A New, Data-Driven Era for Precision Cosmology: Measuring the Expansion Rate of the Universe with Machine Learning.

精确宇宙学的数据驱动新时代：通过机器学习测量宇宙的膨胀率。

• 批准号: RGPIN-2020-05102
• 负责人: PerreaultLevasseur, Laurence
• 金额: $ 2.4万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740391
• 关键词: New; Data; Driven; Era; Precision

With a new generation of sky surveys coming online in the next decade, cosmology is about to enter a new era of big data that is bound to revolutionize this field. These new experiments will provide a wealth of data to answer fundamental questions such as the nature of dark matter and dark energy, but are also opening a new window to solve an emerging crisis in cosmology: the measurement of the Hubble constant, H0, which is now at an almost 4s tension between early (the cosmic microwave background) and late (type 1a supernovae) universe probes. If confirmed, this disagreement would imply new physics beyond the standard model of cosmology, which renders making a precise and accurate determination of this parameter one of the most crucial goals of modern cosmology experiments. My research program will use the power of advanced machine learning methods to unlock the power of the rich datasets from this new generation of observatories to measure H0 using images of a new population of strongly lensed quasars, potentially resolving this crisis. This will be achieved through the development of sophisticated and innovative deep learning pipelines and includes the following three short-term objectives: -The production of fast, inexpensive, and extremely realistic simulations of strongly lensed quasar observations; -The development of a novel machine learning analysis pipeline to obtain lens models and time delays from observations in a completely automated and accurate manner; -The development of a likelihood-free Bayesian inference framework to estimate calibrated uncertainties of predicted parameter values. This will integrate our best understanding of lensing, quasar physics, and cosmology in a self-consistent manner that is not computationally tractable with traditional methods, potentially increasing the number of useable lensing systems for this science in upcoming surveys by an order of magnitude. It will enable us to fully exploit the true potential of the upcoming data from the new generation sky surveys. This grant will provide training for undergraduate, M.Sc., and Ph.D. students in the emerging field of machine learning cosmology. In the era of big data, their expertise and work funded by this Discovery Grant will provide critical resources and ancillary science products for use by the global lensing community, and the tools and products developed will be made open source for the use of the broader community.

随着新一代的Sky Surveys在未来十年的网上进行，宇宙学即将进入一个大数据的新时代，这必将彻底改变这一领域。这些新实验将提供大量数据来回答基本问题，例如暗物质和暗能量的性质，但也正在为解决宇宙学的新兴危机打开一个新的窗口：对哈勃常数的测量，H0，现在在早期之间几乎是4S紧张局势（宇宙的Microhave背景）和后期的（类型1A opernovae）和（1A型超级探测器）。如果得到证实，这种分歧将意味着超出标准宇宙学模型的新物理学，这使得对该参数的精确确定确定是现代宇宙学实验最关键的目标之一。 我的研究计划将使用先进的机器学习方法的力量，从新一代的观测站中释放富裕数据集的功能，以使用新的强烈镜头类星体的新图像来测量H0，从而有可能解决这一危机。这将通过开发复杂和创新的深度学习管道来实现，并包括以下三个短期目标： - 生产快速，廉价且极为现实的类星体观察的模拟； - 新型机器学习分析管道的开发以完全自动化和准确的方式从观察中获取镜头模型和时间延迟； - 无似然的贝叶斯推理框架的发展，以估计预测参数值的校准不确定性。 这将以一种自洽的方式将我们对镜头，类星体物理学和宇宙学的最佳理解融为一体，而传统方法在计算上不可计算，这可能会通过一定的大量级来增加该科学的可用镜头系统的数量。这将使我们能够完全利用新一代天空调查中即将到来的数据的真正潜力。该赠款将为本科，硕士和博士学位提供培训。机器学习宇宙学领域的学生。在大数据时代，他们的专业知识和作品由这项发现赠款资助，将提供关键的资源和辅助科学产品，以供全球镜头社区使用，并且开发的工具和产品将成为开源的，以使用更广泛的社区。