AI 中文总结
研究针对AGN测光红移估计难题，提出VAR-PZnn框架，整合多源数据特征。在大量样本上训练测试，结果显示其精度高，消融研究明确各数据作用，还通过对比展现优势，为LSST提供了可扩展的测光红移估计方法。
AI 中文摘要
活动星系核（AGN）的测光红移估计对当前及未来大规模测光巡天仍是一项基本挑战。传统光谱能量分布（SED）拟合存在颜色 - 红移简并问题，尤其对于幂律连续谱掩盖了锚定红移估计所需强光谱特征的AGN。虽然AGN变异性提供了额外约束能力，但现有框架需要多波段光变曲线，而这并非总是可得。本文提出VAR-PZnn，一个全连接混合密度网络，它整合了从ZTF g波段光变曲线提取的26个变异性特征，以及来自Pan-STARRS1的光学测光、CatWISE的中红外（MIR）测光，对部分样本还有UKIDSS的近红外（NIR）测光。该模型在72728个光谱确认的AGN/QSO上进行训练和测试，范围为0.01 < z < 4.5且g波段星等为17至21.5。对于主样本，我们得到\(\sigma_{NMAD}=0.058\)，异常值比例\(\eta = 8.2\%\)，排除预测不确定性最高的10%源后降至5.4%。消融研究表明MIR测光对测光红移精度提供主要约束，变异性特征起次要细化作用。用UKIDSS NIR数据代表LSST与欧几里得和罗曼等天基任务的未来协同效应，无MIR数据时\(\eta = 13.3\%\)，有MIR时\(\eta = 4.6\%\)。我们与低分辨率模板（LRT）SED拟合（\(\eta = 28.7\%\)）和VAR-PZ框架进行基准测试；应用单波段VAR-PZ先验因单波段光变曲线简并使LRT性能恶化至\(\eta = 39.4\%\)，模拟证实（\(\eta = 27.6\%\)至28.1%）。该框架为时空遗产调查（LSST）提供了一种可扩展方法。
英文摘要
Photometric redshift estimation for active galactic nuclei (AGNs) remains a fundamental challenge for current and upcoming large-scale photometric surveys. Traditional spectral energy distribution (SED) fitting suffers from color-redshift degeneracies, particularly for AGNs whose power-law continua hide the strong spectral features required to anchor redshift estimates. While AGN variability provides additional constraining power, existing frameworks require multi-band light curves that are not always available. This work presents VAR-PZnn, a fully connected mixture density network that integrates 26 variability features extracted from ZTF g-band light curves with optical photometry from Pan-STARRS1, mid-infrared (MIR) photometry from CatWISE, and, for a subsample, NIR photometry from UKIDSS. The model is trained and tested on 72,728 spectroscopically confirmed AGNs/QSOs spanning 0.01 < z < 4.5 and g-band magnitudes from 17 to 21.5. For the main sample, we achieve σ_{NMAD} = 0.058 and an outlier fraction of η= 8.2%, which reduces to 5.4% when the 10% of sources with the highest predicted uncertainty are excluded. An ablation study demonstrates that MIR photometry provides the dominant constraint for photo-z accuracy, while variability features serve as a secondary refiner. Using UKIDSS NIR data as a proxy for future synergies between LSST and space-based missions like Euclid and Roman, we obtain η= 13.3% without MIR data and η= 4.6% when MIR is available. We benchmark against Low-Resolution Templates (LRT) SED fitting (η= 28.7%) and the VAR-PZ framework; applying single-band VAR-PZ priors worsens LRT performance to η= 39.4% due to single-band light-curve degeneracies, confirmed via simulations (η= 27.6% to 28.1%). This framework provides a scalable approach for the Legacy Survey of Space and Time (LSST).
Comments18 pages, 12 figures, 4 tables, Submitted to Astronomy & Astrophysics Journal
