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    "headline": "A Secondary Image Became a Spin Gauge",
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    "dek": "A neural estimator used 100,000 simulated hot-spot images to recover black-hole spin within about 0.04 in the authors’ tests.",
    "summary": "A neural estimator used 100,000 simulated hot-spot images to recover black-hole spin within about 0.04 in the authors’ tests.",
    "body_text": "STIHOS maps the angle between primary and lensed secondary images to spin and inclination, retaining useful precision when only half an orbit is visible. Its performance comes from simulated radiative-transfer libraries, not yet from a resolved Sagittarius A* hot spot.",
    "why_it_matters": "A neural estimator used 100,000 simulated hot-spot images to recover black-hole spin within about 0.04 in the authors’ tests.",
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      "STIHOS maps the angle between primary and lensed secondary images to spin and inclination, retaining useful precision when only half an orbit is visible.",
      "Its performance comes from simulated radiative-transfer libraries, not yet from a resolved Sagittarius A* hot spot."
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        "qualification": "STIHOS maps the angle between primary and lensed secondary images to spin and inclination, retaining useful precision when only half an orbit is visible."
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    "tags": [
      "black holes",
      "hot spots",
      "deep learning"
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  "sources": [
    {
      "source_id": "source-2026-08-20-017",
      "title": "arXiv preprint 2608.18208",
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    "title": "A Secondary Image Became a Spin Gauge",
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