{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:N4NSIXLTKQ6WLH4ZJOY4RTVAPA","short_pith_number":"pith:N4NSIXLT","schema_version":"1.0","canonical_sha256":"6f1b245d73543d659f994bb1c8cea0780dd3a424d751f7408673f4d9f51bd537","source":{"kind":"arxiv","id":"2505.10976","version":1},"attestation_state":"computed","paper":{"title":"Automated quasar continuum estimation using neural networks: a comparative study of deep-learning architectures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.GA","authors_text":"Agnieszka Pollo, Angela Iovino, Daniela Vergani, David N. A. Murphy, Elena S. Mangola, Francesco Pistis, Ignasi P\\'erez-R\\'afols, Katarzyna Ma{\\l}ek, Margherita Grespan, Matteo Fossati, Matthew M. Pieri, Michele Fumagalli, Rajeshwari Dutta, Sean Morrison, Trystyn Berg, William J. Pearson","submitted_at":"2025-05-16T08:24:13Z","abstract_excerpt":"Context. Ongoing and upcoming large spectroscopic surveys are drastically increasing the number of observed quasar spectra, requiring the development of fast and accurate automated methods to estimate spectral continua. Aims. This study evaluates the performance of three neural networks (NN) - an autoencoder, a convolutional NN (CNN), and a U-Net - in predicting quasar continua within the rest-frame wavelength range of $1020~\\text{\\AA}$ to $2000~\\text{\\AA}$. The ability to generalize and predict galaxy continua within the range of $3500~\\text{\\AA}$ to $5500~\\text{\\AA}$ is also tested. Methods."},"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":"2505.10976","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.GA","submitted_at":"2025-05-16T08:24:13Z","cross_cats_sorted":["astro-ph.IM"],"title_canon_sha256":"c9d4ebb63408b5e8778ad2a559c353de596aca3898504126346675bede4596f3","abstract_canon_sha256":"fb31af8d4d71ffc4aa9262b3910f5595917aedd3d5e22b484ac18cee3dc6b4a2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:26:31.429040Z","signature_b64":"X3QJLg8Dkz2ef3MU9HcmfBJcAb4Hcj8ukTNI+01kEcqRRbS0ly81/JWa3/b62SFSqB8zVGsajKAh+1zvt1FABQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f1b245d73543d659f994bb1c8cea0780dd3a424d751f7408673f4d9f51bd537","last_reissued_at":"2026-07-05T11:26:31.428475Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:26:31.428475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automated quasar continuum estimation using neural networks: a comparative study of deep-learning architectures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.GA","authors_text":"Agnieszka Pollo, Angela Iovino, Daniela Vergani, David N. A. Murphy, Elena S. Mangola, Francesco Pistis, Ignasi P\\'erez-R\\'afols, Katarzyna Ma{\\l}ek, Margherita Grespan, Matteo Fossati, Matthew M. Pieri, Michele Fumagalli, Rajeshwari Dutta, Sean Morrison, Trystyn Berg, William J. Pearson","submitted_at":"2025-05-16T08:24:13Z","abstract_excerpt":"Context. Ongoing and upcoming large spectroscopic surveys are drastically increasing the number of observed quasar spectra, requiring the development of fast and accurate automated methods to estimate spectral continua. Aims. This study evaluates the performance of three neural networks (NN) - an autoencoder, a convolutional NN (CNN), and a U-Net - in predicting quasar continua within the rest-frame wavelength range of $1020~\\text{\\AA}$ to $2000~\\text{\\AA}$. The ability to generalize and predict galaxy continua within the range of $3500~\\text{\\AA}$ to $5500~\\text{\\AA}$ is also tested. Methods."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10976","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/2505.10976/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":"2505.10976","created_at":"2026-07-05T11:26:31.428546+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.10976v1","created_at":"2026-07-05T11:26:31.428546+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10976","created_at":"2026-07-05T11:26:31.428546+00:00"},{"alias_kind":"pith_short_12","alias_value":"N4NSIXLTKQ6W","created_at":"2026-07-05T11:26:31.428546+00:00"},{"alias_kind":"pith_short_16","alias_value":"N4NSIXLTKQ6WLH4Z","created_at":"2026-07-05T11:26:31.428546+00:00"},{"alias_kind":"pith_short_8","alias_value":"N4NSIXLT","created_at":"2026-07-05T11:26:31.428546+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.22489","citing_title":"Machine Learning Techniques for Astrophysics and Cosmology: Lyman-$\\alpha$ forest","ref_index":178,"is_internal_anchor":false},{"citing_arxiv_id":"2509.14322","citing_title":"Probing the limits of cosmological information from the Lyman-$\\alpha$ forest 2-point correlation functions","ref_index":47,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/N4NSIXLTKQ6WLH4ZJOY4RTVAPA","json":"https://pith.science/pith/N4NSIXLTKQ6WLH4ZJOY4RTVAPA.json","graph_json":"https://pith.science/api/pith-number/N4NSIXLTKQ6WLH4ZJOY4RTVAPA/graph.json","events_json":"https://pith.science/api/pith-number/N4NSIXLTKQ6WLH4ZJOY4RTVAPA/events.json","paper":"https://pith.science/paper/N4NSIXLT"},"agent_actions":{"view_html":"https://pith.science/pith/N4NSIXLTKQ6WLH4ZJOY4RTVAPA","download_json":"https://pith.science/pith/N4NSIXLTKQ6WLH4ZJOY4RTVAPA.json","view_paper":"https://pith.science/paper/N4NSIXLT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.10976&json=true","fetch_graph":"https://pith.science/api/pith-number/N4NSIXLTKQ6WLH4ZJOY4RTVAPA/graph.json","fetch_events":"https://pith.science/api/pith-number/N4NSIXLTKQ6WLH4ZJOY4RTVAPA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N4NSIXLTKQ6WLH4ZJOY4RTVAPA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N4NSIXLTKQ6WLH4ZJOY4RTVAPA/action/storage_attestation","attest_author":"https://pith.science/pith/N4NSIXLTKQ6WLH4ZJOY4RTVAPA/action/author_attestation","sign_citation":"https://pith.science/pith/N4NSIXLTKQ6WLH4ZJOY4RTVAPA/action/citation_signature","submit_replication":"https://pith.science/pith/N4NSIXLTKQ6WLH4ZJOY4RTVAPA/action/replication_record"}},"created_at":"2026-07-05T11:26:31.428546+00:00","updated_at":"2026-07-05T11:26:31.428546+00:00"}