论文标题
类星体连续预测的无监督学习方法
An Unsupervised Learning Approach for Quasar Continuum Prediction
论文作者
论文摘要
建模类星体光谱是天体物理学的基本任务，因为类星体是宇宙进化的明显标志。我们介绍了一种新型的无监督学习算法，类星体因子分析（QFA），用于从嘈杂的Quasar Spectra中恢复固有的类星体连续图。 QFA假定可以将$α$ forest近似为高斯过程，并且可以很好地描述为潜在因子模型。我们表明，QFA可以通过无监督的学习，并直接从类星体光谱，类星体连续图和$ $α$森林中学习。与以前的方法相比，QFA可实现类星体连续预测的最新性能，但无需预定义的训练连续性。此外，QFA的产生和概率性质为理解黑洞的演变以及进行分布外检测和其他贝叶斯下游推论铺平了道路。
Modeling quasar spectra is a fundamental task in astrophysics as quasars are the tell-tale sign of cosmic evolution. We introduce a novel unsupervised learning algorithm, Quasar Factor Analysis (QFA), for recovering the intrinsic quasar continua from noisy quasar spectra. QFA assumes that the Ly$α$ forest can be approximated as a Gaussian process, and the continuum can be well described as a latent factor model. We show that QFA can learn, through unsupervised learning and directly from the quasar spectra, the quasar continua and Ly$α$ forest simultaneously. Compared to previous methods, QFA achieves state-of-the-art performance for quasar continuum prediction robustly but without the need for predefined training continua. In addition, the generative and probabilistic nature of QFA paves the way to understanding the evolution of black holes as well as performing out-of-distribution detection and other Bayesian downstream inferences.
