论文标题
PhotoredShift-MML：一种用于估计类星体光度红移的多模式机器学习方法
PhotoRedshift-MML: a multimodal machine learning method for estimating photometric redshifts of quasars
论文作者
论文摘要
我们提出了一种多模式学习方法，用于估计类星体的光度红移（简称光速度-MML），这长期以来一直是许多研究的主题。我们的方法包括两个主要模型，即通过多模式表示学习的特征转换模型，以及通过多模式传输学习的光度红移估计模型。由于通过MML从光度数据中学到的生成的光谱特征提供了大量信息，因此光度红移的预测准确性得到了显着提高。 Sloan Digital Sky Survey（SDSS）数据17中总共有415,930个类星体，对我们的实验进行了筛选，其中红移在1到5之间。我们使用了|Δz| = |（z_phot-z_spec）/（1+z_spec）|为了评估红移预测并证明准确性提高了4.04％。借助生成的光谱特征，数据比例|Δz| <0.1可以达到总测试样品的84.45％，而单模式光度数据达到80.41％。此外，|Δz的根平方（rms）|显示出从0.1332降低到0.1235。我们的方法有可能将其推广到其他天文数据分析，例如星系分类和红移预测。可以在https://github.com/hongshuxin/photoredshift-mml上找到算法代码。
We propose a Multimodal Machine Learning method for estimating the Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long been the subject of many investigations. Our method includes two main models, i.e. the feature transformation model by multimodal representation learning, and the photometric redshift estimation model by multimodal transfer learning. The prediction accuracy of the photometric redshift was significantly improved owing to the large amount of information offered by the generated spectral features learned from photometric data via the MML. A total of 415,930 quasars from Sloan Digital Sky Survey (SDSS) Data Release 17, with redshifts between 1 and 5, were screened for our experiments. We used |Δz| = |(z_phot-z_spec)/(1+z_spec)| to evaluate the redshift prediction and demonstrated a 4.04% increase in accuracy. With the help of the generated spectral features, the proportion of data with |Δz| < 0.1 can reach 84.45% of the total test samples, whereas it reaches 80.41% for single-modal photometric data. Moreover, the Root Mean Square (RMS) of |Δz| is shown to decreases from 0.1332 to 0.1235. Our method has the potential to be generalized to other astronomical data analyses such as galaxy classification and redshift prediction. The algorithm code can be found at https://github.com/HongShuxin/PhotoRedshift-MML .
