Geological mapping in Mercury's southern latitudes

项目描述：该项目的目的是使用一种相对较新的机器学习技术--生成对抗网络（GenerativeAdversarialNetworks），预测亚毫米波长图像中未分辨的结构，然后利用这些预测来寻找候选超高红移星系。极端高红移星系可以在赫歇尔空间天文台和其他天文台的亚毫米波长巡天中被探测到，但由于红移，这些星系通常只能在最长波长（例如500微米）处被探测到。在这些波长下，角分辨率很低，所以遥远的星系被视为与前景闯入者的混合。几个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处探测到的星系的位置作为先验，对最长波长的数据进行反卷积，但这在计算上很困难，解决方案也不是唯一的。生成对抗网络是一种相对较新的机器学习技术，一旦经过训练，就可以重建图像中丢失的信息。这已经被证明在模糊的星系图像中有效，在许多情况下准确地重建星系形态。该项目将在真实的和模拟的亚毫米波图像上部署这项技术，寻找候选的超高红移星系，用地面和空间天文台对其进行跟踪，在最早可获得的时期对超亮星星形成星系进行天文约束，并约束高红移星星形成模型

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