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arXiv 1209.4935astro-ph.IMcs.DC
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自适应实时成像合成望远镜

Adaptive Real Time Imaging Synthesis Telescopes

Melvyn Wright

摘要:

数字革命正在将天文学从数据匮乏转变为数据淹没的科学。阿塔卡马大型毫米波阵列(ALMA)、大型综合巡天望远镜(LSST)和平方公里阵列(SKA)等仪器将以PB为单位测量其累积的数据。产生大量数据的能力必须与处理这些数据并产生有意义结果的计算能力相匹配。除了处理巨大的数据速率,我们需要自适应校准和波束形成来处理大气波动和射频干扰,并提供一个用户环境,使专家和非专家用户都可以使用大型望远镜阵列的全部功率。延迟的校准和分析限制了可以做的科学。为了最好地利用望远镜和人力资源,我们必须减轻数据简化的负担。我们的仪器包括一个灵活的相关器,波束形成器和成像器与数字信号处理紧密结合的计算集群。该仪器将非常容易为科学家,工程师和学生提供实时处理算法的研究和开发,并将利用来自工程,计算和天文学背景的有才华和创新的学生和访问科学家。自适应实时成像将通过向观测者提供实时反馈来改变射电天文学。使用天空亮度分布的模型接近实时地进行数据的校准。导出的校准参数被反馈到成像器和波束形成器中。所成像的区域用于更新和改进先验模型,其在观测完成时成为最终校准图像。

英文摘要:

The digital revolution is transforming astronomy from a data-starved to a data-submerged science. Instruments such as the Atacama Large Millimeter Array (ALMA), the Large Synoptic Survey Telescope (LSST), and the Square Kilometer Array (SKA) will measure their accumulated data in petabytes. The capacity to produce enormous volumes of data must be matched with the computing power to process that data and produce meaningful results. In addition to handling huge data rates, we need adaptive calibration and beamforming to handle atmospheric fluctuations and radio frequency interference, and to provide a user environment which makes the full power of large telescope arrays accessible to both expert and non-expert users. Delayed calibration and analysis limit the science which can be done. To make the best use of both telescope and human resources we must reduce the burden of data reduction. Our instrumentation comprises of a flexible correlator, beam former and imager with digital signal processing closely coupled with a computing cluster. This instrumentation will be highly accessible to scientists, engineers, and students for research and development of real-time processing algorithms, and will tap into the pool of talented and innovative students and visiting scientists from engineering, computing, and astronomy backgrounds. Adaptive real-time imaging will transform radio astronomy by providing real-time feedback to observers. Calibration of the data is made in close to real time using a model of the sky brightness distribution. The derived calibration parameters are fed back into the imagers and beam formers. The regions imaged are used to update and improve the a-priori model, which becomes the final calibrated image by the time the observations are complete.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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