Collaborative Research: Optimized frequency-domain analysis for astronomical time series

合作研究:天文时间序列的优化频域分析

基本信息

  • 批准号:
    2307978
  • 负责人:
  • 金额:
    $ 58.74万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-08-15 至 2026-07-31
  • 项目状态:
    未结题

项目摘要

Earth-like planet searches are underway which can measure the motion of small planets around distant stars. However, investments in these instruments will not meet their full potential without advances in computer software. Through a three year award, a team led by the Universities of Delaware and Chicago will adapt a time domain data analysis tool previously used for health science and solar science for astronomy. Developing new analysis methods will save telescope time that costs tens of thousands of dollars per night by reducing the number of observations needed and increasing telescope efficiency. Students will be involved in the planet searches. The Team's goals are to involve physics and astronomy majors with all levels of academic preparation in planet searches and to create a supportive environment in which students can seek help from a faculty, scholars, and each other. While the Lomb-Scargle periodogram is foundational to astronomy, it has a significant short-coming: its variance does not decrease as more data are acquired. Statisticians have a 60-year history of developing variance-suppressing power spectrum estimators, but most are not used in astronomy because they are formulated for time series with uniform observing cadence and without seasonal or daily gaps. The team will mitigate the false-positive and bias problems of the Lomb-Scargle periodogram by adapting the multitaper power spectrum estimator for ground-based astronomical time series. They will present multitaper Magnitude-Squared Coherence (MSC) as a diagnostic of oscillations that manifest jointly in two or more observables. MSC between activity indicators and radial velocity is a powerful tool for identifying stellar rotation and harmonics, which have been responsible for many false positive planet detections. They will introduce a non-multitaper version of complex demodulation for ground-based time series. Complex demodulation, a local Fourier decomposition that reconstructs the long-period component of two coupled oscillations, can distinguish activity-modulated stellar signals from non-modulated planetary signals and recover full-phase rotation signals from observations of pulsating stars. This award funds development of the Oscillation Recognition and CAtegorization Software (ORCAS) package, which will contain python and Julia implementations of our frequency-domain methods. ORCAS will be sustainably hosted on bitbucket and registered with the Astrophysical Source Code Library. The methods developed can be applied to planet hunting, seismology, paleoclimatology, genetics, laser Doppler velocimetry, and the Rubin Observatory Legacy Survey of Space and Time.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.
正在进行类似地球的行星搜索,可以测量遥远恒星周围的小行星的运动。但是,如果没有计算机软件的进步,对这些乐器的投资将无法满足其全部潜力。通过三年奖,由特拉华大学和芝加哥大学领导的团队将适应以前用于天文学的健康科学和太阳科学的时域数据分析工具。开发新的分析方法将节省望远镜的时间,该时间通过减少所需的观察次数并提高望远镜效率,每晚花费数万美元。学生将参与地球搜索。该团队的目标是在星球搜索中涉及各个层次的学术准备,并创造一个支持性的环境,并在其中学生可以在其中寻求教师,学者和彼此的帮助。虽然Lomb-Scargle期间图是天文学的基础,但它具有显着的缩写:随着获取更多数据的获取,其方差不会降低。统计学家有60年的历史,可以发展为差异的功率谱估计器,但大多数人不在天文学中使用,因为它们是为时间序列配制的,具有均匀的观察节奏,没有季节性或每日差距。该团队将通过调整基于地面的天文学时间序列的多底功率频谱估计器来减轻Lomb-Scargle期间图的错误阳性和偏差问题。他们将介绍多级级方方相干性(MSC)作为振荡的诊断,这些振荡在两个或多个可观察物中共同表现出来。活动指标和径向速度之间的MSC是识别恒星旋转和谐波的强大工具,这些工具已导致许多假阳性行星检测。他们将针对地面时间序列引入非媒介版的复杂解调。复杂的解调是一种局部傅立叶分解,可重建两个耦合振荡的长周期成分,可以将活性调节的恒星信号与非调节的行星信号区分开,并恢复全相旋转信号与脉动恒星的观测值。该奖励资金为振荡识别和分类软件(ORCAS)软件包的开发提供了资金,该软件包含我们的频域方法的Python和Julia实现。 Orcas将可持续地托管在Bitbucket上,并在天体物理源代码库中注册。开发的方法可以应用于狩猎行星,地震学,古气候学,遗传学,激光多普勒赛车仪以及鲁宾天文台的时空遗产调查。该奖项反映了NSF的法定任务,并通过使用基金会的知识优点和广泛的影响来评估NSF的法定任务。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

暂无数据

数据更新时间:2024-06-01

Sarah Dodson-Robin...的其他基金

CAREER: Giant Planets in Dusty Disks
职业:尘埃盘中的巨行星
  • 批准号:
    1520101
    1520101
  • 财政年份:
    2014
  • 资助金额:
    $ 58.74万
    $ 58.74万
  • 项目类别:
    Continuing Grant
    Continuing Grant
CAREER: Giant Planets in Dusty Disks
职业:尘埃盘中的巨行星
  • 批准号:
    1055910
    1055910
  • 财政年份:
    2011
  • 资助金额:
    $ 58.74万
    $ 58.74万
  • 项目类别:
    Continuing Grant
    Continuing Grant

相似国自然基金

车联网中基于多智能体系统的协同优化机制研究
  • 批准号:
    62302062
  • 批准年份:
    2023
  • 资助金额:
    30.00 万元
  • 项目类别:
    青年科学基金项目
基于多算法组合协作的城市空中交通建模分析与优化管控研究
  • 批准号:
    72301278
  • 批准年份:
    2023
  • 资助金额:
    30.00 万元
  • 项目类别:
    青年科学基金项目
面向边缘智能的无线网络协作计算与资源优化研究
  • 批准号:
    62301307
  • 批准年份:
    2023
  • 资助金额:
    30 万元
  • 项目类别:
    青年科学基金项目
不确定环境下多式联运系统优化与协作机制研究
  • 批准号:
    72371169
  • 批准年份:
    2023
  • 资助金额:
    41 万元
  • 项目类别:
    面上项目
多冗余度机器人的跨层协作神经动力学优化策略研究
  • 批准号:
    62373157
  • 批准年份:
    2023
  • 资助金额:
    50 万元
  • 项目类别:
    面上项目

相似海外基金

Collaborative Research: Optimized frequency-domain analysis for astronomical time series
合作研究:天文时间序列的优化频域分析
  • 批准号:
    2307979
    2307979
  • 财政年份:
    2023
  • 资助金额:
    $ 58.74万
    $ 58.74万
  • 项目类别:
    Standard Grant
    Standard Grant
Collaborative Research: MoDL: Graph-Optimized Cellular Connectionism via Artificial Neural Networks for Data-Driven Modeling and Optimization of Complex Systems
合作研究:MoDL:通过人工神经网络进行图优化的细胞连接,用于复杂系统的数据驱动建模和优化
  • 批准号:
    2234032
    2234032
  • 财政年份:
    2023
  • 资助金额:
    $ 58.74万
    $ 58.74万
  • 项目类别:
    Standard Grant
    Standard Grant
Collaborative Research: MoDL: Graph-Optimized Cellular Connectionism via Artificial Neural Networks for Data-Driven Modeling and Optimization of Complex Systems
合作研究:MoDL:通过人工神经网络进行图优化的细胞连接,用于复杂系统的数据驱动建模和优化
  • 批准号:
    2234031
    2234031
  • 财政年份:
    2023
  • 资助金额:
    $ 58.74万
    $ 58.74万
  • 项目类别:
    Standard Grant
    Standard Grant
Collaborative Research: CNS Core: Medium: Rethinking Multi-User VR - Jointly Optimized Representation, Caching and Transport
合作研究:CNS 核心:媒介:重新思考多用户 VR - 联合优化表示、缓存和传输
  • 批准号:
    2212200
    2212200
  • 财政年份:
    2022
  • 资助金额:
    $ 58.74万
    $ 58.74万
  • 项目类别:
    Continuing Grant
    Continuing Grant
Collaborative Research: CNS Core: Medium: Rethinking Multi-User VR - Jointly Optimized Representation, Caching and Transport
合作研究:CNS 核心:媒介:重新思考多用户 VR - 联合优化表示、缓存和传输
  • 批准号:
    2212201
    2212201
  • 财政年份:
    2022
  • 资助金额:
    $ 58.74万
    $ 58.74万
  • 项目类别:
    Continuing Grant
    Continuing Grant