Extracting Asteroid Physical Properties from Vera C. Rubin Observatory Science Validation Survey Data: Rotational Periods, Colors, and Taxonomies
Abstract
The Vera C. Rubin Observatory will enable large-scale characterization of minor bodies through high-cadence, multi-band photometry. Early Science Validation (SV) observations from 2025-2026 provide one of the first extensive datasets for Solar System studies, but typically with sparser and more heterogeneous coverage than the Rubin First Look data, where hundreds of observations were available for well-characterized objects. In this work, we build on the methodology developed for the Rubin First Look data to extract asteroid rotation periods, amplitudes, colors, and taxonomic classifications from early Rubin SV data using multi-band (griz) photometry. Rotation periods are determined through a combination of high-order Fourier light-curve fitting and multi-band Lomb-Scargle analysis. A unified light-curve model is then used to derive rotation-corrected colors, which are converted into taxonomic classes using calibrated color indices and spectral slopes. We validate the method on objects with sufficient observational coverage and show that preliminary physical properties can be estimated from sparse and irregular datasets, while robust determinations require improved temporal coverage. By simulating sparsely observed data, we identify the observational conditions required for period determinations. The inclusion of z-band photometry extends traditional color-based taxonomy and improves compositional discrimination relative to gri-only analyses.
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BibTeX
@misc{carruba2026extracting,
title = {{Extracting Asteroid Physical Properties from Vera C. Rubin Observatory Science Validation Survey Data: Rotational Periods, Colors, and Taxonomies}},
author = {V. Carruba and S. Greenstreet and M. Mastropietro and S. Aljbaae and G. Caritá and R. C. Domingos and M. Huaman and M. M. Bala and R. D. Z. Pedroso and E. M. D. S. Delfino},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.29841},
eprint = {2609.29841},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.29841},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Extracting Asteroid Physical Properties from Vera C. Rubin Observatory Science Validation Survey Data: Rotational Periods, Colors, and Taxonomies
AU - V. Carruba
AU - S. Greenstreet
AU - M. Mastropietro
AU - S. Aljbaae
AU - G. Caritá
AU - R. C. Domingos
AU - M. Huaman
AU - M. M. Bala
AU - R. D. Z. Pedroso
AU - E. M. D. S. Delfino
PY - 2026
DA - 2026/09/24
PB - arXiv
DO - 10.48550/arxiv.2609.29841
UR - https://arxiv.org/abs/2609.29841
AB - The Vera C. Rubin Observatory will enable large-scale characterization of minor bodies through high-cadence, multi-band photometry. Early Science Validation (SV) observations from 2025-2026 provide one of the first extensive datasets for Solar System studies, but typically with sparser and more heterogeneous coverage than the Rubin First Look data, where hundreds of observations were available for well-characterized objects. In this work, we build on the methodology developed for the Rubin First Look data to extract asteroid rotation periods, amplitudes, colors, and taxonomic classifications from early Rubin SV data using multi-band (griz) photometry. Rotation periods are determined through a combination of high-order Fourier light-curve fitting and multi-band Lomb-Scargle analysis. A unified light-curve model is then used to derive rotation-corrected colors, which are converted into taxonomic classes using calibrated color indices and spectral slopes. We validate the method on objects with sufficient observational coverage and show that preliminary physical properties can be estimated from sparse and irregular datasets, while robust determinations require improved temporal coverage. By simulating sparsely observed data, we identify the observational conditions required for period determinations. The inclusion of z-band photometry extends traditional color-based taxonomy and improves compositional discrimination relative to gri-only analyses.
ER -