{"paper":{"title":"Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Dejia Zhou, Minglin Wang, Xuelei Chen, Yan Gong","submitted_at":"2025-11-04T14:58:33Z","abstract_excerpt":"We study supernova (SN) classification using the Recurrent Neural Networks (RNNs) within the Chinese Space-station Survey Telescope Ultra-Deep Field (CSST-UDF) photometric survey and explore the improvements in cosmological constraints. We simulate Type Ia supernovae (SNe Ia) and core-collapse supernovae (CCSNe) using SNCosmo with SALT3 SN Ia model and CCSN templates, and apply the SuperNNova (SNN) program for classification. Our study indicates that the SNN combined with the Joint Light-curve Analysis cuts can enhance the purity of the CSST-UDF SN Ia sample up to over 99.5% with 2,193 SNe Ia "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.02631","kind":"arxiv","version":2},"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/2511.02631/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"}