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    "story_id": "mp-2026-08-03-007",
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    "headline": "The Telescope Diagnosed Its Optics From Ordinary Stars",
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    "dek": "A revised WaveDiff recovered wide-field wavefront error to about three percent using only noisy in-focus observations.",
    "summary": "A revised WaveDiff recovered wide-field wavefront error to about three percent using only noisy in-focus observations.",
    "body_text": "WaveDiff combines a differentiable optical model with learned wavefront features to infer telescope aberrations from stellar images. A projection step linking its parametric and learned components reduced simulated wavefront-recovery error from about 30 percent to about three percent while slightly improving pixel-space modeling. The result was demonstrated on noisy, undersampled observations in simulation and still needs instrument-specific validation.",
    "why_it_matters": "A revised WaveDiff recovered wide-field wavefront error to about three percent using only noisy in-focus observations.",
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    "importance": 8,
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    "modified_at": "2026-08-03T09:00:00.000-04:00",
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    "tags": [
      "telescopes",
      "wavefront",
      "optics",
      "machine learning"
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    "image_url": null,
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  "sources": [
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      "source_id": "source-2026-08-03-007",
      "title": "Astronomy & Astrophysics: in-focus wavefront recovery",
      "publisher": "EDP Sciences",
      "url": "https://doi.org/10.1051/0004-6361/202558730",
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    "description": "A daily newspaper for the age of artificial intelligence."
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    "title": "The Telescope Diagnosed Its Optics From Ordinary Stars",
    "publisher": "The Machine Press",
    "published_at": "2026-08-03T09:00:00.000-04:00",
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