A Bayesian method to mitigate the effects of unmodelled time-varying systematics for 21-cm cosmology experiments
A Bayesian method to mitigate the effects of unmodelled time-varying systematics for 21-cm cosmology experiments
复制标题
一种减轻 21 厘米宇宙学实验中未建模时变系统学影响的贝叶斯方法
DOI：
10.1093/mnras/stad3725
复制
发表时间：
2024
影响因子：
4.8
通讯作者：
Kirkham C
中科院分区：
文献类型：
--
作者：
Kirkham C
来自宇宙黎明和再电离时代的中性氢信号的无线电观测有助于提供对第一批恒星和星系性质的限制。由于来自宇宙黎明号的全球21厘米宇宙学信号在观测时间尺度上实际上是恒定的，而且由于系统学产生的影响会随着时间而变化，因此可以在不需要系统模型的情况下减轻这些系统学的影响。我们提出了一种方法来解释21厘米射电宇宙学实验中未建模的时变系统，使用平方指数高斯过程核以完全贝叶斯的方式来解释时间箱之间的相关性。通过改变模拟系统的模型参数，我们发现高斯过程方法通过在系统存在时扩大后验并减少平均拟合参数中的偏差，提高了我们恢复信号参数的能力。当将模型正弦系统的振幅变化为21厘米信号振幅的0.25至2.00倍，周期变化为信号宽度的0.5至4.0倍时，我们发现拟合信号的均方根误差平均改善了5%。我们可以使用拟合的高斯过程超参数来识别数据中系统的存在，证明该方法作为诊断工具的实用性。此外，我们可以使用高斯过程回归来计算残差随时间的平均拟合，为产生时变系统的模型提供基础。
Radio observations of the neutral hydrogen signal from the Cosmic Dawn and Epoch of Reionization have helped to provide constraints on the properties of the first stars and galaxies. Since this global 21-cm cosmological signal from the Cosmic Dawn is effectively constant on observing time-scales and since effects resulting from systematics will vary with time, the effects of these systematics can be mitigated without the need for a model of the systematic. We present a method to account for unmodelled time-varying systematics in 21-cm radio cosmology experiments using a squared exponential Gaussian process kernel to account for correlations between time bins in a fully Bayesian way. We find by varying the model parameters of a simulated systematic that the Gaussian process method improves our ability to recover the signal parameters by widening the posterior in the presence of a systematic and reducing the bias in the mean fit parameters. When varying the amplitude of a model sinusoidal systematic between 0.25 and 2.00 times the 21-cm signal amplitude and the period between 0.5 and 4.0 times the signal width, we find on average a 5 per cent improvement in the root mean squared error of the fitted signal. We can use the fitted Gaussian process hyperparameters to identify the presence of a systematic in the data, demonstrating the method’s utility as a diagnostic tool. Furthermore, we can use Gaussian process regression to calculate a mean fit to the residuals over time, providing a basis for producing a model of the time-varying systematic.