{"paper":{"title":"SE3D: Building a radiative transfer emulator to fit panchromatic resolved galaxy observations with 3D models of dust and stars","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A Bayesian neural network emulator reproduces 3D radiative transfer outputs for galaxies to 0.05 dex accuracy.","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.GA","authors_text":"Cheng Li, Junkai Zhang, Steven Ramnichal, Stijn Wuyts","submitted_at":"2025-11-24T19:00:34Z","abstract_excerpt":"We present a framework for analysing panchromatic and spatially resolved galaxy observations, dubbed SE3D. SE3D simultaneously and self-consistently models a galaxy's spectral energy distribution and its spectral distributions of global structural parameters: the wavelength-dependent galaxy size, light profile and projected axis ratio. To this end, it employs a machine learning emulator trained on a large library of toy model galaxies processed with 3D dust radiative transfer and mock-observed under a range of viewing angles. The toy models vary in their stellar and dust geometries, and includ"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The computationally efficient machine learning emulator uses a Bayesian neural network architecture, and reproduces the spectral distributions at an accuracy of ~ 0.05 dex or less across the dynamic range of input parameters, and across the rest-frame UVJ colour space spanned by observed galaxies.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The library of toy models with chosen stellar and dust geometries plus radial population gradients is representative enough of real galaxies that the emulator can be used to fit actual panchromatic resolved observations.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"SE3D builds a Bayesian neural network emulator that reproduces galaxy spectral distributions and structural parameters from 3D dust radiative transfer models at ~0.05 dex accuracy across UVJ colors and input ranges.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A Bayesian neural network emulator reproduces 3D radiative transfer outputs for galaxies to 0.05 dex accuracy.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"154139fd53eb5c022f72441114c0079de03282925b5e0969efd11be526b4a281"},"source":{"id":"2511.19623","kind":"arxiv","version":2},"verdict":{"id":"1fba7dfc-c59b-4425-90b5-0837335242fd","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T05:53:50.924663Z","strongest_claim":"The computationally efficient machine learning emulator uses a Bayesian neural network architecture, and reproduces the spectral distributions at an accuracy of ~ 0.05 dex or less across the dynamic range of input parameters, and across the rest-frame UVJ colour space spanned by observed galaxies.","one_line_summary":"SE3D builds a Bayesian neural network emulator that reproduces galaxy spectral distributions and structural parameters from 3D dust radiative transfer models at ~0.05 dex accuracy across UVJ colors and input ranges.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The library of toy models with chosen stellar and dust geometries plus radial population gradients is representative enough of real galaxies that the emulator can be used to fit actual panchromatic resolved observations.","pith_extraction_headline":"A Bayesian neural network emulator reproduces 3D radiative transfer outputs for galaxies to 0.05 dex accuracy."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2511.19623/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"}