低频辐射计时间序列数据的奇异谱分析及其对SITARA数据的应用
Singular spectrum analysis of time series data from low frequency radiometers, with an application to SITARA data
摘要：
了解低频射电望远镜数据的时间特性对于制定适当的校准策略具有重要意义。将时间序列分析技术应用于射电望远镜的数据可以揭示大量有助于校准的信息。在本文中，我们研究奇异谱分析（SSA）作为一种分析工具，无线电数据。我们显示SSA和傅立叶技术之间的密切联系。我们从一个理想化的周期性数据集开始开发相关的数学，并继续包括各种非理想行为。我们提出了一种新的技术，以获得长期的增益变化的数据，利用通过天线波束的天空漂移所产生的周期性。我们还模拟了几种可能的场景，并将这些技术应用于2021年6月从SITARA收集的30天时间序列数据，SITARA是一种用于全球21厘米探测的短间距双元件干涉仪。将该技术应用于实际数据，我们发现，第一个重建的组件-趋势-具有很强的反相关性，与当地温度表明温度波动作为最有可能的起源为观测到的数据变化。我们还研究了存在昼夜增益变化的校准的局限性，并发现这种变化是可能的障碍，以SSA校准SITARA数据。
英文摘要：
Understanding the temporal characteristics of data from low frequency radio telescopes is of importance in devising suitable calibration strategies. Application of time series analysis techniques to data from radio telescopes can reveal a wealth of information that can aid in calibration. In this paper, we investigate singular spectrum analysis (SSA) as an analysis tool for radio data. We show the intimate connection between SSA and Fourier techniques. We develop the relevant mathematics starting with an idealised periodic dataset and proceeding to include various non-ideal behaviours. We propose a novel technique to obtain long-term gain changes in data, leveraging the periodicity arising from sky drift through the antenna beams. We also simulate several plausible scenarios and apply the techniques to a 30-day time series data collected during June 2021 from SITARA - a short-spacing two element interferometer for global 21-cm detection. Applying the techniques to real data, we find that the first reconstructed component - the trend - has a strong anti-correlation with the local temperature suggesting temperature fluctuations as the most likely origin for the observed variations in the data. We also study the limitations of the calibration in the presence of diurnal gain variations and find that such variations are the likely impediment to calibrating SITARA data with SSA.
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