Effectively using unsupervised machine learning in next generation astronomical surveys

Abstract

In recent years many works have shown that unsupervised Machine Learning (ML) can help detect unusual objects and uncover trends in large astronomical datasets, but a few challenges remain. We show here, for example, that different methods, or even small variations of the same method, can produce significantly different outcomes. While intuitively somewhat surprising, this can naturally occur when applying unsupervised ML to highly dimensional data, where there can be many reasonable yet different answers to the same question. In such a case the outcome of any single unsupervised ML method should be considered a sample from a conceivably wide range of possibilities. We therefore suggest an approach that eschews finding an optimal outcome, instead facilitating the production and examination of many valid ones. This can be achieved by incorporating unsupervised ML into data visualization portals. We present here such a portal that we are developing, applied to the sample of SDSS spectra of galaxies. The main feature of the portal is interactive 2D maps of the data. Different maps are constructed by applying dimensionality reduction to different subspaces of the data, so that each map contains different information that in turn gives a different perspective on the data. The interactive maps are intuitive to use, and we demonstrate how peculiar objects and trends can be detected by means of a few button clicks. We believe that including tools in this spirit in next generation astronomical surveys will be important for making unexpected discoveries, either by professional astronomers or by citizen scientists, and will generally enable the benefits of visual inspection even when dealing with very complex and extensive datasets. Our portal is available online at galaxyportal.space .