An Explorative Approach for Inspecting Kepler Data

TL;DR

This paper introduces a novel visualization method for Kepler time series data, enabling exploration and revealing correlations between light curve variability and stellar physical properties.

Contribution

It proposes a new dimensionality reduction technique based on model parameter prediction error for visualizing astrophysical time series data.

Findings

Visualization correlates light curve variability with stellar properties

Temperature and surface gravity inferred from non-periodic light curves

Enhanced understanding of stellar variability patterns

Abstract

The Kepler survey has provided a wealth of astrophysical knowledge by continuously monitoring over 150,000 stars. The resulting database contains thousands of examples of known variability types and at least as many that cannot be classified yet. In order to reveal the knowledge hidden in the database, we introduce a new visualisation method that allows us to inspect time series exploratively. To that end, we propose dimensionality reduction on the parameters of a model capable of representing time series as fixed-length vector representation. We show that a more refined objective function can be chosen by minimising the prediction error of the data reconstruction instead of the reconstruction of the model parameters. The proposed visualisation exhibits a strong correlation between the variability behaviour of the light curves and their physical properties. As a consequence, temperature and surface gravity can, for some stars, be directly inferred from non- (or quasi-) periodic light curves.