Development of Efficient Black Hole Spectroscopy and a Desktop Cluster for Detecting Compact Binary Mergers

开发高效黑洞光谱和用于检测紧凑二元合并的桌面集群

• 批准号: 2412341
• 负责人: Collin Capano
• 金额: $ 15.66万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2412341&HistoricalAwards=false
• 关键词: Development; Efficient; Black; Hole; Spectroscopy

This award supports two projects. The first is to develop methods for testing Einstein's theory of relativity in one of the most extreme environments in the universe: near the horizon of a black hole. Einstein's theory predicts that gravitational waves emitted by black holes should consist of specific frequencies, similar to how a chorus consists of multiple singers signing at different pitches. Gravitational waves are detectable here on Earth with NSF's LIGO detector. This project will use LIGO data to determine if the chorus of gravitational waves emitted by a black hole is exactly as Einstein predicted, or if the black hole "sings" an unexpected tune. Such tests may lead to new discoveries in physics, giving us a better understanding of the fundamental workings of the universe. The second component is to develop a network of Apple Silicon computers to search for new gravitational-waves in LIGO data. Such a network has the potential to make searching for new signals substantially faster and at very low cost. This will make it easier for lesser-resourced universities and undergraduate-focused colleges to directly contribute to gravitational-wave astronomy, broadening the appeal and access to fundamental STEM research in the US. The award provides support for students, who will gain widely-applicable data science skills that are of great national need.This award supports the development of an open-source, Python-based transdimensional Markov-chain Monte Carlo sampler that will naturally identify the set of observable quasi-normal modes emitted by a black hole that is formed in binary black hole mergers. This will be applied to new gravitational-wave detections. Key science questions to be addressed include: are overtones of the dominant mode observable at merger, and if so, which ones? Are other sub-dominant modes observable? If more than one mode is observable, are they consistent with general relativity? In order to answer such questions (and to do any science with gravitational waves), candidate signals must first be identified. Currently this is done by performing a matched-filter search using large numbers of CPUs on data-center clusters. Previous efforts to utilize GPUs have been hampered by the need to transfer data between the CPU and GPU. New "Systems on a Chip" (SoC) such as the Apple Silicon processors side-step this issue, as memory is shared between the CPU and GPU cores. This award will fund the construction, software development, and testing of a cluster of SoC processors. The goal is to perform searches significantly faster and for much lower cost than what is currently done.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项支持两个项目。首先是开发在宇宙中最极端环境之一中测试爱因斯坦相对论的方法：靠近黑洞的地平线。爱因斯坦的理论预测，黑洞发出的引力波应由特定频率组成，类似于合唱的方式，包括在不同音调下签名的多个歌手。通过NSF的Ligo检测器，可以在地球上检测到重力波。该项目将使用Ligo数据来确定黑洞发出的引力波的合唱是否正如爱因斯坦所预测的，还是黑洞“唱歌”了意外的曲调。这样的测试可能会导致物理学的新发现，从而使我们对宇宙的基本运作有了更好的了解。第二个组件是开发一个苹果硅计算机网络，以在Ligo数据中搜索新的重力波。这样的网络有可能使搜索新信号的速度更快，成本非常低。这将使较少资源的大学和以本科大学为中心的大学更容易直接促进引力波天文学，从而扩大了美国的吸引力和获得基本的STEM研究。该奖项为学生提供了支持，他们将获得广泛的国家需求数据科学技能。该奖项支持开发开源的，位于Python的开源的跨二维Markov-Chain Monte Carlo Sampler，自然会识别出可观察到的Quasi-Normoral模式，该模式由Black Binaly Blachel Blackhole Blackhole Mermer中形成Black Black Mers in Balliony Blacker Merser中。这将应用于新的重力波检测。要解决的关键科学问题包括：合并时可观察到的主要模式的泛音，如果是，哪个？其他次级主导模式是否可以观察到？如果可以观察到多种模式，它们是否与一般相对论一致？为了回答此类问题（并使用引力波进行任何科学），必须首先确定候选信号。当前，这是通过使用数据中心簇上的大量CPU进行匹配的过滤器搜索来完成的。需要在CPU和GPU之间传输数据的需要阻碍了以前利用GPU的努力。新的“芯片上的系统”（SOC），例如Apple Silicon处理器旁边，因为CPU和GPU内核之间共享内存。该奖项将资助一组SOC处理器的构建，软件开发和测试。目的是要比目前所做的更快地进行搜索速度，并以低得多的成本进行搜索。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响标准，被认为值得通过评估来获得支持。