BIGDATA: IA: Query-by-example for Big astronomical Data

虽然现代观测天体物理学中最重要的项目产生非常大的数据集，但计算方法落后，并且难以有效地分析这些数据。 尽管当前和未来的天文学调查将产生世界上最大的公共数据库，但将这些数据转化为科学发现的方法还不存在。 该项目将开发一个自动查询系统，利用无监督机器学习技术，根据星系与其他星系的相似性对星系进行分类。 这种能力目前还不存在，它可以大大提高数字巡天的经验和发现能力。 该项目有一个教育部分，特别侧重于为代表性不足的少数民族学生进行本科研究，现有的，特别是计划中的调查可以对数十亿个星系进行成像，因此通过计算机分析研究稀有星系的能力是绝对必要的。 这些不寻常的物体对于理解早期，现在和未来宇宙的最基本问题至关重要，因为它们携带了关于物体相互作用，形成和演化历史的关键信息。 待开发的系统将拍摄研究人员确定为感兴趣的某个（通常是奇特的）星系的图像，并将搜索数百万个星系，以找到与查询星系视觉上最相似的星系。 由于研究不寻常的星系并对它们的性质作出科学结论需要一定的人口，从中得出可以与其他系统进行比较的性质，这种能力和由此产生的清单将大大提高从天空调查中发现的能力，优化这些重要而昂贵的研究仪器的科学回报。该项目与现有的吸引代表性不足的少数群体的努力相结合，在他们本科学位的最后几年增加更多的高级研究培训。 像这样的研究总是包括公共宣传的机会，该团队希望为他们所在地区形成大数据中心做出贡献。

While the most significant projects in modern observational astrophysics generate very large data sets, the computational methodology lags behind, and has trouble effectively analyzing these data. Although current and future astronomical surveys will produce the world's largest public databases, the methods to turn these data into scientific discoveries do not yet exist. This project will develop an automatic query-by-example system for classifying galaxies by their similarities to other galaxies, using unsupervised machine learning techniques. This capability does not currently exist and it can substantially enhance the experience and discovery power of digital sky surveys. The project has an educational component, focusing in particular on undergraduate research for under-represented minority students.Existing, and especially planned, surveys can image billions of galaxies, making the ability to study rare galaxies through computer analysis absolutely essential. These uncommon objects are critical for understanding the most fundamental questions about the early, present, and future universe, as they carry crucial information on the history of the interactions of objects, their formation, and their evolution. The system to be developed will take an image of a certain (normally peculiar) galaxy, identified by the researcher as being of interest, and will search through millions of galaxies to find the visually most similar galaxies to the query galaxy. Because studying unusual galaxies and making scientific conclusions about their nature requires a certain population from which to derive properties that can be compared to other systems, this capability and the resulting listings will greatly increase the ability to make discoveries from sky surveys, optimizing the scientific return of these important and expensive research instruments.This project connects with existing efforts to attract under-represented minorities, adding more advanced research training in the later years of their undergraduate degree. Studies like this always include opportunities for public outreach, and the team expects to contribute to the forming big data hub in their area.