{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2F2D5SSMM3RDGUZCX5HLBBGGCT","short_pith_number":"pith:2F2D5SSM","schema_version":"1.0","canonical_sha256":"d1743eca4c66e2335322bf4eb084c614f7edd2a82c05273d1f440d480c393c16","source":{"kind":"arxiv","id":"2501.01070","version":1},"attestation_state":"computed","paper":{"title":"SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy: I. Architecture and Automated Redshift Measurement","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.GA"],"primary_cat":"astro-ph.IM","authors_text":"Clive Binu, Jeyhan S. Kartaltepe, Rohan Pattnaik","submitted_at":"2025-01-02T05:40:39Z","abstract_excerpt":"We introduce the Spectroscopy Pre-trained Transformer (SpecPT), a transformer-based model designed to analyze spectroscopic data, with applications in spectrum reconstruction and redshift measurement. Using the Early Data Release (EDR) of the DESI survey, we evaluate SpecPT's performance on two distinct datasets: the Bright Galaxy Survey (BGS) and Emission Line Galaxy (ELG) samples. SpecPT successfully reconstructs spectra, accurately capturing emission lines, absorption features, and continuum shapes while effectively reducing noise. For redshift prediction, SpecPT achieves competitive accura"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.01070","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2025-01-02T05:40:39Z","cross_cats_sorted":["astro-ph.GA"],"title_canon_sha256":"a37f27755539f971539d7e53ace7d47a04dc2e03fd9c276c148a697804fce1ac","abstract_canon_sha256":"2c6d14d998f917a728a1e9edb9dbfb33ab13f6ca7f9ef97982b4399c4a272578"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:19:23.392144Z","signature_b64":"oqJ5tlcYwrU5DrrKjNv5lkwAIRoR1dvTQLeyevPH8hH4rz4jSqSn66P3FOA0jjHmPysIjD9UDBvF3vOSFEuxDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d1743eca4c66e2335322bf4eb084c614f7edd2a82c05273d1f440d480c393c16","last_reissued_at":"2026-07-05T11:19:23.391743Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:19:23.391743Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy: I. Architecture and Automated Redshift Measurement","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.GA"],"primary_cat":"astro-ph.IM","authors_text":"Clive Binu, Jeyhan S. Kartaltepe, Rohan Pattnaik","submitted_at":"2025-01-02T05:40:39Z","abstract_excerpt":"We introduce the Spectroscopy Pre-trained Transformer (SpecPT), a transformer-based model designed to analyze spectroscopic data, with applications in spectrum reconstruction and redshift measurement. Using the Early Data Release (EDR) of the DESI survey, we evaluate SpecPT's performance on two distinct datasets: the Bright Galaxy Survey (BGS) and Emission Line Galaxy (ELG) samples. SpecPT successfully reconstructs spectra, accurately capturing emission lines, absorption features, and continuum shapes while effectively reducing noise. For redshift prediction, SpecPT achieves competitive accura"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.01070","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/2501.01070/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.01070","created_at":"2026-07-05T11:19:23.391798+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.01070v1","created_at":"2026-07-05T11:19:23.391798+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.01070","created_at":"2026-07-05T11:19:23.391798+00:00"},{"alias_kind":"pith_short_12","alias_value":"2F2D5SSMM3RD","created_at":"2026-07-05T11:19:23.391798+00:00"},{"alias_kind":"pith_short_16","alias_value":"2F2D5SSMM3RDGUZC","created_at":"2026-07-05T11:19:23.391798+00:00"},{"alias_kind":"pith_short_8","alias_value":"2F2D5SSM","created_at":"2026-07-05T11:19:23.391798+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2602.15021","citing_title":"Generalization from Low- to Moderate-Resolution Spectra with Neural Networks for Stellar Parameter Estimation: A Case Study with DESI","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2F2D5SSMM3RDGUZCX5HLBBGGCT","json":"https://pith.science/pith/2F2D5SSMM3RDGUZCX5HLBBGGCT.json","graph_json":"https://pith.science/api/pith-number/2F2D5SSMM3RDGUZCX5HLBBGGCT/graph.json","events_json":"https://pith.science/api/pith-number/2F2D5SSMM3RDGUZCX5HLBBGGCT/events.json","paper":"https://pith.science/paper/2F2D5SSM"},"agent_actions":{"view_html":"https://pith.science/pith/2F2D5SSMM3RDGUZCX5HLBBGGCT","download_json":"https://pith.science/pith/2F2D5SSMM3RDGUZCX5HLBBGGCT.json","view_paper":"https://pith.science/paper/2F2D5SSM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.01070&json=true","fetch_graph":"https://pith.science/api/pith-number/2F2D5SSMM3RDGUZCX5HLBBGGCT/graph.json","fetch_events":"https://pith.science/api/pith-number/2F2D5SSMM3RDGUZCX5HLBBGGCT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2F2D5SSMM3RDGUZCX5HLBBGGCT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2F2D5SSMM3RDGUZCX5HLBBGGCT/action/storage_attestation","attest_author":"https://pith.science/pith/2F2D5SSMM3RDGUZCX5HLBBGGCT/action/author_attestation","sign_citation":"https://pith.science/pith/2F2D5SSMM3RDGUZCX5HLBBGGCT/action/citation_signature","submit_replication":"https://pith.science/pith/2F2D5SSMM3RDGUZCX5HLBBGGCT/action/replication_record"}},"created_at":"2026-07-05T11:19:23.391798+00:00","updated_at":"2026-07-05T11:19:23.391798+00:00"}