Eyes on the future: optimizing science output for next generation surveys with joint crowdsourced and automated classification techniques

未来的星系调查将产生如此多的数据，以至于天文学家将不再能够依靠他们以前的分类方法来提取宇宙历史中星系形成和演化的科学。尽管“众包”银河系分类利用了大量的志愿者劳动力，但即使是像银河动物园(GZ)这样的主要努力也无法跟上。该项目将建立在GZ数据库的基础上，将方法扩展到宇宙中的其他时代，同时使用公民科学工作的结果与机器学习一起开发新的自动分类工具。利用公众和更广泛社区参与的已证明的研究价值，以及新一代智能计算机方法，这项研究将建立在两者的最佳基础上。尽管众包星系分类已经通过斯隆数字天空调查(SDSS)十年的数据证明了它们的价值，但使它们成为下一代调查数据处理管道的标准组件仍然存在两个主要挑战。第一个是证明该方法在高红移时的实用性，在高红移时，更多的星系具有不规则或块状的形态。第二个人承认，即使是众包也没有能力承受即将到来的数据量和数据率，这需要新的、更复杂的机器分类算法。该项目将为高红移众包数据和模拟星系编制星表，并开发一种新的自动分类工具，该工具具有适用于多个星系调查的培训管道，可扩展到高红移。它包括三个科学项目：(A)直接限制星系的大小和质量增长率；(B)测量条形占主导地位的盘和活动星系核的燃料之间的任何关系；(C)量化盘亚结构的人口统计和演化。使用参数和非参数技术使形态分类自动化的努力在SDSS中取得了相当成功的成果，但没有扩展到得出必要的详细结构参数。广州的众包项目取得了超出预期的成功，为志愿者工作提供了科学上可行的参数，并导致了100多篇同行评议的论文。这项研究将扩展这两种方法，为未来更大规模的调查做准备，预计每晚产生的数据与十年的SDSS一样多。将生成的高级星表和将使用的新分类算法将公开发布，将是社区的宝贵资源。除了非正式的引导性探究项目，GZ将被实施到本科天文学课程中。参与这项工作的学生将获得明确的博士项目，并获得对他们未来职业生涯有价值的技术技能。

Future galaxy surveys will produce so much data that astronomers will no longer be able to rely on their previous methods of classifying them in order to extract the science of galaxy formation and evolution through cosmic history. Although "crowd-sourced" galaxy classifications tap into a vast resource of volunteer labor, even major efforts like the Galaxy Zoo (GZ) will not be able to keep up. This project will build on the GZ database, extending methods to other epochs in the Universe, and simultaneously use the results of the citizen science work together with machine learning to develop new automated classification tools. Using the proven research value of involving the public and the broader community, and with a new generation of intelligent computer methods, this study will build on the best of both.While crowd-sourced galaxy classifications have proven their worth on a decade of data from the Sloan Digital Sky Survey (SDSS), there remain two major challenges to making them a standard component of the data processing pipelines for the next generation of surveys. The first is proving the utility of the method at high redshifts, where more galaxies have irregular or clumpy morphologies. The second acknowledges that even crowdsourcing does not have the capacity for the data volume and rates that are to come, requiring new more sophisticated machine classification algorithms. This project will develop catalogs for high-redshift crowd-sourced data and for simulated galaxies, and develop a new automated classification tool that extends to higher redshifts with a training pipeline adaptable to multiple galaxy surveys. It includes three science projects: (a) directly constrain galaxy size and mass growth rates; (b) measure any relationship between bar-dominated disks and fueling of active galactic nuclei, and (c) quantify the demographics and evolution of disk sub-structures. Efforts to automate morphological classifications using parametric and non-parametric techniques have been reasonably successful for SDSS, but did not extend to deriving the necessary detailed structural parameters. The GZ crowd-sourcing project has been successful beyond expectations, providing scientifically viable parameters from volunteer work and leading to over a hundred peer-reviewed papers. This study will extend both of these approaches in preparation for much larger future surveys, which expect to produce as much data per night as ten years of SDSS.The high-level catalogs to be produced, and the new classification algorithms to be used, are to be released publically, and will be a valuable resource for the community. Along with informal guided-inquiry projects, GZ will be implemented into undergraduate astronomy courses. Students involved in this work will get well-defined PhD projects and acquire technical skills valuable in their future professional careers.