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arXiv 2102.02310astro-ph.CO
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用21厘米双谱估计再电离参数的历元

Epoch of reionization parameter estimation with the 21-cm bispectrum

Catherine A. Watkinson, Bradley Greig, Andrei Mesinger

摘要:

我们提出了第一个应用程序的等腰双谱MCMC参数推断宇宙21厘米的信号。我们扩展了MCMC采样器21 cmMC,以便在计算似然性时使用快速双谱代码BiFFT。我们使用SKA 1-low创建模拟1000小时观测,使用PyObs 21来解释UV采样和热噪声。假设自旋温度远高于CMB,我们考虑两个不同的再电离历史为我们的模拟观测:基准和后期再电离。对于这两种模型,我们发现,通过使用等腰双谱(针对各种尺度和三角形形状计算)以及功率谱(而不是仅使用其中一种统计量),可以大大减少推断参数均值和1- $σ$可信区间的偏差。我们发现,使简化假设的高斯似然与对角协方差矩阵不显着偏置参数约束的三参数再电离模型和基本的仪器效应考虑在这里。即使我们使用极端的(不太可能的)初始条件,这也是正确的,因为这些初始条件预计会放大偏差。我们还发现,使用宇宙方差误差计算与蒙特卡罗模拟使用基准模型参数,同时假设后期再电离模型的模拟数据也没有强烈的偏见的推断。这意味着我们可以在参数空间上稀疏地采样和插值宇宙方差误差,从而大大降低计算成本。在这项工作中使用的所有代码都是公开的。

英文摘要:

We present the first application of the isosceles bispectrum to MCMC parameter inference from the cosmic 21-cm signal. We extend the MCMC sampler 21cmMC to use the fast bispectrum code, BiFFT, when computing the likelihood. We create mock 1000h observations with SKA1-low, using PyObs21 to account for uv-sampling and thermal noise. Assuming the spin temperature is much higher than that of the CMB, we consider two different reionization histories for our mock observations: fiducial and late-reionization. For both models we find that bias on the inferred parameter means and 1-$σ$ credible intervals can be substantially reduced by using the isosceles bispectrum (calculated for a wide range of scales and triangle shapes) together with the power spectrum (as opposed to just using one of the statistics). We find that making the simplifying assumption of a Gaussian likelihood with a diagonal covariance matrix does not notably bias parameter constraints for the three-parameter reionization model and basic instrumental effects considered here. This is true even if we use extreme (unlikely) initial conditions which would be expected to amplify biases. We also find that using the cosmic variance error calculated with Monte-Carlo simulations using the fiducial model parameters whilst assuming the late-reionization model for the simulated data also does not strongly bias the inference. This implies we may be able to sparsely sample and interpolate the cosmic variance error over the parameter space, substantially reducing computational costs. All codes used in this work are publicly-available.

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