Exploring the Deep Universe by Computational Analysis of Data from Observations

通过观测数据的计算分析探索宇宙深处

• 批准号: EP/Y031032/1
• 负责人: Peter Tino
• 金额: $ 33.22万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY031032%2F1
• 关键词: Exploring; Deep; Universe; Computational; Analysis

The formation and evolution of massive galaxies is reasonably well understood in the context of the successful standard lambda-CDMformalism. Such simulations of cosmic evolution, however, lead to serious challenges in the regime of the very faint galaxies,including the problems referred to as missing satellites, too big to fail, and planes of satellite galaxies. With the massive amounts ofexcellent data being produced by astronomical surveys, and with new missions scheduled to produce more data of even betterquality, we have a unique chance to solve these problems. To do this, we require innovative developments in information technology.In EDUCADO (Exploring the Deep Universe by Computational Analysis of Data from Observations), an intensive collaboration at theintersection of astronomy and computer science, we bring together experts from different disciplines and sectors. We will train 10Doctoral Candidates in the development of a variety of high-quality methods, needed to address the formation of the fainteststructures. We will reliably and reproducibly detect unprecedented numbers of the faintest observable galaxies from new large-areasurveys. We will study the morphology, populations, and distribution of large samples of various classes of dwarf galaxies andcompare dwarf galaxy populations and properties across different environments. We will confront the results with cosmologicalmodels of galaxy formation and evolution. Finally, we will perform detailed, principled, and robust simulations and observations ofthe Milky Way and the Local Group to compare with dwarf galaxies in other environments. EDUCADO will deliver a comprehensiveinterdisciplinary, intersectoral, and international training programme including a secondment at one of our 11 associated partners foreach DC. We will provide a fresh and sustainable way of training PhD scientists with interdisciplinary and intersectoral data scienceexpertise, a requisite for future European competitiveness.

在成功的标准lambda-cdmformalism的背景下，大规模星系的形成和演变得到了很好的理解。然而，这种宇宙进化的模拟导致了非常微弱的星系政策的严重挑战，包括所谓的缺失卫星的问题，太大而无法失败，以及卫星星系的飞机。随着天文调查产生的大量数据数据，并计划执行新的任务，以产生更多的数据，我们有独特的机会解决这些问题。为此，我们需要信息技术的创新发展。我们将培训10种候选者，以开发各种高质量方法，以解决微弱结构的形成。我们将可靠，可重复地检测出来自新的大型库中最微弱的可观察星系的前所未有的数量。我们将研究各种矮星系的大量样本的形态，种群和分布，以及跨不同环境的矮人星系人群和特性。我们将与星系形成和进化的宇宙学界面面对结果。最后，我们将对银河系和本地群体进行详细，原则性和强大的模拟和观察，以与其他环境中的矮星系进行比较。 Educado将提供一项全面的Intervistriary，Intersecoral和International培训计划，其中包括我们11个相关合作伙伴之一的借调。我们将提供一种新的和可持续的方式，以培训博士学位科学家，并通过跨学科和层间数据科学专长，这是未来欧洲竞争力的必要条件。