Galaxy mergers play an important role in the growth of galaxies and their supermassive black holes. Simulations suggest that tidal interactions could enhance black hole accretion, which can be tested by the fraction of binary active galactic nuclei (
AGNs) among galaxy mergers. But determining the fraction requires a statistical sample of binaries. We have identified kpc-scale binary AGNs directly from high-resolution radio imaging. Inside the 92 square deg covered by the high-resolution Very Large Array survey of the Sloan Digital Sky Survey (SDSS) Stripe 82 field, we identified 22 grade A and 30 grade B candidates of binary radio AGNs with angular separations less than 5 (10 kpc at z = 0.1). Eight of the candidates have optical spectra for both components from the SDSS spectroscopic surveys and our Keck program. Two grade B candidates are projected pairs, but the remaining six candidates are all compelling cases of binary AGNs based on either emission line ratios or the excess in radio power compared to the H-alpha-traced star formation rate. Only two of the six binaries were previously discovered by an optical spectroscopic search. Based on these results, we estimate that ~60% of our binary candidates would be confirmed once we obtain complete spectroscopic information. We conclude that wide-area high-resolution radio surveys offer an efficient method to identify large samples of binary AGNs. These radio-selected binary AGNs complement binaries identified at other wavelengths and are useful for understanding the triggering mechanisms of black hole accretion.
The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that
must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arise in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.
An automated, rapid classification of transient events detected in the modern synoptic sky surveys is essential for their scientific utility and effective follow-up using scarce resources. This presents some unusual challenges: the data are sparse, h
eterogeneous and incomplete; evolving in time; and most of the relevant information comes not from the data stream itself, but from a variety of archival data and contextual information (spatial, temporal, and multi-wavelength). We are exploring a variety of novel techniques, mostly Bayesian, to respond to these challenges, using the ongoing CRTS sky survey as a testbed. The current surveys are already overwhelming our ability to effectively follow all of the potentially interesting events, and these challenges will grow by orders of magnitude over the next decade as the more ambitious sky surveys get under way. While we focus on an application in a specific domain (astrophysics), these challenges are more broadly relevant for event or anomaly detection and knowledge discovery in massive data streams.
The time domain has been identified as one of the most important areas of astronomical research for the next decade. The Virtual Observatory is in the vanguard with dedicated tools and services that enable and facilitate the discovery, dissemination
and analysis of time domain data. These range in scope from rapid notifications of time-critical astronomical transients to annotating long-term variables with the latest modeling results. In this paper, we will review the prior art in these areas and focus on the capabilities that the VAO is bringing to bear in support of time domain science. In particular, we will focus on the issues involved with the heterogeneous collections of (ancillary) data associated with astronomical transients, and the time series characterization and classification tools required by the next generation of sky surveys, such as LSST and SKA.
We present an updated determination of the z ~ 4 QSO luminosity function (QLF), improving the quality of the determination of the faint end of the QLF presented in Glikman et al. (2010). We have observed an additional 43 candidates from our survey sa
mple, yielding one additional QSO at z = 4.23 and increasing the completeness of our spectroscopic follow-up to 48% for candidates brighter than R = 24 over our survey area of 3.76 deg2. We study the effect of using K-corrections to compute the rest-frame absolute magnitude at 1450A compared with measuring M1450 directly from the object spectra. We find a luminosity-dependent bias: template-based K-corrections overestimate the luminosity of low-luminosity QSOs, likely due to their reliance on templates derived from higher luminosity QSOs. Combining our sample with bright quasars from the Sloan Digital Sky Survey and using spectrum-based M1450 for all the quasars, we fit a double-power-law to the binned QLF. Our best fit has a bright-end slope, {alpha} = 3.3pm0.2, and faint-end slope, {beta} = 1.6(+0.8/-0.6). Our new data revise the faint-end slope of the QLF down to flatter values similar to those measured at z ~ 3. The break luminosity, though poorly constrained, is at M* = -24.1(+0.7/-1.9), approximately 1 - 1.5 mag fainter than at z ~ 3. This QLF implies that QSOs account for about half the radiation needed to ionize the IGM at these redshifts.
We conducted an exploratory search for quasars at z~ 6 - 8, using the Early Data Release from United Kingdom Infrared Deep Sky survey (UKIDSS) cross-matched to panoramic optical imagery. High redshift quasar candidates are chosen using multi-color se
lection in i,z,Y,J,H and K bands. After removal of apparent instrumental artifacts, our candidate list consisted of 34 objects. We further refined this list with deeper imaging in the optical for ten of our candidates. Twenty-five candidates were followed up spectroscopically in the near-infrared and in the optical. We confirmed twenty-five of our spectra as very low-mass main-sequence stars or brown dwarfs, which were indeed expected as the main contaminants of this exploratory search. The lack of quasar detection is not surprising: the estimated probability of finding a single z>6 quasar down to the limit of UKIDSS in the 27.3 square degrees of the EDR is <5%. We find that the most important limiting factor in this work is the depth of the available optical data. Experience gained in this pilot project can help refine high-redshift quasar selection criteria for subsequent UKIDSS data releases.
