We present the results of multi-epoch, multi-frequency monitoring of a blazar 4C +29.45, which was regularly monitored as part of the Interferometric Monitoring of GAmma-ray Bright Active Galactic Nuclei (iMOGABA) program - a key science program of the Korean Very long baseline interferometry Network (KVN). Observations were conducted simultaneously at 22, 43, 86, and 129 GHz during the 4 years from 5 December 2012 to 28 December 2016. We also used additional data from the 15 GHz Owens Valley Radio Observatory (OVRO) monitoring program. From the 15 GHz light curve, we estimated the variability time scales of the source during several radio flux enhancements. We found that the source experienced 6 radio flux enhancements with variability time scales of 9-187 days during the observing period, yielding corresponding variability Doppler factors of 9-27. From the multi-frequency simultaneous KVN observations, we were able to obtain accurate radio spectra of the source and hence to more precisely measure the turnover frequencies $ν_{\rm r}$ of synchrotron self-absorbed (SSA) emission with a mean value of $ν_{rm r}$ = 28.9 GHz. Using jet geometry assumptions, we estimated the size of the emitting region at the turnover frequency. We found that the equipartition magnetic field strength is up to two orders of magnitudes higher than the SSA magnetic field strength (0.001-0.1 G). This is consistent with the source being particle dominated. We performed a careful analysis of the systematic errors related to making these estimations. From the results, we concluded that the equipartition region locates upstream the SSA region.
Discovery of ultra-fast outflows with v$_{\rm out}>0.3 \rm c$ in local bright active galactic nuclei
Università di Bologna（博洛尼亚大学）；INAF – OAS, Osservatorio di Astrofisica e Scienza dello Spazio Bologna（意大利国家天体物理研究所-波伦亚天文与空间科学观测站）；European Space Agency (ESA)（欧洲航天局）；European Space Research and Technology Centre (ESTEC)（欧洲航天研究与技术中心）；INAF – Osservatorio Astronomico di Roma（意大利国家天体物理研究所-罗马天文台）；INAF – Istituto di Astrofisica e Planetologia Spaziali（意大利国家天体物理研究所-空间天体物理学与行星科学研究所）；Astronomical Institute of the Czech Academy of Sciences（捷克科学院天文研究所）
Predicting the redshift of gamma-ray loud AGNs using supervised machine learning
活动星系核是非常强大的星系，其特征是从其中心的大质量黑洞发出极其明亮的辐射。了解AGN的红移为我们提供了一个机会，以确定它们的距离，以研究重要的天体物理问题，如早期恒星的演化，它们的形成以及早期星系的结构。红移的测定是具有挑战性的，因为它需要对多波长观测进行详细的跟踪，通常涉及各种天文设施。在这里，我们采用机器学习算法来估计红移从观测到的伽马射线属性和光度数据的伽马射线响亮AGN从第四费米-LAT目录。使用LASSO选择的预测器集，通过超级学习器算法获得预测。我们得到了一个紧密的相关性，推断的红移和观测到的红移之间的皮尔逊相关系数为71.3%，平均Δz_norm = 11.6 x 10^-4。我们强调，尽管伽马射线响亮AGN的小样本，我们获得了一个可靠的预测模型，使用超级学习器，这是一个集成的几个机器学习模型。
