Characterizing Sparse Asteroid Light Curves with Gaussian Processes

Abstract

In the era of wide-field surveys like the Zwicky Transient Facility and the Rubin Observatory's Legacy Survey of Space and Time, sparse photometric measurements constitute an increasing percentage of asteroid observations, particularly for asteroids newly discovered in these large surveys. Follow-up observations to supplement these sparse data may be prohibitively expensive in many cases, so to overcome these sampling limitations, we introduce a flexible model based on Gaussian Processes to enable Bayesian parameter inference of asteroid time series data. This model is designed to be flexible and extensible, and can model multiple asteroid properties such as the rotation period, light curve amplitude, changing pulse profile, and magnitude changes due to the phase angle evolution at the same time. Here, we focus on the inference of rotation periods. Based on both simulated light curves and real observations from the Zwicky Transient Facility, we show that the new model reliably infers rotational periods from sparsely sampled light curves, and generally provides well-constrained posterior probability densities for the model parameters. We propose this framework as an intermediate method between fast, but very limited period detection algorithms and much more comprehensive, but computationally expensive shape modeling based on ray-tracing codes.