论文标题
APERTIF无线电瞬态系统的实时RFI缓解
Real-Time RFI Mitigation for the Apertif Radio Transient System
论文作者
论文摘要
当前和即将到来的射电望远镜的设计越来越敏感性,以检测天体物理起源的新的和神秘的无线电来源。尽管这种提高的敏感性提高了发现的可能性,但它也使这些工具更容易受到射频干扰(RFI)的有害影响。 RFI构成的挑战被现代射电望远镜所获得的高数据率加剧了,这需要实时处理才能跟上数据。此外,高数据速率不允许在高分辨率下永久存储观测值。因此,离线RFI缓解已经不可能了。实时需求使RFI缓解措施更加具有挑战性,因为一方面,用于缓解的技术需要快速和简单,另一方面,它们还需要足够强大的功能才能应对数据的部分视图。 Apertif无线电瞬态系统(ARTS)是Westerbork合成射电望远镜(WSRT)的实时,时间域,瞬态检测工具,每秒处理73 GB的数据。即使使用深度学习分类器,艺术管道也需要最先进的实时RFI缓解措施以减少假阳性检测的数量。我们应对这一挑战的解决方案是RFIM,这是一个高性能,开源,调整和可扩展的RFI缓解库。该库的目的是为用户提供RFI缓解例程,该程序旨在在多核加速器(例如图形处理单元)实时运行,并且可以高度调整以实现对不同硬件平台和科学用例的代码和性能可移植性。艺术的结果表明,我们可以实现实时的RFI缓解措施,对搜索管道的总执行时间产生最小的影响,并大大减少了假阳性的数量。
Current and upcoming radio telescopes are being designed with increasing sensitivity to detect new and mysterious radio sources of astrophysical origin. While this increased sensitivity improves the likelihood of discoveries, it also makes these instruments more susceptible to the deleterious effects of Radio Frequency Interference (RFI). The challenge posed by RFI is exacerbated by the high data-rates achieved by modern radio telescopes, which require real-time processing to keep up with the data. Furthermore, the high data-rates do not allow for permanent storage of observations at high resolution. Offline RFI mitigation is therefore not possible anymore. The real-time requirement makes RFI mitigation even more challenging because, on one side, the techniques used for mitigation need to be fast and simple, and on the other side they also need to be robust enough to cope with just a partial view of the data. The Apertif Radio Transient System (ARTS) is the real-time, time-domain, transient detection instrument of the Westerbork Synthesis Radio Telescope (WSRT), processing 73 Gb of data per second. Even with a deep learning classifier, the ARTS pipeline requires state-of-the-art real-time RFI mitigation to reduce the number of false-positive detections. Our solution to this challenge is RFIm, a high-performance, open-source, tuned, and extensible RFI mitigation library. The goal of this library is to provide users with RFI mitigation routines that are designed to run in real-time on many-core accelerators, such as Graphics Processing Units, and that can be highly-tuned to achieve code and performance portability to different hardware platforms and scientific use-cases. Results on the ARTS show that we can achieve real-time RFI mitigation, with a minimal impact on the total execution time of the search pipeline, and considerably reduce the number of false-positives.