2006/11/17 by Jeff Crowder, Neil J. Cornish, Neil Cornish · 3 citations
Computer Science · Physics and Astronomy · #Algorithm #Artificial intelligence #Astronomy #Astrophysics #Bayesian probability #Computer science #Galaxy #Gaussian Processes and Bayesian Inference #Gravitational wave #Interferometry #Markov chain Monte Carlo #Physics #Pulsars and Gravitational Waves Research #Ranging #Space (punctuation) #Target Tracking and Data Fusion in Sensor Networks #Telecommunications #astro-ph #gr-qc
paper · pdf · doi:10.1103/physrevd.75.043008
published as Phys.Rev.D75:043008,2007 · 19 pages, 27 figures
arxiv created 2006/11/17 · openalex publication_date 2007/02/23 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Low frequency gravitational wave detectors, such as the Laser Interferometer Space Antenna (LISA), will have to contend with large foregrounds produced by millions of compact galactic binaries in our galaxy. While these galactic signals are interesting in their own right, the unresolved component can obscure other sources. The science yield for the LISA mission can be improved if the brighter and more isolated foreground sources can be identified and regressed from the data. Since the signals overlap with one another, we are faced with a ``cocktail party'' problem of picking out individual conversations in a crowded room. Here we present and implement an end-to-end solution to the galactic foreground problem that is able to resolve tens of thousands of sources from across the LISA band. Our algorithm employs a variant of the Markov chain Monte Carlo (MCMC) method, which we call the blocked annealed Metropolis-Hastings (BAM) algorithm. Following a description of the algorithm and its implementation, we give several examples ranging from searches for a single source to searches for hundreds of overlapping sources. Our examples include data sets from the first round of mock LISA data challenges.