论文标题
BLAST-大部分同步加速器峰的机器学习估计器
BlaST -- A Machine-Learning Estimator for the Synchrotron Peak of Blazars
论文作者
论文摘要
活跃的星系指向我们，所谓的大麻，在高能天体物理学领域起着重要作用。 Blazars分类方案中最重要的特征之一是光谱能量分布（SED）中同步器发射的峰值频率（$ν_{\ rm peak}^s $）。与通常计算$ν_ {\ rm peak}^s $的标准Blazar目录相反，我们开发了一种机器学习算法 - BLAST-不仅可以简化估计，而且还提供了可靠的不确定性评估。此外，它自然地是宿主星系和磁盘排放的其他SED组件，这可能是混乱的主要来源。使用我们的工具，我们重新估计Fermi 4LAC-DR2目录中的同步加速器峰。我们发现，爆炸，改善$ν_ {\ rm peak}^s $估计，尤其是在与喷气机不相关的组件的贡献很重要的情况下。
Active Galaxies with a jet pointing towards us, so-called blazars, play an important role in the field of high-energy astrophysics. One of the most important features in the classification scheme of blazars is the peak frequency of the synchrotron emission ($ν_{\rm peak}^S$) in the spectral energy distribution (SED). In contrast to standard blazar catalogs that usually calculate the $ν_{\rm peak}^S$ manually, we have developed a machine-learning algorithm - BlaST - that not only simplifies the estimation, but also provides a reliable uncertainty evaluation. Furthermore, it naturally accounts for additional SED components from the host galaxy and the disk emission, which may be a major source of confusion. Using our tool, we re-estimate the synchrotron peaks in the Fermi 4LAC-DR2 catalog. We find that BlaST, improves the $ν_{\rm peak}^S$ estimation especially in those cases where the contribution of components not related to the jet is important.
