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Geological mapping in Mercury's southern latitudes

水星南部纬度的地质测绘

基本信息

批准号：
2277812
负责人：
金额：
--
依托单位：
The Open University
依托单位国家：
英国
项目类别：
Studentship
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=studentship-2277812
关键词：
Geological mapping Mercury southern latitudes

项目摘要

Description:The aim of this project is to use a relatively new machine learning technology, GenerativeAdversarial Networks, to predict the unresolved structures in submillimetre-wavelengthimages and then use these predictions to find candidate ultra-high redshift galaxies.Extreme high-redshift galaxies can be detected in submm-wavelength surveys made by theHerschel Space Observatory and others, but because of the redshifting, these galaxies aretypically only detected at the longest wavelengths, e.g. 500 microns. At these wavelengths,the angular resolution is at its coarsest, so distant galaxies are seen as blended withforeground interlopers. Several groups have attempted to get around this by modelling thedata at all wavelengths. This approach deconvolves the longest wavelength data by usingthe locations of galaxies detected at other wavelengths as a prior, but this iscomputationally difficult and solutions are not unique. Generative Adversarial Networks area relatively new machine learning technology that, once trained, can reconstruct missinginformation in images. This has been shown to work in blurred images of galaxies,accurately reconstructing galaxy morphologies in many cases. This project will deploy thistechnology on real and simulated submm-wave images, find candidate ultra-high redshiftgalaxies, follow them up with ground-based and space observatories, place statisticalconstraints on ultraluminous star forming galaxies at the earliest accessible epochs andconstrain models of high redshift star formation

描述：该项目的目的是使用一种相对较新的机器学习技术，GenerativeAdversarial Networks，来预测亚毫米波长图像中未解析的结构，然后使用这些预测来寻找候选的超高红移星系。极端高红移星系可以在由赫歇尔空间天文台和其他机构进行的亚毫米波长巡天中检测到，但由于红移，这些星系通常只能在最长的波长（例如500微米）中检测到。在这些波长下，角度分辨率是最粗糙的，所以遥远的星系被看作是与前景闯入者混合在一起的。一些研究小组试图通过模拟所有波长的数据来解决这个问题。这种方法通过使用在其他波长检测到的星系位置作为先验，对最长波长数据进行反卷积，但这在计算上很困难，而且解决方案也不是唯一的。生成对抗网络是相对较新的机器学习技术，一旦训练，可以重建图像中的缺失信息。在许多情况下，这已经被证明在星系模糊图像中有效，准确地重建了星系的形态。该项目将在真实和模拟的亚毫米波图像上部署该技术，寻找候选的超高红移星系，用地面和太空天文台跟踪它们，在最早可到达的时代对超亮恒星形成星系进行统计约束，并约束高红移恒星形成模型