Collaborative Research: CDS&E: Investigating a Self-Assembling Data Paradigm for Detector Arrays

合作研究：CDS

• 批准号: 1419240
• 负责人: Lucy Fortson
• 金额: $ 14.29万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-15 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1419240&HistoricalAwards=false
• 关键词: Collaborative; Research; CDS&E; Investigating; Self

A host of problems in scientific research, security, and commerce involve events registered by many devices in multiple locations. The result is fragmented information that must be gathered and built into a coherent whole. In addition, these events may come in rapid succession. When the event rate is high and the number of fragments large, the problem comes to resemble that of assembling tens, hundreds or even thousands of puzzle pieces that are continually being dumped into a common container. Further, puzzle pieces can become damaged or lost, introducing errors into the puzzle assembly process. These challenges are well-studied in the field of computational (nanoscale) self-assembly, which models processes such as the growth of crystals from organic molecules in solution. This project adapts computational self-assembly models to create a new paradigm that treats pieces of information from multiple sensors like molecules randomly meeting and assembling in solution. The result is a dynamic, fluid database of information chunks that evolve over time to form complete, accurate associations. This approach is applied to assemble data from the telescope arrays of very-high-energy gamma-ray observatories. A successful proof of concept in this domain is of interest to more than high-energy astrophysicists. The methods developed here are relevant to high data-volume experiments in other areas of physics and may have further applications to data transport and mining problems in the economic and security sectors. This radically different method of fault-tolerant association of information from distributed sensors requires a proof-of-concept study, which will take place over a two-year period. The chosen test case is scientific. Very-high-energy gamma rays and cosmic rays initiate showers of charged particles in Earth's atmosphere, which in turn produce light due to an effect known as Cherenkov radiation. Arrays of atmospheric Cherenkov telescopes sample the light from a shower from multiple directions in order to more accurately infer the origin and energy of a given gamma ray. Assembling data from these telescopes into a description of a single gamma- or cosmic-ray shower (event-building) is typically done only once. Since revisiting the event-building process is impractical for a large (up to 100 petabytes per year) volume of data, errors become frozen into the data archive. This problem is addressed by the algorithmic self-assembly paradigm. Real and simulated data from the operating gamma-ray observatory VERITAS and simulated data from a planned next-generation observatory, the Cherenkov Telescope Array (CTA), are used to develop the concept and iteratively design, prototype, and test simple implementations for these instruments. Novel signal processing techniques will be exploited to rapidly extract information used in the association process. A series of use-case-dependent benchmarks are used to assess the performance. CTA's size, roughly 100 telescopes distributed over a square kilometer, and high (30 gigabytes per second) data rates make it a particularly apt test case and a successful proof of concept could lead to adoption of this model by CTA.

科学研究，安全和商业领域的许多问题都涉及许多设备在多个位置注册的事件。结果是零散的信息，必须收集并将其内置到一个连贯的整体中。此外，这些事件可能会迅速连续。当事件速率较高并且碎片数量较大时，问题的出现类似于组装数十个，数百甚至数千个拼图零件的问题，这些拼图不断被倒入一个共同的容器中。此外，拼图可能会损坏或丢失，从而将错误引入拼图组装过程中。这些挑战在计算（纳米级）自组装领域进行了充分研究，该挑战模拟了溶液中有机分子的晶体的生长。该项目适应了计算自组装模型，以创建一个新的范式，该模型可以处理来自多个传感器的信息，例如分子随机相遇和组装解决方案。结果是信息块的动态流体数据库，随着时间的流逝而发展，形成完整，准确的关联。这种方法用于组装非常高能量伽玛射线观测器的望远镜阵列的数据。高能天体物理学家不仅仅是该领域中成功的概念证明。此处开发的方法与其他物理学领域的高数据体积实验有关，并且可能进一步应用于经济和安全部门的数据运输和采矿问题。从分布式传感器中，这种易于故障的信息关联的方法不同，需要进行概念验证研究，该研究将在两年内进行。选择的测试案例是科学的。非常高的能量伽玛射线和宇宙射线会引发地球大气中带电颗粒的阵雨，进而由于称为Cherenkov辐射的效果而产生光。大气Cherenkov望远镜的阵列从多个方向采样了淋浴中的光，以便更准确地推断给定γ射线的起源和能量。将这些望远镜的数据组装成单个γ-或宇宙射线淋浴（事件建造）的描述通常仅完成一次。由于重新审视事件构建过程对于大量数据（每年最多100 pb）的数据是不切实际的，因此错误被冻结到数据存档中。算法自组装范式解决了这个问题。来自计划的下一代天文台（Cherenkov望远镜阵列（CTA））来自操作伽马射线天文台Veritas的真实和模拟数据，用于开发概念和迭代设计，原型，并测试这些工具的简单实现。新型的信号处理技术将被利用以快速提取关联过程中使用的信息。一系列用用例依赖性的基准用于评估性能。 CTA的大小约为100台在平方公里的望远镜，高（每秒30千兆字节）的数据速率使其成为一个特别合适的测试案例，成功的概念证明可能会导致CTA采用该模型。