Million Points of Light (MPoL): a PyTorch library for radio interferometric imaging and inference
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866910808178425856 |
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| author | Czekala, Ian Jennings, Jeff Zawadzki, Brianna Nizam, Kadri Loomis, Ryan Delamer, Megan de Soto, Kaylee Frazier, Robert Grzybowski, Hannah Huang, Jane Ogborn, Mary Quinn, Tyler |
| author_facet | Czekala, Ian Jennings, Jeff Zawadzki, Brianna Nizam, Kadri Loomis, Ryan Delamer, Megan de Soto, Kaylee Frazier, Robert Grzybowski, Hannah Huang, Jane Ogborn, Mary Quinn, Tyler |
| contents | Astronomical radio interferometers achieve exquisite angular resolution by cross-correlating signal from a cosmic source simultaneously observed by distant pairs of radio telescopes to produce a Fourier-type measurement called a visibility. Million Points of Light (MPoL) is a Python library supporting feed-forward modeling of interferometric visibility datasets for synthesis imaging and parametric Bayesian inference, built using the autodifferentiable machine learning framework PyTorch. Neural network components provide a rich set of modular and composable building blocks that can be used to express the physical relationships between latent model parameters and observed data following the radio interferometric measurement equation. Industry-grade optimizers make it straightforward to simultaneously solve for the synthesized image and calibration parameters using stochastic gradient descent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00100 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Million Points of Light (MPoL): a PyTorch library for radio interferometric imaging and inference Czekala, Ian Jennings, Jeff Zawadzki, Brianna Nizam, Kadri Loomis, Ryan Delamer, Megan de Soto, Kaylee Frazier, Robert Grzybowski, Hannah Huang, Jane Ogborn, Mary Quinn, Tyler Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics Astronomical radio interferometers achieve exquisite angular resolution by cross-correlating signal from a cosmic source simultaneously observed by distant pairs of radio telescopes to produce a Fourier-type measurement called a visibility. Million Points of Light (MPoL) is a Python library supporting feed-forward modeling of interferometric visibility datasets for synthesis imaging and parametric Bayesian inference, built using the autodifferentiable machine learning framework PyTorch. Neural network components provide a rich set of modular and composable building blocks that can be used to express the physical relationships between latent model parameters and observed data following the radio interferometric measurement equation. Industry-grade optimizers make it straightforward to simultaneously solve for the synthesized image and calibration parameters using stochastic gradient descent. |
| title | Million Points of Light (MPoL): a PyTorch library for radio interferometric imaging and inference |
| topic | Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics |
| url | https://arxiv.org/abs/2502.00100 |