Machine Learning for the Zwicky Transient Facility
Machine Learning for the Zwicky Transient Facility
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DOI：
10.1088/1538-3873/aaf3fa
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发表时间：
2019-01
影响因子：
3.5
通讯作者：
A. Mahabal;U. Rebbapragada;R. Walters;F. Masci;N. Blagorodnova;J. Roestel;Q. Ye;R. Biswas;K. Bu
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文献类型：
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作者：
A. Mahabal;U. Rebbapragada;R. Walters;F. Masci;N. Blagorodnova;J. Roestel;Q. Ye;R. Biswas;K. Bu
Zwicky Transient Facility是一个大型的光学调查，在多个过滤器中每晚产生数十万个瞬态警报。我们在这里描述了各种机器学习（ML）实现，并计划通过利用数据的时间特性来最大限度地利用大型数据集，并进一步将其与其他数据集相结合。我们从最初的步骤开始，从真实的物体中分离出虚假的候选者，分离出恒星和星系，然后将真实的物体分类为各种类别。除了通常的方法（例如，基于从光变曲线提取的特征），我们还描述了用于替代方法的早期计划，包括使用域自适应和深度学习。以类似的方式，我们描述了探测快速移动小行星的努力。我们还描述了使用Zooniverse平台通过创建训练样本和主动学习来帮助分类。最后，我们从ML的角度提到了ZTF和LSST的协同作用。
The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids. We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective.