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    "headline": "At Realistic Noise, Simple Shapes Catch the Neural Network",
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    "dek": "On 10,000 simulated thermal Sunyaev-Zel’dovich maps, interpretable summaries matched CNN constraints once observatory-like noise was added.",
    "summary": "On 10,000 simulated thermal Sunyaev-Zel’dovich maps, interpretable summaries matched CNN constraints once observatory-like noise was added.",
    "body_text": "The first field-level analysis in the study found tighter noiseless constraints from a convolutional network, but Minkowski functionals combined with the power spectrum became comparable under Simons Observatory-like noise. The comparison uses simulations and motivates real-data work rather than replacing it.",
    "why_it_matters": "On 10,000 simulated thermal Sunyaev-Zel’dovich maps, interpretable summaries matched CNN constraints once observatory-like noise was added.",
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      "cosmology",
      "simulation",
      "neural networks"
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      "source_id": "source-2026-10-30-020",
      "title": "arXiv preprint 2610.40617, original v1",
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    "title": "At Realistic Noise, Simple Shapes Catch the Neural Network",
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