SPAM：高分辨率、低频无线电干涉观测的数据处理方法
SPAM: A data reduction recipe for high-resolution, low-frequency radio-interferometric observations
摘要：
亚GHz射频的高分辨率天文成像已有15年多的历史，VLA为74和330 MHz，GMRT为150、240、330和610 MHz。最近的发展包括VLA和GMRT的宽带升级，以及基于孔径阵列的多光束望远镜LOFAR的调试。这些望远镜的一个共同特点是，必须对它们的宽视场内外的许多可探测源进行去卷积。这由于无线电信号路径中的增益变化而变得复杂，该增益变化取决于观看方向。一个这样的例子是由于电离层的相位误差。在这里，我将讨论SPAM的内部工作原理，这是一组Python中基于AIPS的数据简化脚本，包括方向相关的校准和成像。自2008年第一个版本以来，SPAM已经以各种频率应用于许多GMRT数据集。许多宝贵的经验教训，并转化为各种垃圾邮件软件的修改。如今，半自动的SPAM数据缩减方法可以应用于几乎任何GMRT数据集，产生与手动缩减结果相当（或通常更好）的高质量连续图像。目前，SPAM正在从AIPS迁移到CASA，并扩展到处理宽带。其目的是为甚低频带系统和即将推出的宽带GMRT的用户提供必要的数据简化工具。
英文摘要：
High-resolution astronomical imaging at sub-GHz radio frequencies has been available for more than 15 years, with the VLA at 74 and 330 MHz, and the GMRT at 150, 240, 330 and 610 MHz. Recent developments include wide-bandwidth upgrades for VLA and GMRT, and commissioning of the aperture-array-based, multi-beam telescope LOFAR. A common feature of these telescopes is the necessity to deconvolve the very many detectable sources within their wide fields-of-view and beyond. This is complicated by gain variations in the radio signal path that depend on viewing direction. One such example is phase errors due to the ionosphere. Here I discuss the inner workings of SPAM, a set of AIPS-based data reduction scripts in Python that includes direction-dependent calibration and imaging. Since its first version in 2008, SPAM has been applied to many GMRT data sets at various frequencies. Many valuable lessons were learned, and translated into various SPAM software modifications. Nowadays, semi-automated SPAM data reduction recipes can be applied to almost any GMRT data set, yielding good quality continuum images comparable with (or often better than) hand-reduced results. SPAM is currently being migrated from AIPS to CASA with an extension to handle wide bandwidths. This is aimed at providing users of the VLA low-band system and the upcoming wide-bandwidth GMRT with the necessary data reduction tools.
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