A Robust Determination of the Time Delay in 0957+561A,B and a Measurement of the Global Value of Hubble's Constant
ABSTRACT Continued photometric monitoring of the gravitational lens system 0957+561A,B in the g and r bands with the Apache Point Observatory (APO) 3.5 m telescope during 1996 shows a sharp g band event in the trailing (B) image light curve at the precise time predicted in an earlier paper. The prediction was 1 Supported by the Fannie and John Hertz Foundation 2 Currently at the Kitt Peak National Observatory 3 Currently at the Space Telescope Science Institute -- 2 -- based on the observation of the event during 1995 in the leading (A) image and on a differential time delay of 415 days. This success confirms the so called "short delay", and the absence of any such feature at a delay near 540 days rejects the "long delay" for this system, thus resolving a long standing controversy. A series of statistical analyses of our light curve data yield a best fit delay of 417 Sigma 3 days (95% confidence interval) and demonstrate that this result is quite robust against variations in the analysi...
- SourceAvailable from: Juan Carlos Cuevas-Tello
Conference Paper: Evolved Kernel Method for Time Series.[Show abstract] [Hide abstract]
ABSTRACT: An evolutionary algorithm for parameter estimation of a kernel method for noisy and irregularly sampled time series is presented. We aim to estimate the time delay between time series coming from gravitational lensing in astronomy. The parameters to estimate include the delay, the width of kernels or smoothing, and a regularization parameter. We use mixed types to represent variables within the evolutionary algorithm. The algorithm is tested on several artificial data sets, and also on real astronomical observations. The performance of our method is compared with the most popular methods for time delay estimation. An statistical analysis of results is given, where the results of our approach are more accurate and significant than those of other methods.Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence, http://link.springer.com/chapter/10.1007%2F978-3-540-76631-5_53#page-1; 09/2007
Conference Paper: Computational Intelligence in Astronomy – A Win-Win Situation[Show abstract] [Hide abstract]
ABSTRACT: Large archives of astronomical data (images, spectra and catalogues of derived parameters) are being assembled worldwide as part of the Virtual Observatory project. In order for such massive heterogeneous data collections to be of use to astronomers, development of Computational Intelligence techniques that would combine modern machine learning with deep domain knowledge is crucial. Both fields - Computer Science and Astronomy - can hugely benefit from such a research program. Astronomers can gain new insights into structures buried deeply in the data collections that would, without the help of Computational Intelligence, stay masked. On the other hand, computer scientists can get inspiration and motivation for development of new techniques driven by the specific characteristics of astronomical data and the need to include domain knowledge in a fundamental way. In this review we present three diverse examples of such successful symbiosis.Theory and Practice of Natural Computing, Tarragona, Spain; 10/2012