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Register a new userThe GALEX Time Domain Survey. II. Wavelength-Dependent Variability of Active Galactic Nuclei in the Pan-STARRS1 Medium Deep Survey
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We analyze the wavelength-dependent variability of a sample of spectroscopically confirmed active galactic nuclei (AGN) selected from near-UV ($NUV$) variable sources in the GALEX Time Domain Survey that have a large amplitude of optical variability (difference-flux S/N $>$ 3) in the Pan-STARRS1 Medium Deep Survey (PS1 MDS). By matching GALEX and PS1 epochs in 5 bands ($NUV$, $g_{P1}$, $r_{P1}$, $i_{P1}$, $z_{P1}$) in time, and taking their flux difference, we create co-temporal difference-flux spectral energy distributions ($Delta f$SEDs) using two chosen epochs for each of the 23 objects in our sample on timescales of about a year. We confirm the bluer-when-brighter trend reported in previous studies, and measure a median spectral index of the $Delta f$SEDs of $alpha_{lambda}$ = 2.1 that is consistent with an accretion disk spectrum. We further fit the $Delta f$SEDs of each source with a standard accretion disk model in which the accretion rate changes from one epoch to the other. In our sample, 17 out of 23 ($sim$74 %) sources are well described by this variable accretion-rate disk model, with a median average characteristic disk temperature $bar{T}^*$ of $1.2times 10^5$~K that is consistent with the temperatures expected given the distribution of accretion rates and black hole masses inferred for the sample. Our analysis also shows that the variable accretion rate model is a better fit to the $Delta f$SEDs than a simple power law.
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We study the properties of 975 active galactic nuclei (AGN) selected by variability in the Pan-STARRS1 Medium-Deep Survey. Using complementary multi wavelength data from the ultraviolet to the far-infrared, we use SED fitting to determine the AGN and host properties at $z<1$, and compare to a well-matched control sample. We confirm the trend previously observed that the variability amplitude decreases with AGN luminosity, but on the other hand, we observe that the slope of this relation steepens with wavelength resulting in a redder when brighter trend at low luminosities. Our results show that AGN are hosted by more massive hosts than control sample galaxies, while the restframe, dust-corrected $NUV-r$ color distribution of AGN hosts is similar to control galaxies. We find a positive correlation between the AGN luminosity and star formation rate (SFR), independent of redshift. AGN hosts populate the whole range of SFRs within and outside the Main Sequence of star forming galaxies. Comparing the distribution of AGN hosts and control galaxies, we show that AGN hosts are less likely to be hosted by quiescent galaxies, but more likely to be hosted by Main Sequence or starburst galaxies.
We present a systematic search for periodically varying quasar and supermassive black hole binary (SMBHB) candidates in the Pan-STARRS1 Medium Deep Survey. From $sim9,000$ color-selected quasars in a $sim50$ deg$^{2}$ sky area, we initially identify $26$ candidates with more than $1.5$ cycles of variation. We extend the baseline of observations via our imaging campaign with the Discovery Channel Telescope and the Las Cumbres Observatory network and reevaluate the candidates using a more rigorous, maximum likelihood method. Using a range of statistical criteria and assuming the Damped Random Walk model for normal quasar variability, we identify one statistically significant periodic candidate. We also investigate the capabilities of detecting SMBHBs by the Large Synoptic Survey Telescope using our study with MDS as a benchmark and explore any complementary, multiwavelength evidence for SMBHBs in our sample.
The Pan-STARRS1 (PS1) survey has obtained imaging in 5 bands (grizy_P1) over 10 Medium Deep Survey (MDS) fields covering a total of 70 square degrees. This paper describes the search for apparently hostless supernovae (SNe) within the first year of PS1 MDS data with an aim of discovering new superluminous supernovae (SLSNe). A total of 249 hostless transients were discovered down to a limiting magnitude of M_AB ~ 23.5, of which 76 were classified as Type Ia SNe. There were 57 SNe with complete light curves that are likely core-collapse SNe (CCSNe) or SLSNe and 12 of these have had spectra taken. Of these 12 hostless, non-Type Ia SNe, 7 were SLSNe of Type Ic at redshifts between 0.5-1.4. This illustrates that the discovery rate of Type Ic SLSNe can be maximised by concentrating on hostless transients and removing normal SNe Ia. We present data for two new possible SLSNe; PS1-10pm (z = 1.206) and PS1-10ahf (z = 1.1), and estimate the rate of SLSNe-Ic to be between 3^{+3}_{-2} * 10^{-5} and 8^{+2}_{-1} * 10^{-5} of the CCSNe rate within 0.3 <= z <= 1.4 by applying a Monte-Carlo technique. The rate of slowly evolving, SN2007bi-like explosions is estimated as a factor of 10 lower than this range.
Photometric classification of supernovae (SNe) is imperative as recent and upcoming optical time-domain surveys, such as the Large Synoptic Survey Telescope (LSST), overwhelm the available resources for spectrosopic follow-up. Here we develop a range of light curve classification pipelines, trained on 518 spectroscopically-classified SNe from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS): 357 Type Ia, 93 Type II, 25 Type IIn, 21 Type Ibc, and 17 Type I SLSNe. We present a new parametric analytical model that can accommodate a broad range of SN light curve morphologies, including those with a plateau, and fit this model to data in four PS1 filters (griz). We test a number of feature extraction methods, data augmentation strategies, and machine learning algorithms to predict the class of each SN. Our best pipelines result in 90% average accuracy, 70% average purity, and 80% average completeness for all SN classes, with the highest success rates for Type Ia SNe and SLSNe and the lowest for Type Ibc SNe. Despite the greater complexity of our classification scheme, the purity of our Type Ia SN classification, 95%, is on par with methods developed specifically for Type Ia versus non-Type Ia binary classification. As the first of its kind, this study serves as a guide to developing and training classification algorithms for a wide range of SN types with a purely empirical training set, particularly one that is similar in its characteristics to the expected LSST main survey strategy. Future work will implement this classification pipeline on ~3000 PS1/MDS light curves that lack spectroscopic classification.
Variability of radio-emitting active galactic nuclei can be used to probe both intrinsic variations arising from shocks, flares, and other changes in emission from regions surrounding the central supermassive black hole, as well as extrinsic variations due to scattering by structures in our own Galaxy. Such interstellar scattering also probes the structure of the emitting regions, with microarcsecond resolution. Current studies have necessarily been limited to either small numbers of objects monitored over long periods of time, or large numbers of objects but with poor time sampling. The dramatic increase in survey speed engendered by the Square Kilometre Array will enable precision synoptic monitoring studies of hundreds of thousands of sources with a cadence of days or less. Statistics of variability, in particular concurrent observations at multiple radio frequencies and in other bands of the electromagnetic spectrum, will probe accretion physics over a wide range of AGN classes, luminosities, and orientations, as well as enabling a detailed understanding of the structures responsible for radio wave scattering in the Galactic interstellar medium.
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