Abstract
With ever-increasing numbers of astrophysical transient surveys, new facilities and archives of astronomical time series, time domain astronomy is emerging as a mainstream discipline. However, the sheer volume of data alone - hundreds of observations for hundreds of millions of sources – necessitates advanced statistical and machine learning methodologies for scientific discovery: characterization, categorization, and classification. Whilst these techniques are slowly entering the astronomer’s toolkit, their application to astronomical problems is not without its issues. In this paper, we will review some of the challenges posed by trying to identify variable stars in large data collections, including appropriate feature representations, dealing with uncertainties, establishing ground truths, and simple discrete classes.
Highlights
Time domain astronomy is entering a golden age with an ever-increasing number of new instruments and facilities dedicated to repeated observations of large swathes of sky every few nights or so
The success of automated classification relies on having access to the greatest amount of information and in this era of data-intensive astronomy, there is no lack of science exploration and discovery to do
We found that no single algorithm was generally better than ∼ 60% accurate across the full data set
Summary
Time domain astronomy is entering a golden age with an ever-increasing number of new instruments and facilities dedicated to repeated observations of large swathes of sky every few nights or so. Even though many of these are dedicated to looking for real-time transients (things changing significantly from past observations, if any), they can quickly generate substantial archives of data Systematic searches of these for particular types of astrophysical source or phenomena require new approaches and in recent years, there have been a number of studies attempting automated classification and outlier detection using machine learning-based techniques (see Table 1). These are all with an eye to the generation of synoptic sky surveys, e.g., Gaia ([1]), ZTF ([2]), and LSST ([3]), which will increase the amount of available data by several orders of magnitude and mandate such approaches. Survey ASAS CoRoT Kepler Hipparcos OGLE-II Stripe 82 LINEAR CRTS VVV EROS-2 WISE
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