论文标题

使用Murchison Wideffield阵列,从回报时期的21〜cm信号的多频角功率谱

Multi-frequency angular power spectrum of the 21~cm signal from the Epoch of Reionisation using the Murchison Widefield Array

论文作者

Trott, Cathryn M., Mondal, Rajesh, Mellema, Garrelt, Murray, Steven G., Greig, Bradley, Line, Jack L. B., Barry, Nichole, Morales, Miguel F.

论文摘要

多频角功率谱(MAP)是球形平均功率谱的替代方案,并且在无需视线光谱变换的情况下计算角功率光谱中的局部波动。测试不同的方法以对前景污染的地图和处理,并与球形平均功率谱和单频角功率谱进行比较。我们将地图应用于$ z = 6.2-7.5 $的110〜小时数据,用于默奇森广场的重新离子化实验时期,以计算21〜cm亮度温度波动的统计功率。在存在明亮的前景的情况下,应用了一个过滤器,以在应用之前删除大规模模式,从而大大降低了由于系统学的地图功率。与模拟的21 〜cm宇宙学信号相对于滤波器后的光谱间隔,图显示了10 $^2 $ -10 $^3 $的对比度为0-厘米的宇宙学信号,这反映了球形平均功率谱的结果。还计算了单频角功率谱。在$ z = 7.5 $和$ l = 200 $时,我们找到了53〜mk $^2 $的角功率,超过了模拟的宇宙信号功率一千倍。残留的光谱结构是校准数据固有的,而不是大规模模式的光谱泄漏,是系统功率偏置的主要来源。与球形平均功率谱相比,单频角功率谱的结果略差,并且在应用光谱滤波器以减少前景后。对其他过滤器的探索可能会改善此结果,并考虑更宽的带宽。

The Multi-frequency Angular Power Spectrum (MAPS) is an alternative to spherically-averaged power spectra, and computes local fluctuations in the angular power spectrum without need for line-of-sight spectral transform. To test different approaches to MAPS and treatment of the foreground contamination, and compare with the spherically-averaged power spectrum, and the single-frequency angular power spectrum. We apply the MAPS to 110~hours of data in $z=6.2-7.5$ obtained for the Murchison Widefield Array Epoch of Reionisation experiment to compute the statistical power of 21~cm brightness temperature fluctuations. In the presence of bright foregrounds, a filter is applied to remove large-scale modes prior to MAPS application, significantly reducing MAPS power due to systematics. The MAPS shows a contrast of 10$^2$--10$^3$ to a simulated 21~cm cosmological signal for spectral separations of 0--4~MHz after application of the filter, reflecting results for the spherically-averaged power spectrum. The single-frequency angular power spectrum is also computed. At $z=7.5$ and $l=200$, we find an angular power of 53~mK$^2$, exceeding a simulated cosmological signal power by a factor of one thousand. Residual spectral structure, inherent to the calibrated data, and not spectral leakage from large-scale modes, is the dominant source of systematic power bias. The single-frequency angular power spectrum yields slightly poorer results compared with the spherically-averaged power spectrum, having applied a spectral filter to reduce foregrounds. Exploration of other filters may improve this result, along with consideration of wider bandwidths.

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