快速、大容量、GPU使Ly-alpha森林的模拟成为可能：重子声学振荡实验的功率谱预测
Fast, large volume, GPU enabled simulations for the Ly-alpha forest: power spectrum forecasts for baryon acoustic oscillation experiments
摘要：
来自大型Ly-alpha森林巡天的重子声学振荡标度（BAO）的高红移测量代表了暗能量研究的下一个前沿。作为这项工作的一部分，将需要对Ly-alpha森林的BAO签名进行有效模拟。为此，我们构建了一个模型，用于生产快速，大容量的模拟Ly-alpha森林。利用经过校准的半分析方法，我们能够在1 Gpc^3卷中运行非常大的模拟，使用支持GPU的代码版本，在台式电脑上在不到一天的时间内完全解决了Jeans规模。从我们的半解析模拟中提取的Ly-alpha森林光谱与从完全流体动力学参考模拟中获得的光谱非常一致。此外，我们发现我们的模拟数据与观测测量的通量概率分布和一维通量功率谱是广泛一致的。我们能够正确地恢复输入BAO规模从三维Ly-alpha通量功率谱测量从我们的模拟数据，并估计，每平方度约15个背景源和每像素约5个信噪比的类似BOSS的10^4 deg^2调查应实现BAO尺度的测量，误差在约1.4%以内。我们还使用我们的模拟来提供简单的功率-的法律表达式估计的分数误差的BAO尺度上的变化的信号噪声和背景源的数量密度。我们的方法的速度和灵活性非常适合探索参数空间和观测和天体物理系统学的影响，从即将到来的大规模光谱调查的BAO签名的恢复。
英文摘要：
High redshift measurements of the baryonic acoustic oscillation scale (BAO) from large Ly-alpha forest surveys represent the next frontier of dark energy studies. As part of this effort, efficient simulations of the BAO signature from the Ly-alpha forest will be required. We construct a model for producing fast, large volume simulations of the Ly-alpha forest for this purpose. Utilising a calibrated semi-analytic approach, we are able to run very large simulations in 1 Gpc^3 volumes which fully resolve the Jeans scale in less than a day on a desktop PC using a GPU enabled version of our code. The Ly-alpha forest spectra extracted from our semi-analytical simulations are in excellent agreement with those obtained from a fully hydrodynamical reference simulation. Furthermore, we find our simulated data are in broad agreement with observational measurements of the flux probability distribution and 1D flux power spectrum. We are able to correctly recover the input BAO scale from the 3D Ly-alpha flux power spectrum measured from our simulated data, and estimate that a BOSS-like 10^4 deg^2 survey with ~15 background sources per square degree and a signal-to-noise of ~5 per pixel should achieve a measurement of the BAO scale to within ~1.4 per cent. We also use our simulations to provide simple power-law expressions for estimating the fractional error on the BAO scale on varying the signal-to-noise and the number density of background sources. The speed and flexibility of our approach is well suited for exploring parameter space and the impact of observational and astrophysical systematics on the recovery of the BAO signature from forthcoming large scale spectroscopic surveys.
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