Stellar Cartography: Mapping the Local Group using Variable Stars

该项目将利用包括盖亚、4 MOST、OGLE和JWST在内的一系列地面和空间望远镜对变星的观测，研究我们最近邻星系的结构和演化。这项工作的成果将为大麦哲伦星系演化的研究提供信息，包括麦哲伦桥结构的性质和历史，这项工作还将为本地星系提供一个独立的距离测量方法，从而可以建立一个独立于造父变星的天文距离。重要的是，由于RR-天琴座是在星系晕中发现的，它们作为距离测量的使用将使我们能够校准与我们的观点边缘相对的SNe宿主星系的距离，扩大SNe星系宿主的样本，可以用于确定哈勃常数，同时可能消除距离尺度中的系统误差的进一步来源。该项目还可以利用JWST来研究本星系群外围的星系（有待于提案的批准）。该项目可以细分如下：将利用南半球巡天的数据来确定麦哲伦云中RR天琴座的表观红外星等周期-亮度关系。 GAIA巡天的精确天体测量距离将被用来校准这个P-L关系与当地银河RR-天琴座，以产生一个绝对的P-L关系。金属丰度效应将使用4 MOST调查的金属丰度数据进行解释。利用这个导出的绝对P-L关系，可以确定麦哲伦云中个别RR天琴座的精确距离。将麦哲伦星云中恒星的RR天琴座距离与这些恒星的GAIA 2-D天体测量相结合，将能够构建LMC的详细3-D地图。天琴座RR是较老的恒星，因此衍生的LMC 3-D地图将追踪较老的恒星群。这可以与最近在LMC中对造父变星的研究进行比较，这些研究追踪了年轻的恒星群体，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变的理论模型进行比较项目时间的三分之一将用于与4 MOST直接有关的活动。这方面的确切细节将通过与4 MOST联合体的讨论来确认，但这最有可能是与可变星星识别相关的管道开发活动。这对项目的好处是成为4 MOST联盟的成员，保证专有访问4 MOST数据，包括光谱金属丰度。除了博士学位，这种专有访问权将在4 MOST的生命周期内保留，这种状态可以在机构之间转移，并将大大有利于我的研究生涯。在GAIA目录中，大约有100，000个源被确定为银河RR Lyrics。从以前对造父变星的研究来看，有一种预期，即这些来源中的许多来源将被错误地分类。为了清理该数据集，机器学习将用于准确识别RR Lyrics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.