3 research outputs found
Extracting galactic binary signals from the first round of Mock LISA Data Challenges
We report on the performance of an end-to-end Bayesian analysis pipeline for
detecting and characterizing galactic binary signals in simulated LISA data.
Our principal analysis tool is the Blocked-Annealed Metropolis Hasting (BAM)
algorithm, which has been optimized to search for tens of thousands of
overlapping signals across the LISA band. The BAM algorithm employs Bayesian
model selection to determine the number of resolvable sources, and provides
posterior distribution functions for all the model parameters. The BAM
algorithm performed almost flawlessly on all the Round 1 Mock LISA Data
Challenge data sets, including those with many highly overlapping sources. The
only misses were later traced to a coding error that affected high frequency
sources. In addition to the BAM algorithm we also successfully tested a Genetic
Algorithm (GA), but only on data sets with isolated signals as the GA has yet
to be optimized to handle large numbers of overlapping signals.Comment: 13 pages, 4 figures, submitted to Proceedings of GWDAW-11 (Berlin,
Dec. '06
A Three-Stage Search for Supermassive Black Hole Binaries in LISA Data
Gravitational waves from the inspiral and coalescence of supermassive
black-hole (SMBH) binaries with masses ~10^6 Msun are likely to be among the
strongest sources for the Laser Interferometer Space Antenna (LISA). We
describe a three-stage data-analysis pipeline designed to search for and
measure the parameters of SMBH binaries in LISA data. The first stage uses a
time-frequency track-search method to search for inspiral signals and provide a
coarse estimate of the black-hole masses m_1, m_2 and of the coalescence time
of the binary t_c. The second stage uses a sequence of matched-filter template
banks, seeded by the first stage, to improve the measurement accuracy of the
masses and coalescence time. Finally, a Markov Chain Monte Carlo search is used
to estimate all nine physical parameters of the binary. Using results from the
second stage substantially shortens the Markov Chain burn-in time and allows us
to determine the number of SMBH-binary signals in the data before starting
parameter estimation. We demonstrate our analysis pipeline using simulated data
from the first LISA Mock Data Challenge. We discuss our plan for improving this
pipeline and the challenges that will be faced in real LISA data analysis.Comment: 12 pages, 3 figures, submitted to Proceedings of GWDAW-11 (Berlin,
Dec. '06
Inference on inspiral signals using LISA MLDC data
In this paper we describe a Bayesian inference framework for analysis of data
obtained by LISA. We set up a model for binary inspiral signals as defined for
the Mock LISA Data Challenge 1.2 (MLDC), and implemented a Markov chain Monte
Carlo (MCMC) algorithm to facilitate exploration and integration of the
posterior distribution over the 9-dimensional parameter space. Here we present
intermediate results showing how, using this method, information about the 9
parameters can be extracted from the data.Comment: Accepted for publication in Classical and Quantum Gravity, GWDAW-11
special issu