UnEZ：全无监督神经网络从光谱直接估计星系红移与谱线强度
UnEZ: A Fully Unsupervised Self-Templating Neural Network for Redshift and Line Strength Estimation
天文学和机器学习交叉的论文：一个完全不用标签的神经网络，靠光谱本身学出红移，在 SDSS DR18 上表现接近现有算法，做巡天数据处理的朋友可以看看。
UnEZ 是一个全无监督、物理约束的机器学习模型，无需红移或谱线强度标签即可预测光谱红移和谱线强度。其编码器预测发射线和吸收线的振幅与宽度，固定的确定性解码器生成模板光谱、红移估计及置信曲线。模型在 SDSS Data Release 18 上测试，在 SNR > 10 条件下异常值比例为 0.459%，NMAD 达 1.237×10⁻⁴，在 SNR > 3 条件下异常值比例为 3.89%。与传统依赖模板光谱的方法不同，UnEZ 完全不依赖预置模板或人工整理的红移目录，还能输出传统红移估计算法所需的模板光谱。
UnEZ: A Fully Unsupervised Self-Templating Neural Network for Redshift and Line Strength Estimation
As spectroscopic surveys grow, estimating galaxy properties such as metallicity, star formation rate, and redshift becomes computationally cumbersome. Methods of estimating such properties rely on template spectra or curated redshift catalogs, which fail to represent the diversity of objects seen in large-scale surveys. We present UnEZ, a fully unsupervised, physics-informed machine learning model that can predict spectroscopic redshifts and line strengths with no prior redshift or line strength labels. An encoder predicts emission and absorption line amplitudes/widths, and a fixed deterministic decoder constructs a unique template, estimated redshift, redshift confidence curve, and spectral model. UnEZ was tested on the Sloan Digital Sky Survey (SDSS) Data Release 18 due to its large dataset and thoroughly vetted redshift catalog which serves as an independent benchmark. Despite the lack of templates, line strength labels, or redshift labels, UnEZ's performance competes with modern redshift estimation algorithms. At SNR > 10, UnEZ achieves an outlier fraction of 0.459% and a normalized median absolute deviation (NMAD) of $1.237 \times 10^{-4}$. At SNR > 3, UnEZ achieves an outlier fraction of 3.89% and an NMAD of $1.471 \times 10^{-4}$. Furthermore, we demonstrate how UnEZ's line strength and redshift predictions can be utilized for various purposes such as constructing template spectra required in traditional redshift estimation algorithms. UnEZ illustrates how a fully unsupervised neural network can allow redshift, among other galaxy properties, to emerge naturally from the spectra themselves, providing a template-free, label-free approach that scales to the demands of future surveys.