论文标题
类星体因子分析 - 一种无监督和概率的类星体连续性预测算法，具有潜在因子分析
Quasar Factor Analysis -- An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis
论文作者
论文摘要
自从他们的第一个发现以来，类星体一直是遥远宇宙的重要探针。但是，由于我们对其性质的了解有限，预测固有的类星体连续图已经瓶颈了它们的用法。现有的类星体连续体恢复方法通常依赖于有限数量的高质量的类星体光谱，这可能无法捕获类星体种群的全部多样性。在这项研究中，我们提出了一个无监督的概率模型，即果酱因子分析（QFA），该模型将因子分析（FA）与乳乳介质（IGM）的物理先验结合起来，以克服这些限制。 QFA通过一般建模的类星体光谱捕获了类星体连续图的后验分布。我们证明，与以前的方法相比，QFA可以实现最先进的性能，即$ \ sim 2 \％相对错误，对于LY $α$森林区域的连续预测。我们进一步适合90,678 $ 2 <\ mathrm {z} <3.5 $，snr $> 2 $ 2 $ quasar Spectra来自Sloan Digital Sky Survey Data Release 16版本16，并发现以前的方法不确定continua的情况下，QFA会产生更多的视觉持续性。 QFA还达到$ \ sillsim 1 \％$错误，在$ \ mathrm {z} \ sim 3 $和$ \ sim 4 \％$ in $ \ mathrm {z} \ sim 2.4 $中的1D ly $α$ power频谱测量中的$α$ power频谱测量值。此外，QFA决定了比PCA更具身体动机的潜在因素。我们研究了潜在因素的演变，除了鲍德温效应外，没有明显的红移或光度依赖性。 QFA的生成性质也可以稳健地实现异常检测。我们表明QFA有效地选择了偏远的类星体光谱，包括损坏的$α$系统和潜在的II型类星体光谱。
Since their first discovery, quasars have been essential probes of the distant Universe. However, due to our limited knowledge of its nature, predicting the intrinsic quasar continua has bottlenecked their usage. Existing methods of quasar continuum recovery often rely on a limited number of high-quality quasar spectra, which might not capture the full diversity of the quasar population. In this study, we propose an unsupervised probabilistic model, Quasar Factor Analysis (QFA), which combines factor analysis (FA) with physical priors of the intergalactic medium (IGM) to overcome these limitations. QFA captures the posterior distribution of quasar continua through generatively modeling quasar spectra. We demonstrate that QFA can achieve the state-of-the-art performance, $\sim 2\%$ relative error, for continuum prediction in the Ly$α$ forest region compared to previous methods. We further fit 90,678 $2<\mathrm{z}<3.5$, SNR$>2$ quasar spectra from Sloan Digital Sky Survey Data Release 16 and found that for $\sim 30\%$ quasar spectra where the continua were ill-determined with previous methods, QFA yields visually more plausible continua. QFA also attains $\lesssim 1\%$ error in the 1D Ly$α$ power spectrum measurements at $\mathrm{z}\sim 3$ and $\sim 4\%$ in $\mathrm{z}\sim 2.4$. In addition, QFA determines latent factors representing more physically motivated than PCA. We investigate the evolution of the latent factors and report no significant redshift or luminosity dependency except for the Baldwin effect. The generative nature of QFA also enables outlier detection robustly; we showed that QFA is effective in selecting outlying quasar spectra, including damped Ly$α$ systems and potential Type II quasar spectra.
