论文标题
来自特殊速度的哈密顿蒙特卡洛重建
Hamiltonian Monte Carlo reconstruction from peculiar velocities
论文作者
论文摘要
通过汉密尔顿蒙特卡洛（HMC）采样，在贝叶斯框架内解决了从特殊速度调查重建大规模密度和速度场的问题。哈密顿蒙特卡洛重建本地环境（Hamlet）算法旨在重建线性的大规模密度和速度场，并结合在派生速度的派生距离和速度中的lognormal偏差结合使用。在LCDM标准模型的宇宙学模型的框架内，针对由多达30 000个数据点组成的宇宙流量目录进行了测试。 Hamlet代码的表现优于Gibbs从COSMICFLOWS-3数据中对MCMC重建的先前应用，在CPU时间中，将MCMC重建量占2至4个数量级。性能的增益是由于HMC算法的固有效率较高，并且是由于GPU上而不是CPU上的并行计算引起的。该增益将使即将到来的CosmicFows-4数据以及宇宙学高分辨率模拟的约束初始条件的设置增加大规模结构的重建。
The problem of the reconstruction of the large scale density and velocity fields from peculiar velocities surveys is addressed here within a Bayesian framework by means of Hamiltonian Monte Carlo (HMC) sampling. The HAmiltonian Monte carlo reconstruction of the Local EnvironmenT (Hamlet) algorithm is designed to reconstruct the linear large scale density and velocity fields in conjunction with the undoing of lognormal bias in the derived distances and velocities of peculiar velocities surveys such as the Cosmicflows data. The Hamlet code has been tested against Cosmicflows mock catalogs consisting of up to 30 000 data points with mock errors akin to those of the Cosmicflows-3 data, within the framework of the LCDM standard model of cosmology. The Hamlet code outperforms previous applications of Gibbs sampling MCMC reconstruction from the Cosmicflows-3 data by two to four orders of magnitude in CPU time. The gain in performance is due to the inherent higher efficiency of the HMC algorithm and due to parallel computing on GPUs rather than CPUs. This gain will enable an increase in the reconstruction of the large scale structure from the upcoming Cosmicfows-4 data and the setting of constrained initial conditions for cosmological high resolution simulations.
