Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userRadio-AGN in the AKARI-NEP field and their role in the evolution of galaxies
203
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Radio-loud active galaxies have been found to exhibit a close connection to galactic mergers and host galaxy star-formation quenching. We present preliminary results of an optical spectroscopic investigation of the AKARI NEP field. We focus on the population of radio-loud AGN and use photometric and spectroscopic information to study both their star-formation and nuclear activity components. Preliminary results show that radio-AGN are associated with early type, massive galaxies with relatively old stellar populations.
rate research
Read More
We study the host galaxy properties of radio sources in the AKARI-North Ecliptic Pole (NEP) field, using an ensemble of multi-wavelength datasets. We identify both radio-loud and radio-quiet AGN and study their host galaxy properties by means of SED fitting. We investigate the relative importance of nuclear and star-formation activity in radio-AGN and assess the role of radio-AGN as efficient quenchers of star-formation in their host galaxies.
Particle acceleration in relativistic jets to very high energies occurs at the expense of the dissipation of magnetic or kinetic energy. Therefore, understanding the processes that can trigger this dissipation is key to the characterization of the energy budgets and particle acceleration mechanisms at action in active galaxies. Instabilities and entrainment are two obvious candidates to trigger dissipation. On the one hand, supersonic, relativistic flows threaded by helical fields, as expected from the standard formation models of jets in supermassive black-holes, are unstable to a series of magnetohydrodynamical instabilities, such as the Kelvin-Helmholtz, current-driven, or possibly the pressure-driven instabilities. Furthermore, in the case of expanding jets, the Rayleigh-Taylor and centrifugal instabilities may also develop. With all these destabilizing processes at action, a natural question is how can some jets keep their collimated structure along hundreds of kiloparsecs. On the other hand, the interaction of the jet with stars and clouds of gas that cross the flow in their orbits around the galactic centers provides another scenario in which kinetic energy can be efficiently converted into internal energy and particles can be accelerated to non-thermal energies. In this contribution, I review the conditions under which these processes occur and their role both in jet evolution and propagation and energy dissipation.
The aim of this work is to create a new catalog of reliable AGN candidates selected from the AKARI NEP-Deep field. Selection of the AGN candidates was done by applying a fuzzy SVM algorithm, which allows to incorporate measurement uncertainties into the classification process. The training dataset was based on the spectroscopic data available for selected objects in the NEP-Deep and NEP-Wide fields. The generalization sample was based on the AKARI NEP-Deep field data including objects without optical counterparts and making use of the infrared information only. A high quality catalog of previously unclassified 275 AGN candidates was prepared.
Dust-obscured star-formation becomes much more important with increasing intensity, and increasing redshift. We aim to reveal cosmic star-formation history obscured by dust using deep infrared observation with the AKARI. We construct restframe 8um, 12um, and total infrared (TIR) luminosity functions (LFs) at 0.15<z<2.2 using 4128 infrared sources in the AKARI NEP-Deep field. A continuous filter coverage in the mid-IR wavelength (2.4, 3.2, 4.1, 7, 9, 11, 15, 18, and 24um) by the AKARI satellite allows us to estimate restframe 8um and 12um luminosities without using a large extrapolation based on a SED fit, which was the largest uncertainty in previous work. We have found that all 8um (0.38<z<2.2), 12um (0.15<z<1.16), and TIR LFs (0.2<z<1.6), show a continuous and strong evolution toward higher redshift. In terms of cosmic infrared luminosity density (Omega_IR), which was obtained by integrating analytic fits to the LFs, we found a good agreement with previous work at z<1.2, and that the Omega_IR evolves as propto (1+z)^4.4+-1.0. When we separate contributions to Omega_IR by LIRGs and ULIRGs, we found more IR luminous sources are increasingly more important at higher redshift. We found that the ULIRG (LIRG) contribution increases by a factor of 10 (1.8) from z=0.35 to z=1.4.
Context: It is crucial to develop a method for classifying objects detected in deep surveys at infrared wavelengths. We specifically need a method to separate galaxies from stars using only the infrared information to study the properties of galaxies, e.g., to estimate the angular correlation function, without introducing any additional bias. Aims. We aim to separate stars and galaxies in the data from the AKARI North Ecliptic Pole (NEP) Deep survey collected in nine AKARI / IRC bands from 2 to 24 {mu}m that cover the near- and mid-infrared wavelengths (hereafter NIR and MIR). We plan to estimate the correlation function for NIR and MIR galaxies from a sample selected according to our criteria in future research. Methods: We used support vector machines (SVM) to study the distribution of stars and galaxies in the AKARIs multicolor space. We defined the training samples of these objects by calculating their infrared stellarity parameter (sgc). We created the most efficient classifier and then tested it on the whole sample. We confirmed the developed separation with auxiliary optical data obtained by the Subaru telescope and by creating Euclidean normalized number count plots. Results: We obtain a 90% accuracy in pinpointing galaxies and 98% accuracy for stars in infrared multicolor space with the infrared SVM classifier. The source counts and comparison with the optical data (with a consistency of 65% for selecting stars and 96% for galaxies) confirm that our star/galaxy separation methods are reliable. Conclusions: The infrared classifier derived with the SVM method based on infrared sgc- selected training samples proves to be very efficient and accurate in selecting stars and galaxies in deep surveys at infrared wavelengths carried out without any previous target object selection.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
