Asteroid models from the Lowell Photometric Database

TL;DR

This paper presents a large-scale effort to derive asteroid shape models and spin states from the Lowell Photometric Database using distributed computing, confirming known distributions and addressing data biases.

Contribution

It introduces a scalable method combining convex shape modeling and triaxial ellipsoid fitting, significantly increasing the number of asteroid models from sparse photometry.

Findings

Derived 328 new asteroid models, nearly doubling existing data.

Confirmed non-uniform distribution of asteroid spin axes.

Identified biases towards elongated bodies in noisy data.

Abstract

We use the lightcurve inversion method to derive new shape models and spin states of asteroids from the sparse-in-time photometry compiled in the Lowell Photometric Database. To speed up the time-consuming process of scanning the period parameter space through the use of convex shape models, we use the distributed computing project Asteroids@home, running on the Berkeley Open Infrastructure for Network Computing (BOINC) platform. This way, the period-search interval is divided into hundreds of smaller intervals. These intervals are scanned separately by different volunteers and then joined together. We also use an alternative, faster, approach when searching the best-fit period by using a model of triaxial ellipsoid. By this, we can independently confirm periods found with convex models and also find rotation periods for some of those asteroids for which the convex-model approach gives too many solutions. From the analysis of Lowell photometric data of the first 100,000 numbered asteroids, we derived 328 new models. This almost doubles the number of available models. We tested the reliability of our results by comparing models that were derived from purely Lowell data with those based on dense lightcurves, and we found that the rate of false-positive solutions is very low. We also present updated plots of the distribution of spin obliquities and pole ecliptic longitudes that confirm previous findings about a non-uniform distribution of spin axes. However, the models reconstructed from noisy sparse data are heavily biased towards more elongated bodies with high lightcurve amplitudes.