Phase Curves of Small Bodies from the SLOAN Moving Objects Catalog

TL;DR

This paper introduces a novel automated method combining Monte Carlo simulations and Bayesian inference to accurately derive phase curves and absolute magnitudes of small bodies from sparse, multi-filter photometric data, accounting for rotational variations.

Contribution

It presents the first method to incorporate rotational variations explicitly in phase curve analysis, improving accuracy in large-scale small body surveys.

Findings

Produced nearly 15,000 phase curves from SLOAN data.

Method yields absolute magnitudes and colors consistent with previous studies.

Includes uncertainties and rotational effects, enhancing data reliability.

Abstract

Extensive photometric surveys are and will continue producing massive amounts of data on small bodies. Usually, these data will be sparsely obtained at arbitrary (and unknown)rotational phases. Therefore, new methods to process such data need to be developed to make the most of those large catalogs. We aim to produce a method to create phase curves of small bodies considering the uncertainties introduced by the nominal errors in the magnitudes and the effect introduced by rotational variations. We use the SLOAN Moving Objects Catalog data as a benchmark to construct phase curves of all small bodies in there, in u', g', r', i', and z' filters. We will obtain from the phase curves the absolute magnitudes and set up with them the absolute colors, which are the colors of the asteroids not affected by changes in phase angle. We select objects with $\geq3$ observations taken in at least one filter and spanned over a minimum of 5 degrees in phase angle. We developed a method that combines Monte Carlo simulations and Bayesian inference to estimate the absolute magnitudes using the HG$_{12}^*$ photometric system. We obtained almost 15\,000 phase curves, about 12,000 including all five filters. The absolute magnitudes and absolute colors are compatible with previously published data, supporting our method. The method we developed is fully automatic and well suited to be run on large amounts of data. Moreover, it includes the nominal uncertainties in the magnitudes and the whole distribution of possible rotational states of the objects producing, possibly, less precise values, i.e., larger uncertainties, but more accurate, i.e., closer to the actual value. To our knowledge, this work is the first to include the effect of rotational variations in such a manner.