Euclid: Asteroid rotation periods from the Euclid Ecliptic Survey
B. Y. Irureta-Goyena, B. Altieri, J.-P. Kneib, M. P\"ontinen, O. R. Hainaut, M. R. Alarcon, M. Granvik, A. A. Nucita, B. Carry, M. Devogele, M. Mahlke, R. Vavrek, T. M\"uller, E. Vilenius, C. Snodgrass, R. Kohley, C. Lemon, P. G\'omez-Alvarez, G. Verdoes Kleijn, J. Licandro

TL;DR
This study analyzes over 23,000 appearances of 2,321 asteroids from the Euclid Ecliptic Survey to estimate their rotation periods, providing the first such data for most objects and identifying potential super-fast rotators.
Contribution
The paper introduces a novel pipeline combining Lomb-Scargle and MCMC methods to determine asteroid spin periods from Euclid survey data, including handling period aliases and validating results.
Findings
44% of periods match published data within 1%
98% of periods are within 15% of published values
16 candidate super-fast rotators identified
Abstract
The Euclid Ecliptic Survey was conducted during the calibration phase of the mission, 23-31 December 2023, as a campaign to study Solar System objects. We used data from this survey to analyse more than 23 000 appeareances of 2321 known asteroids. Due to their high apparent angular motion relative to the background stars (5-), these objects appear as streaks in VIS long-exposure images. We set out to estimate their spin periods, since only of them have periods published in the literature. We used multiple apertures along each streak to increase the time resolution of our light curves. Our method combines a Lomb-Scargle approach with a Markov chain Monte Carlo (MCMC) algorithm to characterise the posterior distributions. Some asteroids show multimodality in the MCMC search, indicating period aliases; in these cases, we report all aliases and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
