{"paper":{"title":"VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.GA","authors_text":"A. B. Kova\\v{c}evi\\'c, A. I. Malz, A. Peca, A. Rojas-Lilay\\'u, A. Viitanen, A. W. Graham, B. Rani, B. Sotomayor, C. G. Bornancini, C. Mazzucchelli, C. Ricci, D. De Cicco, D. Ili\\'c, D. Marsango, D. P. Schneider, E. Saremi, F. E. Bauer, F. Zou, G. T. Richards, H. Guo, I. Yoon, J. Fagin, L. Hernandez-Garc\\'ia, M. Espinoza-Ortiz, M. Fatovi\\'c, M. J. Temple, M. Marculewicz, M. Salvato, P. Ar\\'Evalo, P. Lira, P. S\\'anchez-S\\'aez, R. A. Riffel, R. J. Assef, R. Shirley, S. Panda, S. Satheesh-Sheeba, T. Anguita, T. Mkrtchyan, T T. Ananna, V. Petrecca, W.N. Brandt, Z. Yu","submitted_at":"2026-07-17T18:29:57Z","abstract_excerpt":"Photometric redshift estimation for active galactic nuclei (AGNs) remains a fundamental challenge for current and upcoming large-scale photometric surveys. Traditional spectral energy distribution (SED) fitting suffers from color-redshift degeneracies, particularly for AGNs whose power-law continua hide the strong spectral features required to anchor redshift estimates. While AGN variability provides additional constraining power, existing frameworks require multi-band light curves that are not always available. This work presents VAR-PZnn, a fully connected mixture density network that integr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.16434","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/2607.16434/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"}