Galaxy Evolution - a multicomponent machine-learning model

Galaxy Evolution - 多组件机器学习模型

• 批准号: 2113610
• 金额: --
• 依托单位: University of Sussex
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2113610
• 关键词: Galaxy; Evolution; multicomponent; machine; learning

The scientific objective is to understand the formation and evolution of galaxies by applying novel statistical techniques, developed for machine-learning and artificial intelligence, to a new multi-wavelength data-base of galaxies. The statistical characterisation of the extra-galactic populations is fundamental to address the evolution of galaxies and AGN, both for empirical comparisons of populations at different epochs and for confronting theoretical models. The extraordinary wealth of data now available from deep multi-wavelength surveys, the questions being posed, and the theoretical models that address them, are rich and complex. However, the statistical measures being used (luminosity functions, template spectral energy distributions, 2-point correlation functions and ad-hoc scaling relations) have changed little since the 1970s. These outdated measures entrench our prejudices and limit our understanding. In this project you will adopt a radical, new approach. Using techniques from machine learning we will build a probabilistic generative model of the vast multi-wavelength catalogue and map data within the Herschel Extragalactic Legacy Project (HELP). This model will provide a robust probabilistic description of the observables, with limited and well defined prior assumptions. You will use this to characterise the key emission components of galaxy populations simultaneously at all wavelengths and the probabilistic relations between them. You will focus on the star forming and AGN components where understanding has been particularly limited by ad-hoc segregation and classification. The full posterior probabilities will be fully characterised, providing us with a compact "description" of the data. This then allows you to develop a tool that can be used to generate synthetic data sets, consistent with the constraining data, that can then be used to test any physically motivated model of galaxy evolution. The Herschel Extragalactic Legacy Project (HELP https://herschel.sussex.ac.uk/) is a European funded project due to complete in June 2018. HELP combines data from all the world-leading terrestrial and space observatories. These data were taken by ambitious surveys charting over 1000 square degrees of the sky at different wavelengths. HELP has added value through e.g. linking and homogenising the data, by providing high-level descriptions of the selection functions of the data, deriving new physical quantities like redshifts, stellar masses and star formation rates and new tools to access these. A key development in HELP has been the XID+ tool which uses Bayesian Inference for a hierarchical probabilistic model of the low-resolution data from e.g. Herschel using the higher resolution data from other facilities. This project will exploit the data from HELP and in particular exploit, and extend, the XID+ framework.

科学目标是通过将为机器学习和人工智能开发的新的统计技术应用于新的多波长星系数据库，了解星系的形成和演变。河外星系群的统计特征是解决星系和活动星系核演化的基础，无论是在不同时期的人口的经验比较，并面对理论模型。现在从深层多波长调查中获得的异常丰富的数据，提出的问题以及解决这些问题的理论模型是丰富而复杂的。然而，自20世纪70年代以来，所使用的统计测量（亮度函数、模板光谱能量分布、2点相关函数和特殊标度关系）几乎没有变化。这些过时的措施加深了我们的偏见，限制了我们的理解。在这个项目中，你将采用一种激进的新方法。利用机器学习技术，我们将在赫歇尔河外遗产项目（HELP）中建立一个庞大的多波长目录和地图数据的概率生成模型。这个模型将提供一个强大的概率描述的可观测量，有限的和明确的先验假设。您将使用它来同时在所有波长上计算星系群的关键发射分量以及它们之间的概率关系。你将专注于星星的形成和活动星系核的组成部分，这些部分的理解受到特别分离和分类的限制。完整的后验概率将被充分表征，为我们提供一个紧凑的数据“描述”。这就允许你开发一个工具，可以用来生成与约束数据一致的合成数据集，然后可以用来测试任何物理驱动的星系演化模型。赫歇尔河外遗产项目（HELP https：//herschel.sussex.ac.uk/）是一个欧洲资助的项目，将于2018年6月完成。HELP结合了来自世界领先的地面和空间观测站的数据。这些数据是由雄心勃勃的调查采取图表超过1000平方度的天空在不同的波长。“地球物理学、环境和生命科学”方案通过以下方式增加了价值：将数据联系起来并使之均匀化，对数据的选择功能进行高层次的描述，推导出红移、恒星质量和星星形成率等新的物理量，以及获取这些物理量的新工具。HELP的一个关键发展是XID+工具，该工具使用贝叶斯推理，对来自Herschel等的低分辨率数据进行分层概率模型，使用来自其他设施的更高分辨率数据。该项目将利用来自HELP的数据，特别是利用和扩展XID+框架。