A GPU-accelerated semi-coherent hierarchical search for stellar-mass binary inspiral signals in LISA
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| Format: | Preprint |
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2024
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| _version_ | 1866916489865461760 |
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| author | Bandopadhyay, Diganta Moore, Christopher J. |
| author_facet | Bandopadhyay, Diganta Moore, Christopher J. |
| contents | Searching for gravitational waves from stellar-mass binary black holes with LISA remains a challenging open problem. Conventional template-bank approaches to the search are impossible due to the prohibitive number of templates that would be required. This paper continues the development of a hierarchical semi-coherent stochastic search, extending it to a full end-to-end pipeline that is then applied to multiple mock LISA data streams which include simulated noise. Particle swarm optimization is used as a stochastic search algorithm, tracking multiple maxima of a semi-coherent search statistic defined over source parameter space. The pipeline is accelerated by the use of graphical processing units (GPUs). No prior information from observations by ground-based detectors is used; this is necessary in order to provide advance warning of the merger. We find that the pipeline is able to detect sources with signal-to-noise ratios as low as $ρ\sim 17$, we demonstrate that these searches can directly seed coarse parameter estimation in cases where the search trigger is loud. An example of how the false-alarm probability can be estimated for this type of GW search is also included. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_13170 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A GPU-accelerated semi-coherent hierarchical search for stellar-mass binary inspiral signals in LISA Bandopadhyay, Diganta Moore, Christopher J. General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Searching for gravitational waves from stellar-mass binary black holes with LISA remains a challenging open problem. Conventional template-bank approaches to the search are impossible due to the prohibitive number of templates that would be required. This paper continues the development of a hierarchical semi-coherent stochastic search, extending it to a full end-to-end pipeline that is then applied to multiple mock LISA data streams which include simulated noise. Particle swarm optimization is used as a stochastic search algorithm, tracking multiple maxima of a semi-coherent search statistic defined over source parameter space. The pipeline is accelerated by the use of graphical processing units (GPUs). No prior information from observations by ground-based detectors is used; this is necessary in order to provide advance warning of the merger. We find that the pipeline is able to detect sources with signal-to-noise ratios as low as $ρ\sim 17$, we demonstrate that these searches can directly seed coarse parameter estimation in cases where the search trigger is loud. An example of how the false-alarm probability can be estimated for this type of GW search is also included. |
| title | A GPU-accelerated semi-coherent hierarchical search for stellar-mass binary inspiral signals in LISA |
| topic | General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2408.13170 |