{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SG2ER6MHODNTV2EAO3JBP4YAFQ","short_pith_number":"pith:SG2ER6MH","schema_version":"1.0","canonical_sha256":"91b448f98770db3ae88076d217f3002c0c7024a34e65695e86f91ab7aa9cea46","source":{"kind":"arxiv","id":"2408.06147","version":1},"attestation_state":"computed","paper":{"title":"Self-Supervised Learning on MeerKAT Wide-Field Continuum Images","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Daniel Schaerer, Davide Piras, Erica Lastufka, Marc Audard, Mariia Drozdova, Miroslava Dessauges-Zavadsky, Olga Taran, Omkar Bait, Svyatoslav Voloshynovskiy, Taras Holotyak, Vitaliy Kinakh","submitted_at":"2024-08-12T13:42:27Z","abstract_excerpt":"Self-supervised learning (SSL) applied to natural images has demonstrated a remarkable ability to learn meaningful, low-dimension representations without labels, resulting in models that are adaptable to many different tasks. Until now, applications of SSL to astronomical images have been limited to Galaxy Zoo datasets, which require a significant amount of pre-processing to prepare sparse images centered on a single galaxy. With wide-field survey instruments at the forefront of the Square Kilometer Array (SKA) era, this approach to gathering training data is impractical. We demonstrate that c"},"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":"2408.06147","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2024-08-12T13:42:27Z","cross_cats_sorted":[],"title_canon_sha256":"e61dc81728cf6e25bf1c1cf695e44618c42e4739d818f4febc2ec3188375fd0c","abstract_canon_sha256":"cec5934a5fa161aa5eed78417330d2a637ae7c338669b87bab742d2b2645b78e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:23:56.715338Z","signature_b64":"ml65UFjJSJxg9TMeQYjFtPtGiga2XIVBOz+zs+iSAlNk3L/tCyj5F6IFCa4dQgAn5tEacJJF1f/G4q9BwOvEBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91b448f98770db3ae88076d217f3002c0c7024a34e65695e86f91ab7aa9cea46","last_reissued_at":"2026-07-05T09:23:56.714774Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:23:56.714774Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Supervised Learning on MeerKAT Wide-Field Continuum Images","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Daniel Schaerer, Davide Piras, Erica Lastufka, Marc Audard, Mariia Drozdova, Miroslava Dessauges-Zavadsky, Olga Taran, Omkar Bait, Svyatoslav Voloshynovskiy, Taras Holotyak, Vitaliy Kinakh","submitted_at":"2024-08-12T13:42:27Z","abstract_excerpt":"Self-supervised learning (SSL) applied to natural images has demonstrated a remarkable ability to learn meaningful, low-dimension representations without labels, resulting in models that are adaptable to many different tasks. Until now, applications of SSL to astronomical images have been limited to Galaxy Zoo datasets, which require a significant amount of pre-processing to prepare sparse images centered on a single galaxy. With wide-field survey instruments at the forefront of the Square Kilometer Array (SKA) era, this approach to gathering training data is impractical. We demonstrate that c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.06147","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/2408.06147/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":"2408.06147","created_at":"2026-07-05T09:23:56.714839+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.06147v1","created_at":"2026-07-05T09:23:56.714839+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.06147","created_at":"2026-07-05T09:23:56.714839+00:00"},{"alias_kind":"pith_short_12","alias_value":"SG2ER6MHODNT","created_at":"2026-07-05T09:23:56.714839+00:00"},{"alias_kind":"pith_short_16","alias_value":"SG2ER6MHODNTV2EA","created_at":"2026-07-05T09:23:56.714839+00:00"},{"alias_kind":"pith_short_8","alias_value":"SG2ER6MH","created_at":"2026-07-05T09:23:56.714839+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.03736","citing_title":"Source Finding and Characterisation for SKAO Science","ref_index":78,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SG2ER6MHODNTV2EAO3JBP4YAFQ","json":"https://pith.science/pith/SG2ER6MHODNTV2EAO3JBP4YAFQ.json","graph_json":"https://pith.science/api/pith-number/SG2ER6MHODNTV2EAO3JBP4YAFQ/graph.json","events_json":"https://pith.science/api/pith-number/SG2ER6MHODNTV2EAO3JBP4YAFQ/events.json","paper":"https://pith.science/paper/SG2ER6MH"},"agent_actions":{"view_html":"https://pith.science/pith/SG2ER6MHODNTV2EAO3JBP4YAFQ","download_json":"https://pith.science/pith/SG2ER6MHODNTV2EAO3JBP4YAFQ.json","view_paper":"https://pith.science/paper/SG2ER6MH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.06147&json=true","fetch_graph":"https://pith.science/api/pith-number/SG2ER6MHODNTV2EAO3JBP4YAFQ/graph.json","fetch_events":"https://pith.science/api/pith-number/SG2ER6MHODNTV2EAO3JBP4YAFQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SG2ER6MHODNTV2EAO3JBP4YAFQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SG2ER6MHODNTV2EAO3JBP4YAFQ/action/storage_attestation","attest_author":"https://pith.science/pith/SG2ER6MHODNTV2EAO3JBP4YAFQ/action/author_attestation","sign_citation":"https://pith.science/pith/SG2ER6MHODNTV2EAO3JBP4YAFQ/action/citation_signature","submit_replication":"https://pith.science/pith/SG2ER6MHODNTV2EAO3JBP4YAFQ/action/replication_record"}},"created_at":"2026-07-05T09:23:56.714839+00:00","updated_at":"2026-07-05T09:23:56.714839+00:00"}