Parallel Algorithm For Time Delays On Supernovae

Cuevas-Tello, J.C. and Menchaca-Marquez, E.A.
1st International Supercomputing Conference in Mexico (ISUM-2010), Guadalajara, Jal., México. 1(1) 180-184, 2010.


The data under analysis come from two supernovae: SN2004ej and SN2004ex (from Carnegie Supernova Project). This paper presents the data under study, both real and simulated. The synthetic data has not a time delay, so we impose our time delay depending on sampling. Every synthetic data set has 1000 samples. The objective is to estimate time delay between pairs of light curves (positive and negative parity). Here we present some results from χ2, Dispersion Spectra and General Regression Neural Networks (GRNN). These results are compared with the reported results in terms of the uncertainty of estimates. We also present some results from a parallel approach to speed up algorithms, in particular the GRNN. We use Message Passing Interface (MPI) on a beowulf-type cluster to reduce the computational time.