CDS&E: Collaborative Research: Development and Application of Machine Learning Classification of Optical Transients

该项目将开发、测试和使用一系列机器学习(ML)算法和管道，用于从当前和未来的调查中对光学瞬变进行光度分类。光学瞬变的发现率已经超过了传统的光谱分类方法，随着未来的调查，他们的发现中只有一小部分可以用光谱观察到。因此，光度分类对于实时识别罕见的瞬变、对瞬变进行分类以便进行人口研究以及发现新的瞬变类别是必不可少的。这些目标中的每一个都需要不同的ML和算法方法。现在已经有了适当的数据并可用于初始分类测试，现在是测试用于实时发现罕见瞬变的管道的合适时机，为未来更大的数据量做准备。学生和博士后将获得开发和实施ML算法的经验，进行天文瞬变的光谱和多波长研究。这一经验将融入本科教育，将课堂学习和实践研究联系起来，让非计算机科学专业的学生参与进来，包括用大口径望远镜观察学生，以及为K-12学生提供科学展览体验。该项目建立在该团队最近使用一系列ML算法创建初始分类管道的成功基础上，这些算法经过培训，然后应用于真实数据。它结合了大型测量数据、ML技术和积极的多波长跟踪，为学生和博士后准备天文学中的大数据科学技术。该项目将开发管道以：(I)将基于时间序列的光曲线分类与基于图像的宿主星系分类相结合；(Ii)针对特定类别的已知罕见瞬变开发、测试和实施ML管道；以及(Iii)设计异常检测算法以发现新类型的罕见瞬变。这项工作产生的分类工具将用于广泛的时间域天体物理学应用。这一奖项反映了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。

This project will develop, test, and use, a range of machine learning (ML) algorithms and pipelines for the photometric classification of optical transients from current and future surveys. The discovery rate of optical transients already outpaces traditional spectroscopic classification methods, and with future surveys only a tiny fraction of their discoveries can be observed spectroscopically. Photometric classification is therefore essential, for identifying rare transients in real time, for classifying transients to allow population studies, and for discovering new classes of transient. Each of these goals requires different ML and algorithmic approaches. Now that appropriate data are in hand and usable for initial classification tests, this is the right time to test the pipelines to be used for the real-time discovery of rare transients, in preparation for future much larger data volumes. Students and postdocs will gain experience developing and implementing ML algorithms, carrying out spectroscopic and multi-wavelength studies of astronomical transients. This experience will feed into undergraduate education, connecting classroom learning and hands-on research and involving non-computer science majors, including student observing with large-aperture telescopes, and science fair experiences for K-12 students.This project builds on recent successes by this team in creating initial classification pipelines using a range of ML algorithms, which were trained on, and then applied to, real data. It draws on a combination of large survey data, ML techniques, and active multi-wavelength follow-up, to prepare students and postdocs for Big Data scientific techniques in astronomy. This project will develop pipelines to: (i) combine time-series based light curve classification with image-based host galaxy classification; (ii) develop, test, and implement ML pipelines targeted at specific classes of known rare transients; and (iii) design algorithms for anomaly detection to discover new types of rare transients. The classification tools produced by this work will be used for a wide range of time-domain astrophysics applications.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.