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，用于当前和未来调查的光学瞬变的光度分类。 光学瞬变的发现率已经超过了传统的光谱分类方法，并且随着未来的调查，只有一小部分的发现可以通过光谱观察到。 因此，光度分类是必不可少的，在真实的时间识别罕见的瞬变，分类瞬变，使人口研究，并发现新的类别的瞬变。 每个目标都需要不同的ML和算法方法。 现在，适当的数据已经掌握并可用于初始分类测试，这是测试用于实时发现罕见瞬态的管道的正确时机，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，并涉及非计算机科学专业，包括学生使用大口径望远镜进行观察，以及K-12学生的科学博览会体验。该项目建立在该团队最近成功使用一系列ML算法创建初始分类管道的基础上，这些算法经过训练，然后应用于真实的数据。 它结合了大型调查数据，ML技术和主动多波长跟踪，为学生和博士后在天文学中的大数据科学技术做好准备。 该项目将开发管道：（i）将基于时间序列的光变曲线分类与基于图像的宿主星系分类相结合;（ii）开发，测试和实施针对特定类别的已知罕见瞬变的ML管道;（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域天体物理学应用。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.