; no current astronomical catalog is built using either hierarchical probabilistic inference, or built from the union of all available data. Science will be enabled by this catalog as it has been enabled by all previous astronomical catalogs: it will contain the position, brightness, temperature, parallax, and proper motion of every star and position, intensity, and morphology of every galaxy, even for sources for which there is evidence in the collection of data but not (sufficiently) in any individual image. Other internal parameters of the model will contain a quantitative description of calibration properties for all the image-generating hardware. All these products will help to refine and extend an existing astrophysics software and services for calibration and automated data processing.Broader impacts: This project provides unique opportunities in citizen science since it leverages images taken by amateur and hobbyist astronomers in creating astronomical knowledge by offering them opportunities to contribute in exactly the same fashion as professional astronomers. The project draws together two disparate fields: machine learning and astronomy, and thus has the potential to create a new sub-area of "inferential astronomy". The project offers enhanced opportunities for research-based advanced interdisciplinary training for postdoctoral, graduate and undergraduate researchers. All code created for this project will be released under the open-source license and all model components, parameters, and other internals will be freely disseminated to the wider research community through the project websites at: http://Astrometry.net/ and http://thetractor.org/ .