We report the systematic analysis of knots, hotspots, and lobes in 57 active galactic nuclei (AGNs) to investigate the variation of the magnetic field along with the jet from the sub-pc base to the terminus in kpc-to-Mpc scales. Expanding the number
of radio/X-ray samples in Kataoka & Stawarz (2005), we analyzed the data in 12 FR I and 30 FR II radio galaxies, 12 quasars, and 3 BL Lacs that contained 76 knots, 42 hotspots, and 29 radio lobes. We first derived the equipartition magnetic fields in the cores and then estimated those in various jet components by assuming $B_{rm est}$ $propto$ $d^{-1}$, where $d$ is the distance from the jet base. On the other hand, the magnetic field in large-scale jets (knots, hotspots, and lobes), $B_{rm eq}$, can be estimated from the observed flux and spatial extent under the equipartition hypothesis. We show that the magnetic field decreases as the distance along the jet increases, but generally gentler than $propto d^{-1}$. The increase in $B_{rm eq}/B_{rm est}$ at a larger $d$ may suggest the deceleration of the jet around the downstream, but there is no difference between FR I and FR II jets. Moreover, the magnetic fields in the hotspots are systematically larger than those of knots and lobes. Finally, we applied the same analysis to knots and lobes in Centaurus A to check whether the above discussion will hold even in a single jet source.
A novel application of machine-learning (ML) based image processing algorithms is proposed to analyze an all-sky map (ASM) obtained using the Fermi Gamma-ray Space Telescope. An attempt was made to simulate a one-year ASM from a short-exposure ASM ge
nerated from one-week observation by applying three ML based image processing algorithms: dictionary learning, U-net, and Noise2Noise. Although the inference based on ML is less clear compared to standard likelihood analysis, the quality of the ASM was generally improved. In particular, the complicated diffuse emission associated with the galactic plane was successfully reproduced only from one-week observation data to mimic a ground truth (GT) generated from a one-year observation. Such ML algorithms can be implemented relatively easily to provide sharper images without various assumptions of emission models. In contrast, large deviations between simulated ML maps and GT map were found, which are attributed to the significant temporal variability of blazar-type active galactic nuclei (AGNs) over a year. Thus, the proposed ML methods are viable not only to improve the image quality of an ASM, but also to detect variable sources, such as AGNs, algorithmically, i.e., without human bias. Moreover, we argue that this approach is widely applicable to ASMs obtained by various other missions; thus, it has the potential to examine giant structures and transient events, both of which are rarely found in pointing observations.
