The AGK3U is a updated version of the AGK3 catalog in which new positions from the Palomar "Quick V" survey have been added to improve the AGK3 proper motions. It provides FK4/B1950.0 positions and proper motions for 170,464 stars north of -2.5 degrees declination, at an average epoch of 1950.62. The proper motions have a two dimensional formal mean error of 0.82 arcsec/century. In addition to the positions and proper motions, the catalog contains the AGK3 number, the mean errors of the positions and proper motion in each coordinate, the photographic magnitude, the spectral type, and the Palomar plate position, epoch, and mean error.
We perform a global fit to ~5000 radial velocity and ~177000 magnitude measurements in 29 photometric bands covering 0.3{mu}m to 8.0{mu}m distributed among 287 Galactic, Large Magellanic Cloud, and Small Magellanic Cloud Cepheids with P>10 days. We assume that the Cepheid light curves and radial velocities are fully characterized by distance, reddening, and time-dependent radius and temperature variations. We construct phase curves of radius and temperature for periods between 10 and 100 days, which yield light-curve templates for all our photometric bands and can be easily generalized to any additional band. With only four to six parameters per Cepheid, depending on the existence of velocity data and the amount of freedom in the distance, the models have typical rms light and velocity curve residuals of 0.05mag and 3.5km/s. The model derives the mean Cepheid spectral energy distribution and its derivative with respect to temperature, which deviate from a blackbody in agreement with metal-line and molecular opacity effects. We determine a mean reddening law toward the Cepheids in our sample, which is not consistent with standard assumptions in either the optical or near-IR. Based on stellar atmosphere models, we predict the biases in distance, reddening, and temperature determinations due to the metallicity and quantify the metallicity signature expected for our fit residuals.
We investigate the role of host galaxy classification and black hole mass (MBH) in a heterogeneous sample of 276 mostly nearby (z<0.1) X-ray and IR-selected active galactic nuclei (AGN).
We discuss the nature and origin of the nuclear activity observed in a sample of 292 SDSS narrow-emission-line galaxies, considered to have formed and evolved in isolation. All these galaxies are spiral like and show some kind of nuclear activity. The fraction of Narrow Line AGNs (NLAGNs) and Transition type Objects (TOs; a NLAGN with circumnuclear star formation) is relatively high, amounting to 64% of the galaxies. There is a definite trend for the NLAGNs to appear in early-type spirals, while the star forming galaxies and TOs are found in later-type spirals. We verify that the probability for a galaxy to show an AGN characteristic increases with the bulge mass of the galaxy (Torres-Papaqui et al. 2011), and find evidence that this trend is really a by-product of the morphology, suggesting that the AGN phenomenon is intimately connected with the formation process of the galaxies. Consistent with this interpretation, we establish a strong connection between the astration rate -- the efficiency with which the gas is transformed into stars - the AGN phenomenon, and the gravitational binding energy of the galaxies: the higher the binding energy, the higher the astration rate and the higher the probability to find an AGN. The NLAGNs in our sample are consistent with scaled-down or powered-down versions of quasars and Broad Line AGNs.
In this paper, we discuss an application of machine-learning-based methods to the identification of candidate active galactic nucleus (AGN) from optical survey data and to the automatic classification ofAGNs in broad classes. We applied four different machine-learning algorithms, namely the Multi Layer Perceptron, trained, respectively, with the Conjugate Gradient, the Scaled Conjugate Gradient, the Quasi Newton learning rules and the Support Vector Machines, Q4 to tackle the problem of the classification of emission line galaxies in different classes, mainly AGNs versus non-AGNs, obtained using optical photometry in place of the diagnostics based on line intensity ratios which are classically used in the literature. Using the same photometric features, we discuss also the behaviour of the classifiers on finer AGN classification tasks, namely Seyfert I versus Seyfert II, and Seyfert versus LINER. Furthermore, we describe the algorithms employed, the samples of spectroscopically classified galaxies used to train the algorithms, the procedure followed to select the photometric parameters and the performances of our methods in terms of multiple statistical indicators. The results of the experiments show that the application of self-adaptive data mining algorithms trained on spectroscopic data sets and applied to carefully chosen photometric parameters represents a viable alternative to the classical methods that employ time-consuming spectroscopic observations.
We report on the near-infrared-selected active galactic nuclei (AGN) candidates extracted from Two Micron All Sky Survey (2MASS)/ROSAT catalogues and discuss their properties. First, near-infrared counterparts of an X-ray source in ROSAT catalogues [namely bright source catalogue (BSC, Cat. IX/10) and faint source catalogue (FSC, Cat. IX/29)] were extracted by positional cross-identification of <=30arcsec. As these counterparts would contain many mis-identifications, we further imposed near-infrared colour selection criteria and extracted reliable AGN candidates (BSC: 5273, FSC: 10071). Of the 5273 (10071) candidates in the BSC (FSC), 2053 (1008) are known AGNs. Near-infrared and X-ray properties of candidates show similar properties with known AGNs and are consistent with the previous studies. We also searched for counterparts in other wavelengths (i.e. optical, near-infrared and radio) and investigated properties in multiwavelength. No significant difference between known AGNs and unclassified sources could be seen. However, some unclassified sources in the FSC showed slightly different properties compared with known AGNs. Consequently, it is highly probable that we could extract reliable AGN candidates, though candidates in the FSC might be spurious.
The second Fermi-LAT source catalog (2FGL) is the deepest all-sky survey available in the gamma-ray band. It contains 1873 sources, of which 576 remain unassociated. Machine-learning algorithms can be trained on the gamma-ray properties of known active galactic nuclei (AGNs) to find objects with AGN-like properties in the unassociated sample. This analysis finds 231 high-confidence AGN candidates, with increased robustness provided by intersecting two complementary algorithms. A method to estimate the performance of the classification algorithm is also presented, that takes into account the differences between associated and unassociated gamma-ray sources. Follow-up observations targeting AGN candidates, or studies of multiwavelength archival data, will reduce the number of unassociated gamma-ray sources and contribute to a more complete characterization of the population of gamma-ray emitting AGNs.
We have developed the "S_IX_" statistic to identify bright, highly likely active galactic nucleus (AGN) candidates solely on the basis of Wide-field Infrared Survey Explorer (WISE), Two Micron All-Sky Survey (2MASS), and ROSAT all-sky survey (RASS) data. This statistic was optimized with data from the preliminary WISE survey and the Sloan Digital Sky Survey, and tested with Lick 3m Kast spectroscopy. We find that sources with S_IX_<0 have a >~95% likelihood of being an AGN (defined in this paper as a Seyfert 1, quasar, or blazar). This statistic was then applied to the full WISE/2MASS/RASS dataset, including the final WISE data release, to yield the "W2R" sample of 4316 sources with S_IX_<0. Only 2209 of these sources are currently in the Veron-Cetty and Veron (VCV) catalog of spectroscopically confirmed AGNs, indicating that the W2R sample contains nearly 2000 new, relatively bright (J<~16) AGNs. We utilize the W2R sample to quantify biases and incompleteness in the VCV catalog. We find that it is highly complete for bright (J<14), northern AGNs, but the completeness drops below 50% for fainter, southern samples and for sources near the Galactic plane. This approach also led to the spectroscopic identification of 10 new AGNs in the Kepler field, more than doubling the number of AGNs being monitored by Kepler. The W2R sample contains better than 1 bright AGN every 10 deg^2^, permitting construction of AGN samples in any sufficiently large region of sky.
We present spectroscopic redshifts for the first 466 X-ray and radio-selected AGN targets in the 2deg^2^ COSMOS field. Spectra were obtained with the IMACS instrument on the Magellan (Baade) telescope, using the nod-and-shuffle technique. We identify a variety of type 1 and type 2 AGNs, as well as red galaxies with no emission lines. Our redshift yield is 72% down to i_AB_=24, although the yield is >90% for i_AB_<22. We expect the completeness to increase as the survey continues. When our survey is complete and additional redshifts from the zCOSMOS project are included, we anticipate ~1100 AGNs with redshifts over the entire COSMOS field. Our redshift survey is consistent with an obscured AGN population that peaks at z~0.7, although further work is necessary to disentangle the selection effects.
The North Ecliptic Pole (NEP) field provides a unique set of panchromatic data, well suited for active galactic nuclei (AGN) studies. Selection of AGN candidates is often based on mid-infrared (MIR) measurements. Such method, despite its effectiveness, strongly reduces a catalog volume due to the MIR detection condition. Modern machine learning techniques can solve this problem by finding similar selection criteria using only optical and near-infrared (NIR) data. Aims of this work were to create a reliable AGN candidates catalog from the NEP field using a combination of optical SUBARU/HSC and NIR AKARI/IRC data and, consequently, to develop an efficient alternative for the MIR-based AKARI/IRC selection technique. A set of supervised machine learning algorithms was tested in order to perform an efficient AGN selection. Best of the models were formed into a majority voting scheme, which used the most popular classification result to produce the final AGN catalog. Additional analysis of catalog properties was performed in form of the spectral energy distribution (SED) fitting via the CIGALE software. The obtained catalog of 465 AGN candidates (out of 33119 objects) is characterized by 73% purity and 64% completeness. This new classification shows consistency with the MIR-based selection. Moreover, 76% of the obtained catalog can be found only with the new method due to the lack of MIR detection for most of the new AGN candidates. Training data, codes and final catalog are available via the github repository. Final AGN candidates catalog is also available via the CDS service.
