改进移动射频干扰源的建模及其对标记策略的影响
Improved Modeling for Moving Sources of Radio Frequency Interference and Impact on Flagging Strategies
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中文总结 AI 辅助
针对移动射频干扰源，提出解析模型并验证其行为，通过注入实验比较标记策略，发现全频率标记最有效，但需进一步直接建模与扣除。
中文摘要 AI 辅助
射频干扰（RFI）是针对再电离纪元红移21厘米信号的低频射电实验所面临的主要挑战。许多重要的RFI源，包括飞机和卫星，在观测期间会在天空中移动。对于这类移动源，在有限的相关器积分时间内对可见度进行平均，会在干涉测量uv平面上产生一种独特的sinc状图案，导致RFI在某些基线上显得明亮，而在其他基线上则被强烈抑制。在此，我们针对这一效应开发了一个解析模型，并针对真实的MWA观测和数值模拟验证了其定性行为。随后，将受控的RFI注入到干净的MWA数据中，以在EoR功率谱恢复的背景下比较几种标记处理方法。这些处理方法探讨了每基线标记与基线聚合标记之间的权衡，前者可能会遗漏在积分效应导致强烈抑制的基线上微弱但可能不可忽略的污染，而后者虽然能去除更多微弱的残余污染，但会减少可用的uv模式总数。我们发现，首选策略取决于RFI的亮度和占用率，但对于明亮或频繁的事件，在受污染的时间步长上对所有频率进行标记最能持续地最小化多余功率。然而，所测试的标记处理方法均无法完全恢复未受污染的参考频谱，这促使未来工作直接建模和减去移动RFI源。
英文摘要
Radio frequency interference (RFI) is a major challenge for low-frequency radio experiments targeting the redshifted 21-cm signal from the Epoch of Reionization. Many important RFI sources, including aircraft and satellites, move across the sky during an observation. The averaging of visibilities for such moving sources over a finite correlator integration produces a distinct sinc-like pattern in the interferometric uv-plane, causing the RFI to appear bright on some baselines and strongly suppressed on others. Here, we develop an analytical model for this effect and validate its qualitative behavior against real MWA observations and numerical simulations. Controlled RFI injections into clean MWA data are then used to compare several flagging treatments in the context of EoR power spectrum recovery. These treatments explore the trade-off between per-baseline flagging, which can miss weak but potentially non-negligible contamination on baselines where the integration effect leads to strong suppression, and baseline-aggregated flagging, which removes more faint residual contamination but reduces the total number of usable uv-modes. We find that the preferred strategy depends on RFI brightness and occupancy, but that flagging all frequencies at contaminated time-steps most consistently minimizes excess power for bright or frequent events. However, none of the tested flagging treatments fully recovers the uncontaminated reference spectrum, motivating future work on direct modeling and subtraction of moving RFI sources.
发表机构
- Brown University（布朗大学）
- McGill University（麦吉尔大学）
机构由 AI 辅助整理，请以论文原文为准。
