Rotation periods of asteroids from light curves of TESS data

¹ Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange, 06304 Nice Cedex 4, France
² Institute of Applied Astronomy, Russian Academy of Sciences, Kutuzova emb. 10, St. Petersburg, Russia

^★ Corresponding authors; vavilov@iaaras.ru; benoit.carry@oca.eu

Received: 13 December 2023
Accepted: 18 March 2024

Abstract

Context. Understanding the dynamical evolution of asteroids through the secular Yarkovsky effect requires the determination of many physical properties, including the rotation period.

Aims. We propose a method aimed at obtaining a robust determination of the rotation period of asteroids, while avoiding the pitfalls of aliases. We applied this approach to thousands of asteroid light curves measured by the NASA TESS mission.

Methods. We developed a robust period-analysis algorithm based on a Fourier series. Our approach includes a comparison of the results from multiple orders and tests on the number of extremes to identify and reject potential aliases. We also provide the uncertainty interval for the result as well as additional periods that may be plausible.

Results. We report the rotation period for 4521 asteroids within a precision of 10%. A comparison with the literature (whenever available) reveals a very good agreement and validates the approach presented here. Our approach also highlights cases for which the determination of the period should be considered invalid. The dataset presented here confirms the apparent small number of asteroids with a rotation between 50 and 100 h and correlated with diameter. The amplitude of the light curves is found to increase toward smaller diameters, as asteroids become less and less spherical. Finally, there is a systematic difference between the broad C and S complex in the amplitude-period, revealing the statistically lower density of C-types compared to S-type asteroids.

Conclusions. Our approach to the determination of asteroid rotation period is based on simple concepts, yet it is nonetheless robust. It can be applied to large corpora of time series photometry, such as those extracted from exoplanet transit surveys.