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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=763430
• 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.

随着新一代天文测量在未来十年上线，宇宙学即将进入大数据的新时代，这势必会给这一领域带来革命性的变化。这些新的实验将提供大量的数据来回答诸如暗物质和暗能量的本质等基本问题，但也为解决宇宙学中一个新出现的危机打开了一个新的窗口：哈勃常数H0的测量，它现在处于早期(宇宙微波背景)和晚期(1a型超新星)宇宙探测器之间几乎4s的张力。如果得到证实，这种分歧将意味着超越标准宇宙学模型的新物理学，这使得精确和准确地确定这一参数成为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机器学习方法的力量，释放来自新一代天文台的丰富数据集的力量，使用新一批强透镜类星体的图像来测量H0，潜在地解决这场危机。这将通过开发复杂和创新的深度学习管道来实现，包括以下三个短期目标：-对强透镜类星体观测进行快速、廉价和极其逼真的模拟；-开发一种新型的机器学习分析管道，以完全自动和准确的方式从观测中获取透镜模型和时间延迟；-开发一个无似然贝叶斯推理框架，以估计预测参数值的校准不确定度。这将以一种自洽的方式整合我们对透镜、类星体物理和宇宙学的最佳理解，这是传统方法在计算上无法处理的，潜在地将在即将到来的调查中增加这门科学的可用透镜系统的数量一个数量级。它将使我们能够充分挖掘即将到来的新一代天文测量数据的真正潜力。这笔资金将在新兴的机器学习宇宙学领域为本科生、理科硕士和博士生提供培训。在大数据时代，他们的专业知识和由这笔发现基金资助的工作将提供关键资源和辅助科学产品供全球透镜社区使用，开发的工具和产品将开源供更广泛的社区使用。