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arXiv 2111.12596astro-ph.EPastro-ph.IM
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稀疏小行星光变曲线的高斯过程表征

Characterizing Sparse Asteroid Light Curves with Gaussian Processes

Christina Willecke Lindberg, Daniela Huppenkothen, R. Lynne Jones, Bryce T. Bolin, Mario Juric, V. Zach Golkhou, Eric C. Bellm, Andrew J. Drake, Matthew J. Grah… 展开作者

Christina Willecke Lindberg, Daniela Huppenkothen, R. Lynne Jones, Bryce T. Bolin, Mario Juric, V. Zach Golkhou, Eric C. Bellm, Andrew J. Drake, Matthew J. Graham, Russ R. Laher, Ashish A. Mahabal, Frank J. Masci, Reed Riddle, Kyung Min Shin

摘要:

在像Zwicky Transient Facility和Rubin Observatory's Legacy Survey of Space and Time这样的宽场测量时代,稀疏的光度测量在小行星观测中所占的比例越来越大,特别是对于这些大型测量中新发现的小行星。后续观测,以补充这些稀疏的数据可能是昂贵的,在许多情况下,为了克服这些采样的限制,我们引入了一个灵活的模型,高斯过程的基础上,使贝叶斯参数推断的小行星时间序列数据。该模型具有灵活性和可扩展性,可以同时模拟多个小行星属性,如旋转周期,光变曲线幅度,变化的脉冲轮廓以及由于相位角演变而引起的幅度变化。在这里,我们专注于旋转周期的推断。基于模拟的光变曲线和Zwicky瞬态设施的实际观测,我们表明,新模型可靠地推断旋转周期从稀疏采样的光变曲线,一般提供良好的约束后验概率密度的模型参数。我们提出这个框架作为快速,但非常有限的周期检测算法和更全面,但计算昂贵的形状建模的基础上射线跟踪代码之间的中间方法。

英文摘要:

In the era of wide-field surveys like the Zwicky Transient Facility and the Rubin Observatory's Legacy Survey of Space and Time, sparse photometric measurements constitute an increasing percentage of asteroid observations, particularly for asteroids newly discovered in these large surveys. Follow-up observations to supplement these sparse data may be prohibitively expensive in many cases, so to overcome these sampling limitations, we introduce a flexible model based on Gaussian Processes to enable Bayesian parameter inference of asteroid time series data. This model is designed to be flexible and extensible, and can model multiple asteroid properties such as the rotation period, light curve amplitude, changing pulse profile, and magnitude changes due to the phase angle evolution at the same time. Here, we focus on the inference of rotation periods. Based on both simulated light curves and real observations from the Zwicky Transient Facility, we show that the new model reliably infers rotational periods from sparsely sampled light curves, and generally provides well-constrained posterior probability densities for the model parameters. We propose this framework as an intermediate method between fast, but very limited period detection algorithms and much more comprehensive, but computationally expensive shape modeling based on ray-tracing codes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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