Efficient GPU-Accelerated MultiSource Global Fit Pipeline for LISA Data Analysis
The large-scale analysis task of deciphering gravitational-wave signals in the LISA data stream will be difficult, requiring a large amount of computational resources and extensive development of computational methods. Its high dimensionality, multiple model types, and complicated noise profile requ...
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| Veröffentlicht in: | Physical review. D Jg. 111; H. 2 |
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Marshall Space Flight Center
American Physical Society
24.01.2025
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| Abstract | The large-scale analysis task of deciphering gravitational-wave signals in the LISA data stream will be difficult, requiring a large amount of computational resources and extensive development of computational methods. Its high dimensionality, multiple model types, and complicated noise profile require a global fit to all parameters and input models simultaneously. In this work, we detail our global fit algorithm, called “Erebor,” designed to accomplish this challenging task. It is capable of analyzing current state-of-the-art datasets and then growing into the future as more pieces of the pipeline are completed and added. We describe our pipeline strategy, the algorithmic setup, and the results from our analysis of the LDC2A Sangria dataset, which contains massive black hole binaries, compact galactic binaries, and a parametrized noise spectrum whose parameters are unknown to the user. The Erebor algorithm includes three unique and very useful contributions: GPU acceleration for enhanced computational efficiency; ensemble Markov Chain Monte Carlo (MCMC) sampling with multiple MCMC walkers per temperature for better mixing and parallelized sample creation; and special online updates to reversible-jump (or transdimensional) sampling distributions to ensure sampler mixing and accurate initial estimates for detectable sources in the data.We recover posterior distributions for all 15 (6) of the injected massive black hole binaries (MBHB) in the LDC2A training (hidden) dataset. We catalog ∼12000 galactic binaries (∼8000 as high confidence detections) for both the training and hidden datasets. All of the sources and their posterior distributions are provided in publicly available catalogs. |
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| AbstractList | The large-scale analysis task of deciphering gravitational-wave signals in the LISA data stream will be difficult, requiring a large amount of computational resources and extensive development of computational methods. Its high dimensionality, multiple model types, and complicated noise profile require a global fit to all parameters and input models simultaneously. In this work, we detail our global fit algorithm, called “Erebor,” designed to accomplish this challenging task. It is capable of analyzing current state-of-the-art datasets and then growing into the future as more pieces of the pipeline are completed and added. We describe our pipeline strategy, the algorithmic setup, and the results from our analysis of the LDC2A Sangria dataset, which contains massive black hole binaries, compact galactic binaries, and a parametrized noise spectrum whose parameters are unknown to the user. The Erebor algorithm includes three unique and very useful contributions: GPU acceleration for enhanced computational efficiency; ensemble Markov Chain Monte Carlo (MCMC) sampling with multiple MCMC walkers per temperature for better mixing and parallelized sample creation; and special online updates to reversible-jump (or transdimensional) sampling distributions to ensure sampler mixing and accurate initial estimates for detectable sources in the data.We recover posterior distributions for all 15 (6) of the injected massive black hole binaries (MBHB) in the LDC2A training (hidden) dataset. We catalog ∼12000 galactic binaries (∼8000 as high confidence detections) for both the training and hidden datasets. All of the sources and their posterior distributions are provided in publicly available catalogs. The large-scale analysis task of deciphering gravitational-wave signals in the LISA data stream will be difficult, requiring a large amount of computational resources and extensive development of computational methods. Its high dimensionality, multiple model types, and complicated noise profile require a global fit to all parameters and input models simultaneously. In this work, we detail our global fit algorithm, called “Erebor,” designed to accomplish this challenging task. It is capable of analyzing current state-of-the-art datasets and then growing into the future as more pieces of the pipeline are completed and added. We describe our pipeline strategy, the algorithmic setup, and the results from our analysis of the LDC2A Sangria dataset, which contains massive black hole binaries, compact galactic binaries, and a parametrized noise spectrum whose parameters are unknown to the user. The Erebor algorithm includes three unique and very useful contributions: GPU acceleration for enhanced computational efficiency; ensemble Markov Chain Monte Carlo (MCMC) sampling with multiple MCMC walkers per temperature for better mixing and parallelized sample creation; and special online updates to reversible-jump (or transdimensional) sampling distributions to ensure sampler mixing and accurate initial estimates for detectable sources in the data. We recover posterior distributions for all 15 (6) of the injected massive black hole binaries (MBHB) in the LDC2A training (hidden) dataset. We catalog ∼ 12000 galactic binaries ( ∼ 8000 as high confidence detections) for both the training and hidden datasets. All of the sources and their posterior distributions are provided in publicly available catalogs. |
| ArticleNumber | 024060 |
| Audience | PUBLIC |
| Author | Stergioulas, Nikolaos Korsakova, Natalia Katz, Michael L Gair, Jonathan Karnesis, Nikolaos |
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| Cites_doi | 10.1088/1674-4527/acdfa5 10.1103/PhysRevD.105.044055 10.1103/PhysRevD.100.022003 10.1103/PhysRevD.104.043019 10.3847/1538-4357/ad2068 10.1093/mnras/stac2555 10.1038/s42254-021-00364-9 10.1093/mnras/stz903 10.1017/pasa.2023.36 10.1038/287307a0 10.1093/mnras/stad554 10.1111/j.1365-246X.2006.03155.x 10.1103/PhysRevD.110.104069 10.1103/PhysRevD.108.103018 10.1093/biomet/82.4.711 10.1093/mnras/stae1283 10.1103/PhysRevD.108.082004 10.1103/PhysRevLett.120.161102 10.1103/PhysRevD.108.022008 10.1103/PhysRevD.105.044035 10.1103/PhysRevD.101.123021 10.1051/0004-6361/202346844 10.1103/PhysRevD.108.044065 10.1103/PhysRevD.66.122002 10.1103/PhysRevD.106.103001 10.1103/PhysRevD.107.063004 10.1093/mnras/stab113 10.1080/01621459.1992.10475289 10.1103/PhysRevD.59.102003 10.1103/PhysRevD.68.061303 10.1103/PhysRevD.104.084037 10.1093/mnras/stv2422 10.1103/PhysRevD.110.022003 10.3847/1538-4357/ac9139 10.1103/PhysRevD.95.103012 10.1103/PhysRevX.13.041039 10.1007/s41114-020-00029-6 10.1103/PhysRevD.105.122008 10.1051/0004-6361/202347222 10.1093/mnras/stad2939 10.1007/s41114-022-00041-y 10.1016/j.advwatres.2011.04.013 10.1109/MCSE.2007.55 10.1088/0004-637X/758/2/131 10.1103/PhysRevD.106.042005 10.1038/s41586-020-2649-2 10.1103/PhysRevD.109.042001 10.1103/PhysRevD.93.044006 10.1103/PhysRevD.106.022003 10.3847/2041-8213/acdac6 10.1016/j.jpdc.2005.03.010 10.1103/PhysRevD.105.042002 10.1086/670067 10.1038/s41592-019-0686-2 10.1103/PhysRevD.76.083006 10.1103/PhysRevD.109.022004 10.1103/PhysRevD.93.044007 10.2140/camcos.2010.5.65 10.1103/PhysRevD.108.123029 10.1103/PhysRevD.110.024005 10.1103/PhysRevD.104.104054 10.1103/PhysRevD.71.022001 10.1016/j.jpdc.2007.09.005 10.1109/MCSE.2021.3083216 |
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| References | PhysRevD.111.024060Cc26R1 PhysRevD.111.024060Cc24R1 PhysRevD.111.024060Cc68R1 J.-B. Bayle (PhysRevD.111.024060Cc23R1) 2023 PhysRevD.111.024060Cc28R1 PhysRevD.111.024060Cc49R1 PhysRevD.111.024060Cc66R1 PhysRevD.111.024060Cc22R1 PhysRevD.111.024060Cc43R1 PhysRevD.111.024060Cc60R1 PhysRevD.111.024060Cc20R1 PhysRevD.111.024060Cc62R1 PhysRevD.111.024060Cc14R1 PhysRevD.111.024060Cc37R1 PhysRevD.111.024060Cc12R1 PhysRevD.111.024060Cc35R1 PhysRevD.111.024060Cc18R1 PhysRevD.111.024060Cc56R1 PhysRevD.111.024060Cc79R1 PhysRevD.111.024060Cc16R1 PhysRevD.111.024060Cc39R1 PhysRevD.111.024060Cc58R1 PhysRevD.111.024060Cc3R1 PhysRevD.111.024060Cc7R1 PhysRevD.111.024060Cc5R1 PhysRevD.111.024060Cc52R1 PhysRevD.111.024060Cc75R1 PhysRevD.111.024060Cc54R1 PhysRevD.111.024060Cc77R1 PhysRevD.111.024060Cc9R1 PhysRevD.111.024060Cc10R1 PhysRevD.111.024060Cc71R1 PhysRevD.111.024060Cc31R1 PhysRevD.111.024060Cc50R1 PhysRevD.111.024060Cc73R1 PhysRevD.111.024060Cc25R1 PhysRevD.111.024060Cc48R1 PhysRevD.111.024060Cc46R1 W. R. Gilks (PhysRevD.111.024060Cc41R1) 1992; 41 PhysRevD.111.024060Cc29R1 PhysRevD.111.024060Cc67R1 PhysRevD.111.024060Cc27R1 PhysRevD.111.024060Cc65R1 PhysRevD.111.024060Cc21R1 PhysRevD.111.024060Cc44R1 PhysRevD.111.024060Cc15R1 PhysRevD.111.024060Cc36R1 PhysRevD.111.024060Cc13R1 PhysRevD.111.024060Cc19R1 PhysRevD.111.024060Cc57R1 PhysRevD.111.024060Cc78R1 PhysRevD.111.024060Cc17R1 PhysRevD.111.024060Cc38R1 PhysRevD.111.024060Cc59R1 F. Pedregosa (PhysRevD.111.024060Cc63R1) 2011; 12 PhysRevD.111.024060Cc4R1 PhysRevD.111.024060Cc8R1 PhysRevD.111.024060Cc6R1 PhysRevD.111.024060Cc53R1 PhysRevD.111.024060Cc74R1 PhysRevD.111.024060Cc55R1 PhysRevD.111.024060Cc76R1 PhysRevD.111.024060Cc11R1 PhysRevD.111.024060Cc32R1 PhysRevD.111.024060Cc30R1 PhysRevD.111.024060Cc51R1 PhysRevD.111.024060Cc72R1 |
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| Title | Efficient GPU-Accelerated MultiSource Global Fit Pipeline for LISA Data Analysis |
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