New Methods for Observational Cosmology and Galactic Archaeology

我们能否使用机器智能来发现庞大天文学数据集中的复杂性和关系，以了解宇宙结构和恒星系统？天空测量产生的海量数据需要有效的技术来将原始测量数据提炼成物理洞察。这项研究建议旨在开发新的方法，以最大限度地最大限度地从多个天文调查中联合提取物理量。我们将利用可扩展的机器智能和统计技术来分析多波长天体物理数据，重点是银河系考古学和宇宙学。通过收集数百万颗恒星的光谱，来自公共天空测量和最先进的太空模拟，我们将实施智能机器来绘制我们的宇宙。这些系统将学习多波长数据的复杂性，特别强调仪器和大气污染物，这些污染物传统上很难解释。由此产生的系统将通过概率输出来预测恒星的一致温度、金属丰度、di和化学丰度。我们将通过包括来自广域成像测量的信息来发展我们的系统，从而提供额外的能力来预测恒星的质量、年龄和距离。我们的目标是建造一台银河系学习机器，研究恒星系统的化学演化，以及前所未有的元素核合成。同时，我们将为河外来源设计智能系统。欧几里德联盟就是这种应用的一个典型例子：地面和空间望远镜收集数以百万计的银河系外天空在光学和红外波长下的图像和光谱，以探测宇宙的黑暗部分。我们将发展我们的银河系机器，通过模拟学习星系像素、空间分布和光谱，增加预测河外红移和约束宇宙学模型的能力。该项目的重点是培养研究生在这个大天文数据的黄金时代创造和开发新颖的机器学习技术。在该计划的支持下，加拿大的年轻研究人员将获得强大的学术研究和工业数据科学技能。

Can we use machine intelligence to discover complexities and relationships within huge astronomy data sets to understand cosmic structure and stellar systems? The enormous quantities of data generated from sky surveys require efficient techniques to distill raw survey data into physical insights. This research proposal aims at developing novel methods to maximize the extraction of physical quantities jointly from multiple sky surveys. We will take advantage of scalable machine intelligence and statistical techniques to analyze multi-wavelength astrophysical data with a focus on galactic archaeology and cosmology. By assembling spectra of millions of stars, from public sky surveys and state-of-the-art simulations, we will implement smart machines to map our universe. The systems will learn the complexities of the multi-wavelength data with a special emphasis on instrumental and atmospheric contaminants, traditionally difficult to account for. The resulting systems will predict consistent temperatures, metallicities, di and chemical abundances of the stars with probabilistic outputs. We will evolve our systems by including information from wide field imaging surveys, giving the extra capacity to predict mass, age and distances of the stars. Our goal is to build a Milky Way learning machine to study the chemical evolution of stellar systems, and the nucleosynthesis of elements at an unprecedented level. In parallel we will design smart systems for extragalactic sources. The Euclid consortium is a prime example for such an application: ground and space telescopes collecting millions of images and spectra of the extragalactic sky at optical and infrared wavelengths, to probe the dark sector of the universe. We will evolve our Milky Way machine to learn from galaxy pixels, spatial distributions and spectra together with simulations, adding a capacity to predict extragalactic redshifts and constrain cosmological models. The program has a strong focus on training graduate students to create and develop novel machine learning techniques in this Golden Era of Big Astronomy Data. Young researchers in Canada supported by this program will acquire strong skills for both academic research and data science in industry.