{"paper":{"title":"Reimagining SED Fitting with Cosmological Galaxy Simulations and Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.GA","authors_text":"Desika Narayanan, Dhruv T. Zimmerman","submitted_at":"2026-06-17T18:00:06Z","abstract_excerpt":"SED fitting is the most common technique to recover galaxy physical properties from observed photometry. However, SED fitting requires many assumptions that essentially collapse a galaxy from a three-dimensional spatially varying object with complex structure into a scalar point. Moreover, modern inference techniques are computationally intensive, which presents a unique challenge in the era of extremely large datasets. We present \\textsc{Phot-Gal}, a new galaxy SED modeling tool that solves the inverse problem of SED fitting by training a machine learning model on photometry generated from 3D"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.19447","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.19447/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"}