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arXiv 2201.05374astro-ph.HEastro-ph.GAastro-ph.IM
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使用监督机器学习预测gamma-ray大声活动星系核的红移:第2部分

Predicting the redshift of gamma-ray loud AGNs using Supervised Machine Learning: Part 2

Aditya Narendra, Spencer James Gibson, Maria Giovanna Dainotti, Malgorzata Bogdan, Agnieszka Pollo, Ioannis Liodakis, Artem Poliszczuk

摘要:

测量活动星系核(AGN)的红移需要使用耗时和昂贵的光谱分析。然而,获得活动星系核的红移测量是至关重要的,因为它可以使活动星系核的人口研究,提供洞察恒星形成率,光度函数,和密度率演化。因此,需要替代的红移测量技术。在这个项目中,我们的目标是使用费米伽马射线空间望远镜的4LAC数据发布(DR 2)目录训练机器学习模型能够可靠地预测红移。此外,该项目旨在通过新的4LAC目录改进和扩展Dainotti等人(2021)发表的机器学习(ML)方法的预测能力。此外,我们实施的特征工程,以扩大参数空间和偏差校正技术,我们的最终结果。这项研究使用了额外的机器学习技术集成方法,SuperLearner,以前在Dainotti等人。(2021年)。此外,我们还测试了一种名为Sorted L-One Penalized Estimation(SLOPE)的新型ML模型。使用这些方法,我们提供了一个目录的估计红移值的活动星系核,没有光谱红移测量。这些估计可以作为一个红移参考社区验证更新的费米目录发布更多的红移测量。

英文摘要:

Measuring the redshift of active galactic nuclei (AGNs) requires the use of time-consuming and expensive spectroscopic analysis. However, obtaining redshift measurements of AGNs is crucial as it can enable AGN population studies, provide insight into the star formation rate, the luminosity function, and the density rate evolution. Hence, there is a requirement for alternative redshift measurement techniques. In this project, we aim to use the Fermi gamma-ray space telescope's 4LAC Data Release (DR2) catalog to train a machine learning model capable of predicting the redshift reliably. In addition, this project aims at improving and extending with the new 4LAC Catalog the predictive capabilities of the machine learning (ML) methodology published in Dainotti et al. (2021). Furthermore, we implement feature engineering to expand the parameter space and a bias correction technique to our final results. This study uses additional machine learning techniques inside the ensemble method, the SuperLearner, previously used in Dainotti et al.(2021). Additionally, we also test a novel ML model called Sorted L-One Penalized Estimation (SLOPE). Using these methods we provide a catalog of estimated redshift values for those AGNs that do not have a spectroscopic redshift measurement. These estimates can serve as a redshift reference for the community to verify as updated Fermi catalogs are released with more redshift measurements.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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