论文标题
带有贝叶斯统计的全局EOR信号的直接参数推断
Direct Parameter Inference from Global EoR Signal with Bayesian Statistics
论文作者
论文摘要
在观察来自电离时期的天空平均HI信号时，模型参数推断可以是计算密集型工作，这使得很难通过在贝叶斯框架中使用MCMC采样方法执行直接的一阶段模型参数推断。取而代之的是，通常使用两个阶段的推理，即，首先估算EOR频谱模型上某些特征点的参数，然后将其用作进一步估算物理模型参数的输入。但是，一些以前的作品已经注意到，这种方法可能会偏向结果，并且回答直接执行直接一阶段MCMC采样并获得无偏见的物理模型参数估计的问题可能是有意义的。在这项工作中，我们研究了这个问题并确认了可行性。我们发现，可以通过单阶段直接MCMC采样方法获得对物理模型参数的无偏估计。我们还研究了某些因素的影响，这些因素应在实际观察结果中被考虑以模拟参数推断。我们发现，具有复杂频谱结构的非常小的放大器增益校准误差（$ 10^{-5} $相对误差）可能会显着偏置参数估计；频率依赖性天线束和地理位置也会影响结果，因此应仔细处理。
In the observation of sky-averaged HI signal from Epoch of Reionization, model parameter inference can be a computation-intensive work, which makes it hard to perform a direct one-stage model parameter inference by using MCMC sampling method in Bayesian framework. Instead, a two-stage inference is usually used, i.e., the parameters of some characteristic points on the EoR spectrum model are first estimated, which are then used as the input to estimate physical model parameters further. However, some previous works had noticed that this kind of method could bias results, and it could be meaningful to answer the question of whether it is feasible to perform direct one-stage MCMC sampling and obtain unbiased physical model parameter estimations. In this work, we studied this problem and confirmed the feasibility. We find that unbiased estimations to physical model parameters can be obtained with a one-stage direct MCMC sampling method. We also study the influence of some factors that should be considered in practical observations to model parameter inference. We find that a very tiny amplifier gain calibration error ($10^{-5}$ relative error) with complex spectral structures can significantly bias the parameter estimation; the frequency-dependent antenna beam and geographical position can also influence the results, so that should be carefully handled.
