{"paper":{"title":"Sensitivities of Black Hole Images from GRMHD Simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Automatic differentiation-computed gradients of GRMHD black hole images can guide parameter exploration even in the presence of noise.","cross_cats":[],"primary_cat":"astro-ph.HE","authors_text":"Alejandro C\\'ardenas-Avenda\\~no, Cora Prather, M\\'ario Raia Neto, Pedro Naethe Motta","submitted_at":"2026-04-13T18:00:00Z","abstract_excerpt":"The advent of high-fidelity imaging of supermassive black holes calls for efficient and robust data-analysis methods. In this work, we use $\\texttt{Jipole}$, a differentiable, $\\texttt{ipole}$-based radiative transfer code, to enable gradient-based analyses of images generated from state-of-the-art general relativistic magnetohydrodynamic (GRMHD) simulations. We compute image sensitivities, i.e., pixel-wise derivatives of the intensity with respect to model parameters, which form the Jacobian of the forward model and define a local map from parameter space to image space. Using these sensitivi"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Automatic differentiation-computed image gradients can guide parameter exploration effectively even in the presence of noise.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the mock data analysis and idealized blurring/noise models sufficiently represent the challenges of real EHT observations, including unmodeled systematics and the full complexity of GRMHD parameter degeneracies.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Differentiable GRMHD image sensitivities create a structured error landscape that supports gradient-based parameter recovery for black hole imaging under idealized and noisy conditions.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Automatic differentiation-computed gradients of GRMHD black hole images can guide parameter exploration even in the presence of noise.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"9b703be8dcb48876bdf29e49df8d41de3cffa62dcdd597a7676cde6a6ed8cea6"},"source":{"id":"2604.11869","kind":"arxiv","version":2},"verdict":{"id":"5904473c-bac8-4aba-ac92-bbbc2996249a","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T16:29:36.576393Z","strongest_claim":"Automatic differentiation-computed image gradients can guide parameter exploration effectively even in the presence of noise.","one_line_summary":"Differentiable GRMHD image sensitivities create a structured error landscape that supports gradient-based parameter recovery for black hole imaging under idealized and noisy conditions.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the mock data analysis and idealized blurring/noise models sufficiently represent the challenges of real EHT observations, including unmodeled systematics and the full complexity of GRMHD parameter degeneracies.","pith_extraction_headline":"Automatic differentiation-computed gradients of GRMHD black hole images can guide parameter exploration even in the presence of noise."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.11869/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}