分类： 天文学 >> 天文学 提交时间： 2023-02-19
Hiroki Okino
Kazunori Akiyama
Keiichi Asada
José L. Gómez
Kazuhiro Hada
Mareki Honma
Thomas P. Krichbaum
Motoki Kino
Hiroshi Nagai
Uwe Bach
Lindy Blackburn
Katherine L. Bouman
Andrew Chael
Geoffrey B. Crew
Sheperd S. Doeleman
Vincent L. Fish
Ciriaco Goddi
Sara Issaoun
Michael D. Johnson
Svetlana Jorstad
摘要： The collimation of relativistic jets launched from the vicinity of supermassive black holes (SMBHs) at the centers of active galactic nuclei (AGN) is one of the key questions to understand the nature of AGN jets. However, little is known about the detailed jet structure for AGN like quasars since very high angular resolutions are required to resolve these objects. We present very long baseline interferometry (VLBI) observations of the archetypical quasar 3C 273 at 86 GHz, performed with the Global Millimeter VLBI Array, for the first time including the Atacama Large Millimeter/submillimeter Array. Our observations achieve a high angular resolution down to $\sim$60 ${\rm \mu}$as, resolving the innermost part of the jet ever on scales of $\sim 10^5$ Schwarzschild radii. Our observations, including close-in-time High Sensitivity Array observations of 3C 273 at 15, 22, and 43 GHz, suggest that the inner jet collimates parabolically, while the outer jet expands conically, similar to jets from other nearby low luminosity AGN. We discovered the jet collimation break around $10^{7}$ Schwarzschild radii, providing the first compelling evidence for structural transition in a quasar jet. The location of the collimation break for 3C 273 is farther downstream the sphere of gravitational influence (SGI) from the central SMBH. With the results for other AGN jets, our results show that the end of the collimation zone in AGN jets is governed not only by the SGI of the SMBH but also by the more diverse properties of the central nuclei.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： We introduce a new library of 535,194 model images of the supermassive black holes and Event Horizon Telescope (EHT) targets Sgr A* and M87*, computed by performing general relativistic radiative transfer calculations on general relativistic magnetohydrodynamics simulations. Then, to infer underlying black hole and accretion flow parameters (spin, inclination, ion-to-electron temperature ratio, and magnetic field polarity), we train a random forest machine learning model on various hand-picked polarimetric observables computed from each image. Our random forest is capable of making meaningful predictions of spin, inclination, and the ion-to-electron temperature ratio, but has more difficulty inferring magnetic field polarity. To disentangle how physical parameters are encoded in different observables, we apply two different metrics to rank the importance of each observable at inferring each physical parameter. Details of the spatially resolved linear polarization morphology stand out as important discriminators between models. Bearing in mind the theoretical limitations and incompleteness of our image library, for the real M87* data, our machinery favours high-spin retrograde models with large ion-to-electron temperature ratios. Due to the time-variable nature of these targets, repeated polarimetric imaging will further improve model inference as the EHT and next-generation (EHT) continue to develop and monitor their targets.
点击量
待评议
点击量
下载量
评论
