发表机构
Yunnan University; Yunnan Normal University; Yunnan University of Chinese Medicine（云南大学; 云南师范大学; 云南中医药大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究构建了35个机器学习模型的集成框架，在1/(1+z)空间估计费米耀变体红移，为1590个无红移源提供带不确定性的概率分布，恢复双峰结构，支持种群演化研究。
AI 中文摘要
大约一半由费米大面积望远镜（Fermi-LAT）探测到的耀变体缺乏光谱红移测量，这限制了对伽马射线耀变体群体的种群研究及其宇宙学演化的探究。我们的目标是开发一个多模型集成框架，系统评估机器学习模型在耀变体红移估计中的性能，并为所有未测量红移的耀变体提供带有量化不确定性的预测红移概率分布。我们在$1/(1+z)$空间中构建了一个包含35个机器学习回归模型的集成。各个模型的预测结果被组合起来，为每个源构建非参数红移概率密度函数（PDFs），从而明确表征预测不确定性。随后，源级PDFs被叠加以推导出种群级红移分布。在我们框架内的各种模型中，我们发现了可比较的预测性能，表明存在一个受特征限制的性能平台期。我们编制了一个包含1,590个未测量红移耀变体的预测红移分布目录。叠加的PDFs无需使用源类别标签即可恢复耀变体特征性的双峰红移结构。对于类型不确定的耀变体候选体，预测的红移分布呈现出双峰结构，表明类星体（FSRQ）类源的贡献略大。我们的多模型统计框架在保留微弱源的同时实现了具有竞争力的预测性能。我们为1,590个未测量红移的耀变体提供了带有量化不确定性的预测红移分布，为种群和演化研究提供了更好的支持。更广泛地说，这种方法凸显了在天文数据分析中采用不确定性感知的多模型回归集成的实用价值。
英文摘要
Approximately half of the Fermi-LAT-detected blazars lack spectroscopic redshift measurements, which limits population studies and investigations of the cosmological evolution of the gamma-ray blazar population. We aim to develop a multi-model ensemble framework to systematically evaluate the performance of machine-learning models for blazar redshift estimation and to provide predictive redshift probability distributions with quantified uncertainties for all blazars without measured redshifts. We construct an ensemble of 35 machine-learning regression models in the $1/(1+z)$ space. Predictions from individual models are combined to build non-parametric redshift probability density functions (PDFs) for each source, allowing an explicit characterization of predictive uncertainty. The source-level PDFs are subsequently stacked to derive population-level redshift distributions. Across the diverse models within our framework, we find comparable predictive performance, indicating a feature-limited performance plateau. We compile a catalog of predictive redshift distributions for 1,590 blazars without measured redshifts. The stacked PDFs recover the characteristic double-peaked redshift structure of blazars without using source class labels. For blazar candidates of uncertain type, the predicted redshift distribution exhibits a bimodal structure, suggesting a slightly larger contribution from FSRQ-like sources. Our multi-model statistical framework achieves competitive predictive performance while retaining faint sources. We provide predicted redshift distributions with quantified uncertainties for 1,590 blazars without measured redshifts, offering improved support for population and evolutionary studies. More broadly, this approach highlights the practical value of uncertainty-aware multi-model regression ensembling in astronomical data analysis.
Comments13 pages, 7 figures. Accepted for publication in Astronomy & Astrophysics
