Stellar Cartography: Mapping the Local Group using Variable Stars

这个项目将使用一系列地面和空间望远镜对变星的观测，包括GAIA、4MOST、OGLE和JWST，以调查我们最近的邻近星系的结构和演化。这项工作的结果将为LMC的演化研究提供信息，包括麦哲伦桥结构的性质和历史，将允许与麦哲伦云合并历史的模拟进行比较。这项工作还将为本地星系提供独立的距离测量，这将允许建立与造父变星无关的天文距离。重要的是，由于RR-Lyrae是在星系晕中发现的，使用它们作为距离测量将能够校准到我们视点边缘的SNE宿主星系的距离，扩大可用于确定哈勃常数的SNE星系主机的样本，同时潜在地消除距离尺度上的另一个系统误差来源。该项目有机会利用JWST来研究位于Local Group外围的星系(有待批准)。该项目可以分解如下：天琴座RR天琴座在麦哲伦星云中的视红外星等周期-光度关系将使用南半球天空测量的数据来确定。然后，我们将使用GAIA测量的精密天体测量距离，将这个P-L关系与当地的天琴座RR-Lyrae星系进行校准，以产生一个绝对的P-L关系。将使用4MOST调查的金属丰度数据来考虑金属丰度的影响。利用导出的绝对P-L关系，可以确定麦哲伦星云中天琴座RR星的精确距离。将麦哲伦星云中恒星的天琴座RR距离与这些恒星的GAIA二维天文测量相结合，将能够构建详细的LMC三维地图。天琴座RR是较老的恒星，因此导出的LMC 3-D图将跟踪较老的恒星群。然后，这可以与最近在LMC中追踪年轻恒星群的造父变星的工作进行比较，可以在参数空间的几个区域进行比较，包括金属丰度、年龄和空间。与麦哲伦星云合并历史和演化的理论模型比较。项目时间的三分之一将花在与4MOST直接相关的活动上。这方面的确切细节将通过与4MOST财团的讨论得到确认，但这最有可能是与变星识别有关的管道开发活动。这对该项目的好处是成为4MOST联盟的成员，并保证获得4MOST数据的专有权限，包括光谱金属。除了博士学位之外，这一专有访问权限将在4MOST的一生中保留，这种身份可以在机构之间转移，并将极大地有利于我作为研究人员的职业生涯。在GAIA星表中，大约有100,000个来源被确定为天琴座RR星系。从以前关于造父变星的工作中，人们预计这些来源中的许多将被错误地分类。为了清理这个数据集，将使用机器学习来准确地识别RR天琴座的光曲线。此步骤可能需要使用高性能计算资源。

This project will use observations of variable stars from a range of ground and space-based telescopes including Gaia, 4MOST, OGLE, and JWST to investigate the structure and evolution of our nearest neighbour galaxies.This output from this work will inform studies of the evolution of the LMC, including the nature and history of the Magellanic Bridge structure, will allow comparison to simulations of the merger history of the Magellanic Clouds.This work will also provide an independent distance measure for local galaxies which will allow the construction of a Cepheid-independent astronomical distance. Importantly, as RR-Lyrae are found in galactic haloes, their use as a distance measure will enable the calibration of distances to SNe host galaxies that are edge-on to our viewpoint, widening the sample of SNe galactic hosts that can be used in the determination of the Hubble Constant at the same time as potentially removing a further source of systematic error in the distance scale. There is scope for the project to reach out to examine galaxies at the outskirts of the Local Group using JWST (subject to proposal approval).The project can be broken down as follows: An apparent infrared magnitude Period-Luminosity Relation for RR Lyrae in the Magellanic Clouds will be determined using data from Southern Hemisphere sky surveys. Precision astrometric distances from the GAIA survey will then be used to calibrate this P-L relation with local galactic RR-Lyrae in order to produce an absolute P-L relation. Metallicity effects will be accounted for using metallicity data from the 4MOST survey. Using this derived absolute P-L relation, accurate distances to individual RR Lyrae in the Magellanic Clouds can be determined. Coupling RR Lyrae distances of stars in the Magellanic Clouds to GAIA 2-D astrometry of these stars will enable the construction of detailed 3-D map of the LMC. RR Lyrae are older stars, hence the derived LMC 3-D map will trace the older stellar population. This can then be compared to recent work on Cepheids in the LMC which trace the younger stellar population, with comparisons possible in several regions of parameter space including metallicity, age, and spatial. Comparison with theoretical models of the merger history and evolution of the Magellanic Clouds.One third of the project time will be spent on activities directly related to 4MOST. The exact details of this aspect will be confirmed through discussion with the 4MOST consortium, but this is most likely to be pipeline development activity related to variable star identification. The benefit from this to the project is membership of the 4MOST consortium, with a guarantee of proprietary access to 4MOST data including spectroscopic metallicities. Beyond the PhD, this proprietary access will be retained for the lifetime of 4MOST, and this status is transferable between institutions and will significantly benefit my career as a researcher.There are around 100,000 sources identified as galactic RR Lyrae in the GAIA catalogue. From previous work on Cepheids there is an expectation that many of these sources will have been incorrectly classified. In order to clean this data set, machine learning will be used to accurately recognise RR Lyrae light-curves. This step may require the use of High-Performance Computing resources.