Reproducible analysis frameworks in Lattice Field Theory and STFC-enabled computational research in Wales

威尔士格子场论和 STFC 支持的计算研究中的可重复分析框架

基本信息

  • 批准号:
    EP/V052489/1
  • 负责人:
  • 金额:
    $ 78.19万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Fellowship
  • 财政年份:
    2021
  • 资助国家:
    英国
  • 起止时间:
    2021 至 无数据
  • 项目状态:
    未结题

项目摘要

Lattice Field Theory is a technique used to study many models of physics, most notable Quantum ChromoDynamics, the theory of the strong interaction which holds the nuclei of atoms together. Calculations at the very smallest scales-many thousands of times smaller than the size of an atom-can make predictions for properties of matter that can be observed at experiments such as those at the Large Hadron Collider in Switzerland. Even though (and because) the length scales considered are so small, these calculations require vast amounts of computing power, using some of the largest supercomputers in the world. This produces large amounts of data, which must be carefully analysed by researchers to extract the properties being studied and make predictions. As the size of computers and the amount of data that can be produced grows, the likelihood of making errors in this process increases. To reduce the chance of this happening, this project will develop a set of tools to automate these analyses, so that they can always run consistently, and their results can be re-run and checked by anyone. The project will work with researchers to adapt their existing software to make use of these tools, and train them to do the same themselves for new software.The Solar System Physics group at Aberystwyth University studies our Sun and its interactions with the rest of our Solar System. This includes studying phenomena like solar storms, which have the potential to significantly disrupt electronics and telecommunications on Earth; better understanding these phenomena allows us to predict when they may occur and take steps to protect critical infrastructure. These computations make use of programs written in a computer language called IDL, which requires paying money to use, with the amount you pay determining the number of processing units you can use at once, and so how fast your computations will be performed. While this was a good choice when the programs were written, the amount of data needing to be analysed has grown to the point that waiting for IDL to run an analysis is causing a bottleneck in research. This project will support researchers at Aberystwyth in adapting the programs to instead use the commonly-used Python programming language instead. This will let them use many more processing units simultaneously, giving answers more quickly. This will also mean that other researchers who haven't paid for IDL will be able to verify the results.The Laser Interferometer Gravitational-Wave Observatory (LIGO) is an international collaboration to observe gravitational waves, which are ripples in spacetime itself, predicted by Einstein's theory of General Relativity. When very heavy objects in space, like black holes, collide with each other, their acceleration is strong enough to create gravitational waves that we can detect on Earth. These waves cause lengths and distances to change ever so slightly, which can be measured using devices called interferometers. In order to understand what kind of astronomical event created a gravitational wave, the readings from the interferometers must be compared with the results of simulations. At first, LIGO could only sense the largest of large events, the collisions of large black holes. Researchers are working to make LIGO more sensitive, to be able to detect collisions between smaller objects. This means that it observes a lot more gravitational waves than previously, so there is a lot more data to analyse. This project will work with the LIGO team at the Gravity Exploration Institute at Cardiff University to redevelop their software so that each analysis takes less time, which will allow LIGO to keep up with the increasing number of gravitational wave events that they observe.
晶格场论是一种用于研究许多物理模型的技术,最著名的是量子色动学,将原子核保持在一起的强相互作用理论。在最小尺度上的计算比原子的尺寸小几千倍可以预测物质的性质,这些性质可以在瑞士大型强子对撞机等实验中观察到。即使(也因为)所考虑的长度尺度如此之小,这些计算需要大量的计算能力,使用世界上最大的超级计算机。这产生了大量的数据,研究人员必须仔细分析这些数据,以提取正在研究的属性并进行预测。随着计算机的规模和可以产生的数据量的增长,在这个过程中出错的可能性也会增加。为了减少这种情况发生的机会,该项目将开发一套工具来自动化这些分析,以便它们可以始终一致地运行,并且任何人都可以重新运行和检查其结果。该项目将与研究人员合作,调整他们现有的软件,使其利用这些工具,并训练他们自己做同样的新软件。阿伯里斯特威斯大学的太阳系物理小组研究我们的太阳及其与太阳系其他部分的相互作用。这包括研究太阳风暴等现象,这些现象有可能严重破坏地球上的电子和电信;更好地了解这些现象使我们能够预测它们何时可能发生,并采取措施保护关键基础设施。这些计算使用了一种名为IDL的计算机语言编写的程序,这需要付费才能使用,您支付的金额决定了您可以一次使用的处理单元的数量,以及您的计算将以多快的速度执行。虽然在编写程序时这是一个很好的选择,但需要分析的数据量已经增长到了等待IDL运行分析的程度,这导致了研究的瓶颈。该项目将支持阿伯里斯特威斯的研究人员调整程序,而不是使用常用的Python编程语言。这将使他们能够同时使用更多的处理单元,更快地给出答案。这也意味着其他没有为IDL付费的研究人员将能够验证结果。激光干涉引力波天文台(LIGO)是一个国际合作项目,旨在观测引力波,引力波是时空本身的涟漪,由爱因斯坦的广义相对论预测。当太空中非常重的物体,如黑洞,相互碰撞时,它们的加速度足以产生我们在地球上可以检测到的引力波。这些波导致长度和距离发生非常微小的变化,这可以使用称为干涉仪的设备进行测量。为了了解什么样的天文事件产生了引力波,必须将干涉仪的读数与模拟结果进行比较。起初,LIGO只能感知到最大的大型事件,即大型黑洞的碰撞。研究人员正在努力使LIGO更敏感,能够检测较小物体之间的碰撞。这意味着它观测到的引力波比以前多得多,因此有更多的数据需要分析。该项目将与卡迪夫大学重力探测研究所的LIGO团队合作,重新开发他们的软件,以便每次分析所需的时间更少,这将使LIGO能够跟上他们观测到的越来越多的引力波事件。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Investigating the conformal behavior of SU(2) with one adjoint Dirac flavor
  • DOI:
    10.1103/physrevd.104.074519
  • 发表时间:
    2021-03
  • 期刊:
  • 影响因子:
    5
  • 作者:
    A. Athenodorou;E. Bennett;G. Bergner;B. Lucini
  • 通讯作者:
    A. Athenodorou;E. Bennett;G. Bergner;B. Lucini
New lattice results for SU(2) gauge theory with one adjoint Dirac flavor
Open Science in Lattice Gauge Theory community
格子规范理论社区中的开放科学
  • DOI:
    10.22323/1.430.0341
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Athenodorou A
  • 通讯作者:
    Athenodorou A
Status of reproducibility and open science in hep-lat in 2021
2021 年 hep-lat 的再现性和开放科学现状
  • DOI:
    10.22323/1.430.0337
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Bennett E
  • 通讯作者:
    Bennett E
Color dependence of the topological susceptibility in Yang-Mills theories
  • DOI:
    10.1016/j.physletb.2022.137504
  • 发表时间:
    2022-05
  • 期刊:
  • 影响因子:
    4.4
  • 作者:
    E. Bennett;D. Hong;Jong-Wan Lee;C.-J. David Lin;B. Lucini;M. Piai;Davide Vadacchino
  • 通讯作者:
    E. Bennett;D. Hong;Jong-Wan Lee;C.-J. David Lin;B. Lucini;M. Piai;Davide Vadacchino
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Ed Bennett其他文献

Real-time epidemiological modelling during the COVID-19 emergency in Wales
威尔士 COVID-19 紧急情况期间的实时流行病学建模
  • DOI:
    10.1101/2023.08.02.23293519
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    2.2
  • 作者:
    M. Gravenor;M. Dawson;Ed Bennett;Ben Thorpe;Carla White;Alma A. M. Rahat;D. Archambault;Noemi Picco;Gibin G. Powathil;B. Lucini
  • 通讯作者:
    B. Lucini
素粒子質量起源の理論探索
基本粒子质量起源的理论探索
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    青木保道;青山龍美;Ed Bennett;倉知昌史;益川敏英;三浦光太郎;長井敬一;大木洋;Enrico Rinaldi;柴田章博;山脇幸一;山崎剛 for LatKMI Collaboration
  • 通讯作者:
    山崎剛 for LatKMI Collaboration

Ed Bennett的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

相似国自然基金

Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
  • 批准号:
  • 批准年份:
    2024
  • 资助金额:
    万元
  • 项目类别:
    合作创新研究团队
Intelligent Patent Analysis for Optimized Technology Stack Selection:Blockchain BusinessRegistry Case Demonstration
  • 批准号:
  • 批准年份:
    2024
  • 资助金额:
    万元
  • 项目类别:
    外国学者研究基金项目
利用全基因组关联分析和QTL-seq发掘花生白绢病抗性分子标记
  • 批准号:
    31971981
  • 批准年份:
    2019
  • 资助金额:
    58.0 万元
  • 项目类别:
    面上项目
基于SERS纳米标签和光子晶体的单细胞Western Blot定量分析技术研究
  • 批准号:
    31900571
  • 批准年份:
    2019
  • 资助金额:
    24.0 万元
  • 项目类别:
    青年科学基金项目
利用多个实验群体解析猪保幼带形成及其自然消褪的遗传机制
  • 批准号:
    31972542
  • 批准年份:
    2019
  • 资助金额:
    57.0 万元
  • 项目类别:
    面上项目
基于Meta-analysis的新疆棉花灌水增产模型研究
  • 批准号:
    41601604
  • 批准年份:
    2016
  • 资助金额:
    22.0 万元
  • 项目类别:
    青年科学基金项目
基于个体分析的投影式非线性非负张量分解在高维非结构化数据模式分析中的研究
  • 批准号:
    61502059
  • 批准年份:
    2015
  • 资助金额:
    19.0 万元
  • 项目类别:
    青年科学基金项目
多目标诉求下我国交通节能减排市场导向的政策组合选择研究
  • 批准号:
    71473155
  • 批准年份:
    2014
  • 资助金额:
    60.0 万元
  • 项目类别:
    面上项目
大规模微阵列数据组的meta-analysis方法研究
  • 批准号:
    31100958
  • 批准年份:
    2011
  • 资助金额:
    20.0 万元
  • 项目类别:
    青年科学基金项目
基于物质流分析的中国石油资源流动过程及碳效应研究
  • 批准号:
    41101116
  • 批准年份:
    2011
  • 资助金额:
    23.0 万元
  • 项目类别:
    青年科学基金项目

相似海外基金

Collaborative Research: Frameworks: Scalable Performance and Accuracy analysis for Distributed and Extreme-scale systems (SPADE)
协作研究:框架:分布式和超大规模系统的可扩展性能和准确性分析 (SPADE)
  • 批准号:
    2311707
  • 财政年份:
    2023
  • 资助金额:
    $ 78.19万
  • 项目类别:
    Standard Grant
Collaborative Research: Frameworks: Scalable Performance and Accuracy analysis for Distributed and Extreme-scale systems (SPADE)
协作研究:框架:分布式和超大规模系统的可扩展性能和准确性分析 (SPADE)
  • 批准号:
    2311708
  • 财政年份:
    2023
  • 资助金额:
    $ 78.19万
  • 项目类别:
    Standard Grant
Extension of Innovative Frameworks for Application Analysis in Post-Peta Scale Systems
后 Peta 规模系统应用分析创新框架的扩展
  • 批准号:
    22KK0182
  • 财政年份:
    2023
  • 资助金额:
    $ 78.19万
  • 项目类别:
    Fund for the Promotion of Joint International Research (Fostering Joint International Research (A))
Collaborative Research: Frameworks: Scalable Performance and Accuracy analysis for Distributed and Extreme-scale systems (SPADE)
协作研究:框架:分布式和超大规模系统的可扩展性能和准确性分析 (SPADE)
  • 批准号:
    2311709
  • 财政年份:
    2023
  • 资助金额:
    $ 78.19万
  • 项目类别:
    Standard Grant
Resolving single-cell analysis challenges via data-driven decision frameworks and novel statistical methods
通过数据驱动的决策框架和新颖的统计方法解决单细胞分析挑战
  • 批准号:
    10707308
  • 财政年份:
    2022
  • 资助金额:
    $ 78.19万
  • 项目类别:
CAREER: Future phylogenies: novel computational frameworks for biomolecular sequence analysis involving complex evolutionary origins
职业:未来的系统发育:涉及复杂进化起源的生物分子序列分析的新型计算框架
  • 批准号:
    2144121
  • 财政年份:
    2022
  • 资助金额:
    $ 78.19万
  • 项目类别:
    Continuing Grant
Novel frameworks for explaining unequal access to the health benefits of social ties: a longitudinal analysis in wild chimpanzees
解释社会关系带来的健康益处不平等的新框架:对野生黑猩猩的纵向分析
  • 批准号:
    10512365
  • 财政年份:
    2022
  • 资助金额:
    $ 78.19万
  • 项目类别:
CAREER:Computational Frameworks for Higher-order Graph and Network Data Analysis
职业:高阶图和网络数据分析的计算框架
  • 批准号:
    2045555
  • 财政年份:
    2021
  • 资助金额:
    $ 78.19万
  • 项目类别:
    Continuing Grant
Frameworks: Bayesian Analysis of Nuclear Dynamics
框架:核动力学贝叶斯分析
  • 批准号:
    2004601
  • 财政年份:
    2020
  • 资助金额:
    $ 78.19万
  • 项目类别:
    Continuing Grant
Automated Analysis of Tutorials for Software Frameworks
软件框架教程的自动分析
  • 批准号:
    552275-2020
  • 财政年份:
    2020
  • 资助金额:
    $ 78.19万
  • 项目类别:
    University Undergraduate Student Research Awards
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了