Leading the Next Generation of Data-Driven Discoveries

引领下一代数据驱动的发现

• 批准号: MR/T044136/1
• 负责人: Brooke Simmons
• 金额: $ 155.97万
• 依托单位: Lancaster University
• 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
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT044136%2F1
• 关键词: Leading; Next; Generation; Data; Driven

Extracting accurate information from the modern data flood without sacrificing the ability to discover the unexpected is a core data challenge in many fields. A cross-disciplinary approach presents major opportunities to advance the state of the art in each. This project focuses on bringing together the state of the art in two fields: astrophysics, and humanitarian Earth observation. In astrophysics, the proliferation of data that will be available from upcoming large sky surveys presents significant challenges and opportunities. Accurate classification of this data (for example, whether a galaxy contains a buried, feeding supermassive black hole, or whether a new point of light in a galaxy is a supernova, and what kind) will enable major advancement in our understanding of how the Universe has evolved and will continue to evolve, and how galaxies such as the Milky Way form and grow within it. Following a natural disaster, responders need to know where roads are blocked, where buildings are damaged, and where survivors are sheltering. When satellites look down at the Earth instead of up at the heavens, they capture data that has some similar qualities but many complementary differences to astrophysical images. The driving need, however, is fundamentally the same. For instance, detecting the important changes before and after a storm or earthquake is similar to identifying the type of exploding star newly observed in a distant galaxy. In this example, astrophysical and humanitarian analysis have lessons to teach each other: Astrophysics has advanced methods of adjusting its algorithms to compensate for differences in observing conditions such as atmospheric turbulence, while humanitarian "Earth Observation" algorithms have tested promising methods of identifying critical changes based on only one before-and-after data point. There are many other examples of the potential for symbiosis, and this project will capitalise on this by working on both simultaneously in a cross-pollinating environment.Generally speaking, this research project will develop new tools for efficient, accurate data classification and labelling in these new data regimes where images and other data points arrive rapidly and are of varying quality and origin. Both fields make use of machine learning algorithms and combine machine classification with expert and high-quality crowd data labels. The project will test new techniques developed in each field on the other, using tool-specific expertise and combined domain knowledge to make new discoveries. By extracting insights from each regime and advancing their most effective tools, this work will enable us to understand the changing skies and the changing Earth in a way that provides real benefit (e.g. increased resilience, decreased recovery time, saving of lives) to distressed populations, maximising impact both near and far.The specific scientific topics this project will address cover some of the most pressing humanitarian needs across the globe and some of the most fundamental open questions about the Universe.

从现代数据洪流中提取准确的信息，而不牺牲发现意外的能力，是许多领域的核心数据挑战。一个跨学科的方法提供了重大的机会，以推进国家的艺术在每一个。该项目的重点是汇集两个领域的最新技术：天体物理学和人道主义地球观测。在天体物理学方面，即将进行的大型巡天将提供大量数据，这带来了重大的挑战和机遇。这些数据的准确分类（例如，一个星系是否包含一个被掩埋的、正在进食的超大质量黑洞，或者星系中的一个新光点是否是超新星，以及是哪种）将使我们对宇宙如何进化以及将继续进化的理解取得重大进展，以及银河系等星系如何在其中形成和成长。在自然灾害之后，救援人员需要知道哪里的道路被封锁，哪里的建筑物被损坏，以及幸存者在哪里避难。当卫星向下看地球而不是向上看天空时，它们捕获的数据与天体物理图像有一些相似的质量，但有许多互补的差异。然而，驱动需求基本上是相同的。例如，探测风暴或地震前后的重要变化类似于识别在遥远星系中新观察到的爆炸星星的类型。在这个例子中，天体物理学和人道主义分析可以相互借鉴：天体物理学有先进的方法来调整其算法，以补偿观测条件（如大气湍流）的差异，而人道主义“地球观测”算法已经测试了基于一个前后数据点识别关键变化的有前途的方法。还有许多其他的共生潜力的例子，这个项目将利用这一点，在一个异花授粉的环境中同时研究这两个方面。一般来说，这个研究项目将开发新的工具，在这些新的数据体系中，图像和其他数据点快速到达，质量和来源不同，有效，准确的数据分类和标签。这两个领域都利用机器学习算法，并将联合收割机机器分类与专家和高质量的人群数据标签相结合。该项目将测试每个领域开发的新技术，使用特定工具的专业知识和综合领域知识来进行新的发现。通过从每个政权中提取见解并推进其最有效的工具，这项工作将使我们能够以提供真实的益处的方式了解不断变化的天空和不断变化的地球（如提高复原力、减少恢复时间、拯救生命），最大限度地发挥远近影响。该项目将涉及的具体科学主题涵盖了全球一些最紧迫的人道主义需求。地球仪和一些关于宇宙的最基本的悬而未决的问题。

项目成果

The most luminous, merger-free AGNs show only marginal correlation with bar presence

最明亮、无合并的活动星系核仅与条形存在存在边际相关性

• DOI: 10.1093/mnras/stad966
• 发表时间: 2023
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: Garland I

Supermassive black holes in merger-free galaxies have higher spins which are preferentially aligned with their host galaxy

无合并星系中的超大质量黑洞具有更高的自旋，优先与其宿主星系对齐

• DOI: 10.1093/mnras/stad1795
• 发表时间: 2023
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: Beckmann R

Galaxy zoo: stronger bars facilitate quenching in star-forming galaxies

• DOI: 10.1093/mnras/stab2064
• 发表时间: 2021-07
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: T. Géron;R. Smethurst;C. Lintott;S. Kruk;K. Masters;B. Simmons;D. Stark

Panning for gold, but finding helium: Discovery of the ultra-stripped supernova SN 2019wxt from gravitational-wave follow-up observations

淘金却发现氦：通过引力波后续观测发现超剥离超新星 SN 2019wxt

• DOI: 10.1051/0004-6361/202244751
• 发表时间: 2023
• 期刊: Astronomy & Astrophysics
• 影响因子: 6.5
• 作者: Agudo I

GRB 080928 afterglow imaging and spectro-polarimetry

GRB 080928 余辉成像和分光偏振测量

• DOI: 10.1051/0004-6361/202243786
• 发表时间: 2022
• 期刊: Astronomy & Astrophysics
• 影响因子: 6.5
• 作者: Brivio R