AI 中文总结
研究利用不确定性感知变压器架构，通过AGNFormer模型重建AGN光谱，输入光谱通量和不确定性，经测试集评估性能，能高精度重建未观测宽线，再现光谱多样性，优于现有算法，助力提取AGN光谱信息。
AI 中文摘要
我们探索了一种基于不确定性感知变压器的架构如何利用活跃星系核（AGN）整个观测光谱中嵌入的信息，重点关注该算法预测发光AGN光谱中未观测到或被掩盖部分的能力。这直接探究了AGN连续谱与宽线之间的可学习相关性。我们引入了AGNFormer，这是一种变压器模型，经过训练可预测掩蔽光谱区域（主要宽线至\({\pm}10^{4}\)千米每秒\(^{-1}\)；缺失的一半）中的平均预期通量和方差，输入斯隆数字巡天DR16类星体目录整个红移范围内的静止帧光谱通量和不确定性。我们使用负对数似然评估该模型在全光谱（无信噪比限制）和高质量（信噪比>10）光谱样本上的性能，并与现有的C IV和ly - a重建算法进行比较。该模型成功重建了未观测到的AGN宽线，对于全光谱（信噪比>10）测试集，重建精度优于通量的10 - 16%（4 - 8%），在信噪比约为40时，通量误差下限约为2 - 6%，而对于距离可见输入光谱截止波长越远的更大未观测到的一半，预测误差增长到通量的12 - 25%（5 - 15%）。预测结果忠实地再现了整个光学和紫外类星体主序列参数空间中广泛的AGN光谱多样性，包括高斯和洛伦兹轮廓区域、Feii复合体和窄发射线。与先前的光谱重建算法相比，性能相似或更好。宽线区域重建的高精度表明该方法成功地整合了光谱信息，并突出了AGN连续谱和较弱谱线/复合体在帮助天文学家提取AGN光谱中所有信息方面的潜力。
英文摘要
We explore how an uncertainty-aware transformer-based architecture can leverage information embedded across the entire observed optical spectra of AGN, focusing on the algorithm's ability to predict unseen or masked parts of luminous AGN spectra. This provides a direct probe of the learnable correlations between AGN continua and broad lines. We introduce AGNFormer, a transformer model trained to predict the mean expected flux and variance in masked spectral regions (major broad lines to ${\pm}10^{4}$kms$^{-1}$; missing halves), inputting rest-frame spectral fluxes and uncertainties across the entire redshift range of the SDSS DR16 Quasar Catalogue. We evaluate the performance of the model on both full (no S/N limit) and high-quality (S/N > 10) spectral samples using the negative-log likelihood, and via comparisons with existing C IV and ly-a reconstruction algorithms. The model successfully reconstructs unseen AGN broad lines to better than 10-16% (4-8%) of the flux for the full (S/N > 10) test sets, up to an error floor of $\approx$2-6% of the flux at S/N $\approx$ 40, while predictions for larger unseen halves grow to 12-25% (5-15%) of the flux the further away they are from the cut-off wavelength of the seen input spectrum. Predictions faithfully reproduce the broad AGN spectral diversity across the entire optical and UV QSO main sequence parameter spaces, including both Gaussian and Lorentzian profile regimes, Feii complexes, and narrow emission lines. Performance is similar or better compared to previous spectral reconstruction algorithms. The high precision of the broad-line region reconstruction demonstrates that the method successfully aggregates information across the spectrum and highlights how the AGN continuum and weaker lines/complexes have the potential to assist astronomers in the extraction of the entire wealth of information embedded in AGN spectra.
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