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.

大质量星系的形成和演化在成功的标准Eduarda-CDM形式主义的背景下得到了相当好的理解。然而，这种对宇宙演化的模拟导致了非常微弱星系的严重挑战，包括被称为失踪卫星的问题，太大而不能失败，以及卫星星系的平面。随着天文观测产生的大量优秀数据，以及计划产生更多更高质量数据的新任务，我们有一个独特的机会来解决这些问题。为此，我们需要信息技术的创新发展。在EDUCADO（通过观测数据的计算分析探索深层宇宙）中，天文学和计算机科学的交叉点进行了深入的合作，我们汇集了来自不同学科和部门的专家。我们将培养10名博士生，以开发各种高质量的方法，以解决工厂结构的形成。我们将从新的大面积测量中可靠地、可重复地探测到前所未有数量的最微弱的可观测星系。我们将研究各种类型矮星系的大样本的形态，人口和分布，并比较不同环境中矮星系的人口和属性。我们将用星系形成和演化的宇宙学模型来面对这些结果。最后，我们将对银河系和本星系群进行详细的、有原则的、鲁棒的模拟和观测，以与其他环境中的矮星系进行比较。EDUCADO将提供一个全面的跨学科，跨部门和国际培训计划，包括在我们的11个合作伙伴之一借调每个DC。我们将提供一种新的和可持续的方式来培养具有跨学科和跨部门数据科学专业知识的博士科学家，这是未来欧洲竞争力的必要条件。