Deep unsupervised machine learning approaches for galaxy evolution studies

天文观测为研究星系的形成和演化提供了重要信息。这些信息通常以光谱或光度的形式提供，每种形式都提供有价值的见解。检查星系的光谱揭示了有关其性质和内部过程的关键信息。换句话说，星系的光谱中包含了大量关于星系的信息。各种方法可以用来推断不同的物理参数，如星系的金属丰度，恒星质量和星星形成率。虽然光谱数据为我们提供了关于星系的宝贵和详细的信息，但测光记录（在不同的过滤器中）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。这些巡天图通常提供许多不同物理参数的二维地图，让我们了解银河系的历史。这种调查的一个例子，以及本提案中使用的数据，是在阿帕奇点天文台调查（MaNGA）附近测绘Galerkin。由于这些光谱和光度数据是如此不同，没有一个经典的高通量的方法来结合它们。要做到这一点，并在我们对星系演化的理解中迈出下一步，我们需要使用机器学习（ML）技术。ML中的无监督方法旨在以最少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隐藏的关系和模式。此外，可以联合收割机组合多个深度无监督方法，进一步增强了可扩展性和整体性能。我的研究建议侧重于利用潜在的数据，从现有的光度和光谱数据从MANGA调查。MaNGA为我们提供了非常翔实的数据，但代价是它非常复杂。例如，当以复杂的方式对星系进行采样时，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以相差一个数量级。这些星系的分光光度数据也是可用的。通过将量化不同特征的两个数据集的这些潜在表示拼接在一起，我们将生成更强大的MaNGA星系摘要。在这个提案中，我们将继续开发应用于天文数据的无监督ML技术。在新研究人员中建立这些方法的专业知识的同时，我们还将通过基于内部过程的星系无偏聚类为天文学和天体物理学研究社区做出贡献。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）与我们观测到的宇宙提供基础。

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.