arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.01767astro-ph.COastro-ph.IM
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使用MeerKAT的HI强度标测:1/f噪声分析

HI intensity mapping with MeerKAT: 1/f noise analysis

Yichao Li, Mario G. Santos, Keith Grainge, Stuart Harper, Jingying Wang

摘要:

单碟射电望远镜的时间相关噪声成分(1/f噪声)的性质对强度测绘实验中HI信号的可探测性至关重要。在本文中,我们提出了1/f噪声特性的MeerKAT接收机系统使用南天极(SCP)跟踪数据。我们估计每个天线和极化的时间功率谱密度和2D功率谱密度。我们将奇异值分解(SVD)应用于数据集,并表明,通过去除最强的分量,可以大大减少1/f噪声,这表明它在频率上高度相关。在没有SVD模式减法的情况下,$20\,$ MHz积分上的拐点频率高于$0.1\,\rm Hz$ ;仅使用$2$ ~模式减法,拐点频率降低到$\sim 3\times 10^{-3}\,{\rm Hz}$,这表明系统诱导的1/f型变化在几百秒时间尺度上完全处于热噪声波动之下。二维功率谱表明,1/f型变化被限制在一个小的区域中的时间-频率空间,无论是在频率或时间的长波长相关。这给出了一个宽范围的宇宙学尺度,其中21厘米的信号可以被测量,而无需进一步校准增益时间波动。最后,我们证明了一个简单的功率谱参数化足以描述数据,并提供拟合参数的1D和2D功率谱。

英文摘要:

The nature of the time correlated noise component (the 1/f noise) of single dish radio telescopes is critical to the detectability of the HI signal in intensity mapping experiments. In this paper, we present the 1/f noise properties of the MeerKAT receiver system using South Celestial Pole (SCP) tracking data. We estimate both the temporal power spectrum density and the 2D power spectrum density for each of the antennas and polarizations. We apply Singular Value Decomposition (SVD) to the dataset and show that, by removing the strongest components, the 1/f noise can be drastically reduced, indicating that it is highly correlated in frequency. Without SVD mode subtraction, the knee frequency over a $20\,$MHz integration is higher than $0.1\,\rm Hz$; with just $2$~mode subtraction, the knee frequency is reduced to $\sim 3\times 10^{-3}\,{\rm Hz}$, indicating that the system induced 1/f-type variations are well under the thermal noise fluctuations over a few hundred seconds time scales. The 2D power spectrum shows that the 1/f-type variations are restricted to a small region in the time-frequency space, either with long wavelength correlations in frequency or in time. This gives a wide range of cosmological scales where the 21cm signal can be measured without further need to calibrate the gain time fluctuations. Finally, we demonstrate that a simple power spectrum parameterization is sufficient to describe the data and provide fitting parameters for both the 1D and 2D power spectrum.

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