The impact of tandem redundant/sky-based calibration in MWA Phase II data analysis

The impact of tandem redundant/sky-based calibration in MWA Phase II data analysis
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串联冗余/天基校准对 MWA 第二阶段数据分析的影响

DOI:
10.1017/pasa.2020.37
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发表时间:
2020
影响因子:
6.3
通讯作者:
Barry, Nichole
Barry, Nichole
中科院分区:
物理与天体物理3区
文献类型:
--
作者:
Zhang, Zheng;Pober, Jonathan C.;Li, Wenyang;Hazelton, Bryna J.;Morales, Miguel F.;Trott, Cathryn M.;Jordan, Christopher H.;Joseph, Ronniy C.;Beardsley, Adam;Barry, Nichole

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精确的仪器校准对21厘米宇宙学实验至关重要。Murchison宽场阵列(MWA) II阶段紧凑型配置为我们提供了冗余校准和基于天空的校准算法的机会;同时使用两者是一种潜在的方法,可以减轻由不准确的天空模型引起的校准误差。MWA再电离时代(EoR)实验针对天空的三个区域(称为EoR0, EoR1和EoR2)进行深度观测。Li et al.(2018)和(2019)先前的工作研究了串联校准对EoR0场的影响,发现与单独基于天空的校准相比,串联校准对功率谱(PS)没有显著改善。在这项工作中,我们将类似的技术应用于EoR1油田,并发现了一个明显的结果:串联校准对PS的改善是显著的。为了理解这一结果,我们分析了校准溶液本身以及三个晚上EoR1观测对PS的影响。我们得出结论,EoR1中明亮射电星系天炉座A的存在降低了基于天空的校准性能,这反过来又使冗余校准产生更大的影响。这些结果表明,冗余校准确实可以减轻一定程度的模型不完整性误差。
Precise instrumental calibration is of crucial importance to 21-cm cosmology experiments. The Murchison Widefield Array’s (MWA) Phase II compact configuration offers us opportunities for both redundant calibration and sky-based calibration algorithms; using the two in tandem is a potential approach to mitigate calibration errors caused by inaccurate sky models. The MWA Epoch of Reionization (EoR) experiment targets three patches of the sky (dubbed EoR0, EoR1, and EoR2) with deep observations. Previous work in Li et al. (2018) and (2019) studied the effect of tandem calibration on the EoR0 field and found that it yielded no significant improvement in the power spectrum (PS) over sky-based calibration alone. In this work, we apply similar techniques to the EoR1 field and find a distinct result: the improvements in the PS from tandem calibration are significant. To understand this result, we analyse both the calibration solutions themselves and the effects on the PS over three nights of EoR1 observations. We conclude that the presence of the bright radio galaxy Fornax A in EoR1 degrades the performance of sky-based calibration, which in turn enables redundant calibration to have a larger impact. These results suggest that redundant calibration can indeed mitigate some level of model incompleteness error.
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