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SpaceHub: A high-performance gravity integration toolkit for few-body problems in astrophysics
Monthly Notices of the Royal Astronomical Society ( IF 4.7 ) Pub Date : 2021-04-28 , DOI: 10.1093/mnras/stab1189
Yi-Han Wang 1 , Nathan W C Leigh 2, 3 , Bin Liu 4, 5 , Rosalba Perna 1, 6
Affiliation  

We present the open source few-body gravity integration toolkit SpaceHub. SpaceHub offers a variety of algorithmic methods, including the unique algorithms AR-Radau, AR-Sym6, AR-ABITS, and AR-chain+ which we show outperform other methods in the literature and allow for fast, precise, and accurate computations to deal with few-body problems ranging from interacting black holes to planetary dynamics. We show that AR-Sym6 and AR-chain+, with algorithmic regularization, chain algorithm, active round-off error compensation and a symplectic kernel implementation, are the fastest and most accurate algorithms to treat black hole dynamics with extreme mass ratios, extreme eccentricities, and very close encounters. AR-Radau, the first regularized Radau integrator with round off error control down to 64 bits floating point machine precision, has the ability to handle extremely eccentric orbits and close approaches in long-term integrations. AR-ABITS, a bit efficient arbitrary precision method, achieves any precision with the least CPU cost compared to other open source arbitrary precision few-body codes. With the implementation of deep numerical and code optimization, these new algorithms in SpaceHub prove superior to other popular high precision few-body codes in terms of performance, accuracy, and speed.

中文翻译:

SpaceHub:用于天体物理学中少体问题的高性能重力集成工具包

我们展示了开源的少体重力集成工具包 SpaceHub。SpaceHub 提供了多种算法方法,包括独特的算法 AR-Radau、AR-Sym6、AR-ABITS 和 AR-chain+,我们展示的这些算法优于文献中的其他方法,并允许快速、精确和准确的计算来处理从相互作用的黑洞到行星动力学的少体问题。我们表明,AR-Sym6 和 AR-chain+ 具有算法正则化、链式算法、主动舍入误差补偿和辛核实现,是处理具有极端质量比、极端偏心率的黑洞动力学的最快和最准确的算法,和非常亲密的相遇。AR-Radau,第一个具有舍入误差控制到 64 位浮点机器精度的正则化 Radau 积分器,有能力在长期积分中处理极端偏心的轨道和接近的方法。AR-ABITS 是一种高效的任意精度方法,与其他开源任意精度少体代码相比,以最少的 CPU 成本实现任意精度。随着深度数值和代码优化的实施,SpaceHub 中的这些新算法在性能、准确性和速度方面证明优于其他流行的高精度少体代码。
更新日期:2021-04-28
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