G2G: from galaxies to the ground

G2G：从星系到地面

• 批准号: ST/S002057/1
• 负责人: James Geach
• 金额: $ 9.63万
• 依托单位: University of Hertfordshire
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ST%2FS002057%2F1
• 关键词: G2G; galaxies; ground

Research into galaxy formation and evolution involves attempting to reconstruct the detailed properties of extremely distant galaxies using a very small amount of data. For example, even in the best imaging possible with the Hubble Space Telescope, the most distant galaxies appear as faint smudges, comprised of just a handful of pixels, scarcely brighter than the random noise in the image. Yet with knowledge of the basic constituents of galaxies, we can accurately model their properties: the total mass in stars, the rate at which they are forming new stars, their ages, and so-on. These are vital for our understanding of how galaxies formed and evolved in the Universe. One of the key techniques is the use of stellar population synthesis models that predict the amount of light emitted by individual stars of different types in galaxies. Even though we only observe the integrated starlight from distant galaxies, and cannot resolve the individual stars, we can derive likelihood distributions for the key underlying physical properties that give rise to those few bright pixels. Effectively we are measuring 'sub-grid' properties, often pushing the data to its limit and extracting useful information even from below the noise. The similarities between the problem of modelling distant galaxies and that of modelling the properties of a small portion of land are striking: in both cases we are trying to understand a complex system (a disc of stars or a field of maize, for example) from a very limited set of data. A small, contaminating patch of diseased or unhealthy crop, mixed in with healthy crop, could be blended below the resolution limit of the data. Nevertheless, this mixture is expected to leave a tell-tale spectral signature in the imaging. This is akin to detecting the presence of dust in distant galaxies, that subtly alters the colour of the starlight. In the case of earth observation, continuous monitoring satellites relevant to crop surveillance have a spatial resolution of order 10-metres, such that a small field of crops could be represented by just a few pixels in an image.G2G will develop and apply astrophysical techniques to continuous-monitoring earth observation satellite imaging, with a specific focus on extracting useful information from images down to the pixel scale. The aim will be to provide spatially pin-pointed information on the health of crops growing within small (10s of metres) parcels of land, measured simultaneously over swaths of hundreds of kilometres. The goal will be to rapidly identify subtle - otherwise undetected - signatures of crop stress or yield threats, with information delivered efficiently to large numbers of farmers and land users, even those with very small agricultural footprints.

对星系形成和演化的研究涉及到试图使用非常少量的数据重建极遥远星系的详细性质。例如，即使是在哈勃太空望远镜最好的成像中，最遥远的星系也看起来像是模糊的污点，只由少数几个像素组成，几乎不比图像中的随机噪音亮多少。然而，有了星系的基本组成知识，我们就可以准确地模拟它们的属性：恒星的总质量、它们形成新恒星的速度、它们的年龄等等。这些对于我们理解星系是如何在宇宙中形成和演化至关重要的。关键技术之一是使用恒星群合成模型来预测星系中不同类型的单个恒星发出的光量。即使我们只观察来自遥远星系的综合星光，不能分辨单个恒星，我们也可以推导出产生这几个明亮像素的关键潜在物理属性的似然分布。有效地，我们正在测量‘子网格’的属性，经常把数据推到它的极限，并提取有用的信息，即使在噪音之下。模拟遥远星系的问题和模拟一小部分陆地性质的问题有惊人的相似之处：在这两种情况下，我们都试图从非常有限的数据集理解一个复杂的系统(例如，星盘或玉米地)。一小块受污染的病态或不健康作物，与健康作物混合在一起，可以在数据的分辨率极限以下混合。然而，这种混合物预计会在成像中留下一个明显的光谱特征。这类似于探测遥远星系中存在的尘埃，微妙地改变了星光的颜色。就对地观测而言，与作物监测有关的连续监测卫星具有10米量级的空间分辨率，因此一幅图像中只有几个像素就可以代表一小块庄稼。G2G将开发和应用天体物理技术来连续监测对地观测卫星成像，重点是从像素尺度的图像中提取有用信息。其目的将是提供关于在小块(10米)土地上生长的作物健康的空间精确信息，这些土地同时在数百公里长的范围内进行测量。其目标将是快速识别作物压力或产量威胁的微妙--否则未被发现--的特征，并将信息高效地传递给大量农民和土地使用者，即使是那些农业足迹非常小的人。