Characterizing Sparse Asteroid Light Curves with Gaussian Processes

Christina Willecke Lindberg,R Lynne Jones,Reed Riddle,Matthew J Graham,Andrew J Drake,Bryce T Bolin,Kyung Min Shin,Mario Jurić,Daniela Huppenkothen,Ashish A Mahabal,V Zach Golkhou,Eric C Bellm,Frank J Masci,Russ R Laher

doi:10.3847/1538-3881/ac3079

Abstract

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.

Highlights

Asteroids are small rocky bodies known to contain information about the formation of planetary objects in our Solar System
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 dataa)
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

Summary

Introduction

Asteroids are small rocky bodies known to contain information about the formation of planetary objects in our Solar System. Their shapes, sizes, spin, orbits, and compositions all help us constrain models for asteroid formation, collision history, and orbital evolution. These models influence our understanding of the protoa) code can be found at AsteroGaP planetary disk along with the evolution and distribution of materials such as water in our Solar System. The amount of light observed by a telescope on Earth at a given point in time depends on the illuminated asteroid surface visible by the telescope as well as surface properties such as composition and albedo. Because asteroids move with respect to both Earth and Sun, and rotate around their own axis, the observed flux will change as a function of time (Barucci & Fulchignoni 1982)

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