Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userQuasar Variability Measurements With SDSS Repeated Imaging and POSS Data
401
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We analyze the properties of quasar variability using repeated SDSS imaging data in five UV-to-far red photometric bands, accurate to 0.02 mag, for 13,000 spectroscopically confirmed quasars. The observed time lags span the range from 3 hours to over 3 years, and constrain the quasar variability for rest-frame time lags of up to two years, and at rest-frame wavelengths from 1000 Ang. to 6000 Ang. We demonstrate that 66,000 SDSS measurements of magnitude differences can be described within the measurement noise by a simple function of only three free parameters. The addition of POSS data constrains the long-term behavior of quasar variability and provides evidence for a turn-over in the structure function. This turn-over indicates that the characteristic time scale for optical variability of quasars is of the order 1 year.
rate research
Read More
We measure quasar variability using the Panoramic Survey Telescope and Rapid Response System 1 Survey (Pan-STARRS1 or PS1) and the Sloan Digital Sky Survey (SDSS) and establish a method of selecting quasars via their variability in 10,000 square degree surveys. We use 100,000 spectroscopically confirmed quasars that have been well measured in both PS1 and SDSS and take advantage of the decadal time scales that separate SDSS measurements and PS1 measurements. A power law model fits the data well over the entire time range tested, 0.01 to 10 years. Variability in the current PS1-SDSS dataset can efficiently distinguish between quasars and non-varying objects. It improves the purity of a griz quasar color cut from 4.1% to 48% while maintaining 67% completeness. Variability will be very effective at finding quasars in datasets with no u band and in redshift ranges where exclusively photometric selection is not efficient. We show that quasars rest-frame ensemble variability, measured as a root mean squared in delta magnitudes, is consistent with V(z, L, t) = A0 (1+z)^0.37 (L/L0)^-0.16 (t/1yr)^0.246 , where L0 = 10^46 ergs^-1 and A0 = 0.190, 0.162, 0.147 or 0.141 in the gP1 , rP1 , iP1 or zP1 filter, respectively. We also fit across all four filters and obtain median variability as a function of z, L and lambda as V(z, L, lambda, t) = 0.079(1 + z)^0.15 (L/L0 )^-0.2 (lambda/1000 nm)^-0.44 (t/1yr)^0.246 .
We investigate the effect of conditional null measurements on a quantum system and find a rich variety of behaviors. Specifically, quantum dynamics with a time independent $H$ in a finite dimensional Hilbert space are considered with repeated strong null measurements of a specified state. We discuss four generic behaviors that emerge in these monitored systems. The first arises in systems without symmetry, along with their associated degeneracies in the energy spectrum, and hence in the absence of dark states as well. In this case, a unique final state can be found which is determined by the largest eigenvalue of the survival operator, the non-unitary operator encoding both the unitary evolution between measurements and the measurement itself. For a three-level system, this is similar to the well known shelving effect. Secondly, for systems with built-in symmetry and correspondingly a degenerate energy spectrum, the null measurements dynamically select the degenerate energy levels, while the non-degenerate levels are effectively wiped out. Thirdly, in the absence of dark states, and for specific choices of parameters, two or more eigenvalues of the survival operator match in magnitude, and this leads to an oscillatory behavior controlled by the measurement rate and not solely by the energy levels. Finally, when the control parameters are tuned, such that the eigenvalues of the survival operator all coalesce to zero, one has exceptional points that corresponds to situations that violate the null measurement condition, making the conditional measurement process impossible.
We report the discovery of extreme X-ray variability in a type 1 quasar: SDSS J$075101.42+291419.1$. It has a black hole mass of $1.6times 10^7~rm M_odot$ measured from reverberation mapping (RM), and the black hole is accreting with a super-Eddington accretion rate. Its XMM-Newton observation in 2015 May reveals a flux drop by a factor of $sim 22$ with respect to the Swift observation in 2013 May when it showed a typical level of X-ray emission relative to its UV/optical emission. The lack of correlated UV variability results in a steep X-ray-to-optical power-law slope ($alpha_{rm OX}$) of -1.97 in the low X-ray flux state, corresponding to an X-ray weakness factor of 36.2 at rest-frame 2 keV relative to its UV/optical luminosity. The mild UV/optical continuum and emission-line variability also suggest that the accretion rate did not change significantly. A single power-law model modified by Galactic absorption describes well the $0.3-10$ keV spectra of the X-ray observations in general. The spectral fitting reveals steep spectral shapes with $Gammaapprox3$. We search for active galactic nuclei (AGNs) with such extreme X-ray variability in the literature and find that most of them are narrow-line Seyfert 1 galaxies and quasars with high accretion rates. The fraction of extremely X-ray variable objects among super-Eddington accreting AGNs is estimated to be $approx 15-24%$. We discuss two possible scenarios, disk reflection and partial covering absorption, to explain the extreme X-ray variability of SDSS J$075101.42+291419.1$. We propose a possible origin for the partial covering absorber, which is the thick inner accretion disk and its associated outflow in AGNs with high accretion rates.
We present an improved photometric error analysis for the 7,100 CRTS (Catalina Real-Time Transient Survey) optical light curves for quasars from the SDSS (Sloan Digital Sky Survey) Stripe 82 catalogue. The SDSS imaging survey has provided a time-resolved photometric data set which greatly improved our understanding of the quasar optical continuum variability: Data for monthly and longer time-scales are consistent with a damped random walk (DRW). Recently, newer data obtained by CRTS provided puzzling evidence for enhanced variability, compared to SDSS results, on monthly time-scales. Quantitatively, SDSS results predict about 0.06 mag root-mean-square (rms) variability for monthly time-scales, while CRTS data show about a factor of 2 larger rms, for spectroscopically confirmed SDSS quasars. Our analysis has successfully resolved this discrepancy as due to slightly underestimated photometric uncertainties from the CRTS image processing pipelines. As a result, the correction for observational noise is too small and the implied quasar variability is too large. The CRTS photometric error correction factors, derived from detailed analysis of non-variable SDSS standard stars that were re-observed by CRTS, are about 20-30%, and result in reconciling quasar variability behaviour implied by the CRTS data with earlier SDSS results. An additional analysis based on independent light curve data for the same objects obtained by the Palomar Transient Factory provides further support for this conclusion. In summary, the quasar variability constraints on weekly and monthly time-scales from SDSS, CRTS and PTF surveys are mutually compatible, as well as consistent with DRW model.
We determine the number counts and z=0-5 luminosity function for a well-defined, homogeneous sample of quasars from the Sloan Digital Sky Survey (SDSS). We conservatively define the most uniform statistical sample possible, consisting of 15,343 quasars within an effective area of 1622 deg^2 that was derived from a parent sample of 46,420 spectroscopically confirmed broad-line quasars in the 5282 deg^2 of imaging data from SDSS Data Release Three. The sample extends from i=15 to i=19.1 at z<3 and to i=20.2 for z>3. The number counts and luminosity function agree well with the results of the 2dF QSO Survey, but the SDSS data probe to much higher redshifts than does the 2dF sample. The number density of luminous quasars peaks between redshifts 2 and 3, although uncertainties in the selection function in this range do not allow us to determine the peak redshift more precisely. Our best fit model has a flatter bright end slope at high redshift than at low redshift. For z<2.4 the data are best fit by a redshift-independent slope of beta = -3.1 (Phi(L) propto L^beta). Above z=2.4 the slope flattens with redshift to beta=-2.37 at z=5. This slope change, which is significant at a >5-sigma level, must be accounted for in models of the evolution of accretion onto supermassive black holes.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
