利用并行差分进化和蜂窝体形状模型,从多条光曲线加速小行星周期和极点反演

Yong-Xiong Zhang, Wen-Xiu Guo, Xiao-Ping Lu, Hua Zheng, Hai-bin Zhao, Jun Tian, Wei-Lin Wang
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引用次数: 0

摘要

确定小行星的特性可以提供宝贵的物理洞察力,但从光度光曲线反演这些特性仍然需要大量计算。本文提出了一种新方法,将简化的 Cellinoid 形状模型与并行微分进化(PDE)算法相结合,以加快反演速度。PDE 算法比微分演化(DE)算法更高效,在多核 CPU 上使用 64 个工作站时,速度提高了 37.983 倍。PDE 算法能从模拟数据中准确推导出周期和极值。对真实小行星光曲线的分析验证了该方法的可靠性:与其他地方发表的结果相比,PDE 算法准确地恢复了旋转周期,并且在适当的观测几何条件下,与极点方向非常吻合。PDE 方法可在 20,000 次迭代和不到一小时的时间内收敛到解决方案,证明了其在大规模数据分析方面的潜力。这项工作通过克服关键的计算瓶颈,为揭示小行星的物理特性提供了一种很有前途的新工具。
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Accelerating Asteroidal Period and Pole Inversion from Multiple Lightcurves Using Parallel Differential Evolution and Cellinoid Shape Model
Determining asteroid properties provides valuable physical insights but inverting them from photometric lightcurves remains computationally intensive. This paper presents a new approach that combines a simplified Cellinoid shape model with the Parallel Differential Evolution (PDE) algorithm to accelerate inversion. The PDE algorithm is more efficient than the Differential Evolution (DE) algorithm, achieving an extraordinary speedup of 37.983 with 64 workers on multicore CPUs. The PDE algorithm accurately derives period and pole values from simulated data. The analysis of real asteroid lightcurves validates the method's reliability: in comparison with results published elsewhere, the PDE algorithm accurately recovers the rotational periods and, given adequate viewing geometries, closely matches the pole orientations. The PDE approach converges to solutions within 20,000 iterations and under one hour, demonstrating its potential for large-scale data analysis. This work provides a promising new tool for unveiling asteroid physical properties by overcoming key computational bottlenecks.
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