The AGK3 provides positions and proper motions for stars north of -2.5 degrees. For the most part, it contains the stars in the AGK2 but is based on newly remeasured reference stars whose positions were reduced to the FK4 system. A list of 446 AGK2 stars not in the AGK3 and of three new stars is provided. All plates were taken at the Bergedorf Observatory. In addition to the positions and proper motions, the catalog contains magnitudes and spectral types, the epoch of the observations, the epoch difference between the AGK2 and AGK3, and the BD numbers.
A catalog of positions of reference stars distributed uniformly on the celestial sphere north of declination -5 deg. contains meridian observations of 21,499 stars with a mean epoch 1958.5. This catalog was compiled from more than 300,000 observations made with 11 northern meridian circles during the period 1956 to 1963. The observations are on the FK4 system.
The AGK3R and SRS are lists of reference stars containing, respectively, 21,499 stars in the northern hemisphere and 20,500 stars in the southern hemisphere. This paper presents computations of proper motions of these two groups of stars that will permit the use of the observed positions away from the epochs of observation. Tables are presented summarizing the mean errors of AGK3R proper motions and the observational histories of AGK3R and SRS stars.
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.
