GPU-Accelerated X-ray Pulse Profile Modeling
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arXiv
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| Format: | Preprint |
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2025
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| author | Zhou, Tianzhe Huang, Chun |
| author_facet | Zhou, Tianzhe Huang, Chun |
| contents | Pulse-profile modeling (PPM) of thermal X-ray emission from rotation-powered millisecond pulsars enables simultaneous constraints on the mass $M$, radius $R$, and hence the equation of state of cold, dense matter. However, Bayesian PPM has faced a hard accuracy-speed bottleneck: current production resolutions used to keep inference tractable can under-resolve extreme hotspot geometries and bias the waveform computation, whereas the higher resolutions that remove this bias push forward models to minutes per evaluation, making inference impractical. We break this trade-off with, to our knowledge, the first public GPU-accelerated X-ray PPM framework that matches established benchmarks to within $\sim10^{-3}$ relative accuracy even for extreme geometries, while collapsing minutes-long high-fidelity computations to $2$--$5$ ms on an RTX 4080 ($10^{3}$--$10^{4}\times$ speedups), enabling posterior exploration at resolutions and complexities previously out of reach. We further uncover a bias near the interpolation boundaries of atmosphere lookup tables, demonstrate it with two diagnostic tests, and counter it with a mixed-order interpolator. Together, these advances enlarge the feasible hotspot model space and reduce key systematics in PPM, strengthening inferences for current and future X-ray missions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07764 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPU-Accelerated X-ray Pulse Profile Modeling Zhou, Tianzhe Huang, Chun High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics General Relativity and Quantum Cosmology Nuclear Theory Pulse-profile modeling (PPM) of thermal X-ray emission from rotation-powered millisecond pulsars enables simultaneous constraints on the mass $M$, radius $R$, and hence the equation of state of cold, dense matter. However, Bayesian PPM has faced a hard accuracy-speed bottleneck: current production resolutions used to keep inference tractable can under-resolve extreme hotspot geometries and bias the waveform computation, whereas the higher resolutions that remove this bias push forward models to minutes per evaluation, making inference impractical. We break this trade-off with, to our knowledge, the first public GPU-accelerated X-ray PPM framework that matches established benchmarks to within $\sim10^{-3}$ relative accuracy even for extreme geometries, while collapsing minutes-long high-fidelity computations to $2$--$5$ ms on an RTX 4080 ($10^{3}$--$10^{4}\times$ speedups), enabling posterior exploration at resolutions and complexities previously out of reach. We further uncover a bias near the interpolation boundaries of atmosphere lookup tables, demonstrate it with two diagnostic tests, and counter it with a mixed-order interpolator. Together, these advances enlarge the feasible hotspot model space and reduce key systematics in PPM, strengthening inferences for current and future X-ray missions. |
| title | GPU-Accelerated X-ray Pulse Profile Modeling |
| topic | High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics General Relativity and Quantum Cosmology Nuclear Theory |
| url | https://arxiv.org/abs/2510.07764 |