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We expand our Bayesian Monte Carlo method for analyzing the light curves of gravitationally lensed quasars to simultaneously estimate time delays and quasar structure including their mutual uncertainties. We apply the method to HE1104-1805 and QJ0158-4325, two doubly-imaged quasars with microlensing and intrinsic variability on comparable time scales. For HE1104-1805 the resulting time delay of (Delta t_AB) = t_A - t_B = 162.2 -5.9/+6.3 days and accretion disk size estimate of log(r_s/cm) = 15.7 -0.5/+0.4 at 0.2 micron in the rest frame are consistent with earlier estimates but suggest that existing methods for estimating time delays in the presence of microlensing underestimate the uncertainties. We are unable to measure a time delay for QJ0158-4325, but the accretion disk size is log(r_s/cm) = 14.9 +/- 0.3 at 0.3 micron in the rest frame.
Aims. The main purpose of this paper is to study time delays between the light variations in different wavebands for a sample of quasars. Measuring a reliable time delay for a large number of quasars may help constraint the models of their central en
We present new measurements of the time delays of WFI2033-4723. The data sets used in this work include 14 years of data taken at the 1.2m Leonhard Euler Swiss telescope, 13 years of data from the SMARTS 1.3m telescope at Las Campanas Observatory and
We present time-delay estimates for the quadruply imaged quasar PG 1115+080. Our resuls are based on almost daily observations for seven months at the ESO MPIA 2.2m telescope at La Silla Observatory, reaching a signal-to-noise ratio of about 1000 per
We present 13 seasons of $R$-band photometry of the quadruply-lensed quasar WFI 2033-4723 from the 1.3m SMARTS telescope at CTIO and the 1.2m Euler Swiss Telescope at La Silla, in which we detect microlensing variability of $sim0.2$ mags on a timesca
Classical distributed estimation scenarios typically assume timely and reliable exchanges of information over the sensor network. This paper, in contrast, considers single time-scale distributed estimation via a sensor network subject to transmission