从Vera C. Rubin天文台科学验证巡天数据中提取小行星物理性质：自转周期、颜色和分类
Extracting Asteroid Physical Properties from Vera C. Rubin Observatory Science Validation Survey Data: Rotational Periods, Colors, and Taxonomies
- São Paulo State University (UNESP)（圣保罗州立大学）
- Laboratório Interinstitucional de e-Astronomia（跨机构电子天文实验室）
- NSF–DOE Vera C. Rubin Observatory / NSF NOIRLab（美国国家科学基金会-能源部薇拉·C·鲁宾天文台/美国国家科学基金会NOIRLab）
- DiRAC Institute and the Department of Astronomy, University of Washington（华盛顿大学天文学系及DiRAC研究所）
- Instituto de Astronomía y Ciencias Planetarias, Universidad de Atacama（阿塔卡马大学天文与行星科学研究所）
- Universidad Tecnológica del Perú (UTP)（秘鲁理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究基于Rubin天文台SV数据，通过傅里叶拟合与Lomb-Scargle分析提取小行星自转周期，利用统一光变模型获取颜色并分类，验证稀疏数据可估计初步性质，且z波段提升成分判别。
AI中文摘要：
Vera C. Rubin天文台将通过高频率、多波段测光实现对小天体的大规模表征。2025-2026年的早期科学验证（SV）观测提供了太阳系研究首批广泛数据集之一，但其覆盖通常比Rubin First Look数据更稀疏且更不均匀，后者为充分表征的天体提供了数百次观测。在本工作中，我们基于为Rubin First Look数据开发的方法，利用早期Rubin SV数据的多波段（griz）测光提取小行星自转周期、振幅、颜色和分类学类别。自转周期通过高阶傅里叶光变曲线拟合与多波段Lomb-Scargle分析的组合来确定。随后使用统一光变曲线模型推导出经自转校正的颜色，并通过校准的颜色指数和光谱斜率将其转换为分类学类别。我们在具有充分观测覆盖的天体上验证了该方法，并表明初步物理性质可从稀疏和不规则数据中估计，而可靠的确定需要改进的时间覆盖。通过模拟稀疏观测数据，我们确定了周期测定所需的观测条件。z波段测光的纳入扩展了基于颜色的传统分类学，并相对于仅gri分析提高了成分判别能力。
英文摘要：
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.
