Deep unsupervised machine learning approaches for galaxy evolution studies

用于星系演化研究的深度无监督机器学习方法

• 批准号: RGPIN-2022-05148
• 负责人: Teimoorinia, Hossen
• 金额: $ 1.75万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=763113
• 关键词: Deep; unsupervised; machine; learning; approaches

Astronomical observations present essential information for the study of galaxy formation and evolution. This information is usually delivered in spectroscopic or photometric form, each providing valuable insights. Examining a galaxy's spectra reveals crucial information about its properties and internal processes. In other words, a considerable volume of information about galaxies is encoded in their spectra. Various methods can be used to infer different physical parameters, such as the metallicity of galaxies, their stellar masses, and star formation rates. While spectroscopic data provide us with invaluable and detailed information about galaxies, photometric records (in different filters) also present valuable knowledge about the morphological characteristics of galaxies. Recent technological advances in Integral Field Units (IFUs) are ideal for exploring spatially extended sources. These surveys generally provide two-dimensional maps in many different physical parameters, informing us of galactic history. An example of such a survey, and the data to be used in this proposal, is Mapping Nearby Galaxies at the Apache Point Observatory survey (MaNGA). Since these spectroscopic and photometric data are so different, there isn't a classical high-throughput way of combining them. To do this, and take the next step in our understanding of galaxy evolution, we need to use Machine Learning (ML) techniques. Unsupervised methods in ML aim to infer structures from the input data with minimal assumptions. These methods can discover hidden relationships and patterns within the data by abstracting the data to a compressed informative representation called latent data. In addition, the possibility to combine multiple deep unsupervised methods further enhances the scalability and overall performance. My research proposal focuses on utilizing latent data from available photometric and spectroscopic data from the MaNGA survey. MaNGA gives us very informative data, but the trade-off is that it is very complex. For example, it is challenging to compare galaxies using spectra alone when they are sampled in a complicated way. The number of samples from two galaxies can differ by an order of magnitude. Spectroscopic and photometric data for the same galaxies are available. By stitching together these latent representations of two data sets quantifying different characteristics, we will generate more robust summaries of MaNGA galaxies. In this proposal, we're continuing to develop unsupervised ML techniques applied to astronomical data. While building expertise with these methods in new researchers, we will also be contributing to the astronomy and astrophysics research communities by unbiased clustering of galaxies based on internal processes. Summarizing galaxies using these data-driven methods will provide a foundation for comparing different data sets, including simulations, to our observed universe.

天文观测为研究星系的形成和演化提供了基本信息。这些信息通常以光谱或光度的形式提供，每种形式都提供了有价值的见解。研究星系的光谱可以揭示有关其性质和内部过程的关键信息。换句话说，关于星系的大量信息都编码在它们的光谱中。不同的方法可以用来推断不同的物理参数，如星系的金属丰度、恒星质量和恒星形成率。虽然光谱数据为我们提供了关于星系的宝贵而详细的信息，但光度学记录(在不同的滤光片中)也提供了关于星系形态特征的宝贵知识。积分场单元(IFU)的最新技术进步是探索空间扩展源的理想选择。这些调查通常提供许多不同物理参数的二维图，告诉我们银河系的历史。阿帕奇点天文台勘测(MANGA)的一个例子是绘制附近星系的地图，这也是这项提议中使用的数据。由于这些光谱和光度学数据是如此不同，没有一种经典的高通量方法来结合它们。要做到这一点，并在我们理解星系演化的下一步，我们需要使用机器学习(ML)技术。ML中的非监督方法旨在以最小的假设从输入数据中推断结构。这些方法可以通过将数据抽象为称为潜在数据的压缩信息表示来发现数据中的隐藏关系和模式。此外，组合多个深度非监督方法的可能性进一步增强了可伸缩性和整体性能。我的研究建议侧重于利用漫画调查中现有的光度和光谱数据中的潜在数据。漫画为我们提供了非常丰富的数据，但代价是它非常复杂。例如，当星系以复杂的方式进行采样时，仅使用光谱来比较星系是具有挑战性的。来自两个星系的样本数量可能相差一个数量级。这些星系的光谱和光度学数据都是可用的。通过将这些量化不同特征的两个数据集的潜在表示拼接在一起，我们将生成更健壮的漫画星系摘要。在这项提案中，我们继续开发应用于天文数据的无监督最大似然技术。除了在新的研究人员中积累这些方法的专业知识外，我们还将通过基于内部过程对星系进行无偏见的星系团，为天文学和天体物理学研究界做出贡献。使用这些数据驱动的方法总结星系将为比较不同的数据集，包括模拟，与我们观察到的宇宙提供基础。