{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:7D2O4PCVFNBHMO7UNXXG3DSWQ3","short_pith_number":"pith:7D2O4PCV","schema_version":"1.0","canonical_sha256":"f8f4ee3c552b42763bf46dee6d8e5686e1b40d057f71dff89ab68604fc961e61","source":{"kind":"arxiv","id":"1703.05128","version":2},"attestation_state":"computed","paper":{"title":"DeepVel: deep learning for the estimation of horizontal velocities at the solar surface","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"astro-ph.SR","authors_text":"2), 2) ((1) Instituto de Astrofisica de Canarias, (2) Universidad de La Laguna), A. Asensio Ramos (1, I. S. Requerey (1, N. Vitas (1","submitted_at":"2017-03-15T12:49:07Z","abstract_excerpt":"Many phenomena taking place in the solar photosphere are controlled by plasma motions. Although the line-of-sight component of the velocity can be estimated using the Doppler effect, we do not have direct spectroscopic access to the components that are perpendicular to the line-of-sight. These components are typically estimated using methods based on local correlation tracking. We have designed DeepVel, an end-to-end deep neural network that produces an estimation of the velocity at every single pixel and at every time step and at three different heights in the atmosphere from just two consecu"},"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":"1703.05128","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.SR","submitted_at":"2017-03-15T12:49:07Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"b194824f6d398c1c4a57d1783c477dfcd562e6d926dc0609c228ec7e4dab856e","abstract_canon_sha256":"bb6817d9cde4703298ead9ba9ed38da31e7cf7bf1c5ee0b428180fc1f2b120ff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:56.743872Z","signature_b64":"DdjFUJxVlBD+sP9xbrkRzYh84yvyD92ucQc+LFvPi7j6U+j29RQKEyD0PJK2gT/xyLPGDzLSTX26Z6OtRuIMDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f8f4ee3c552b42763bf46dee6d8e5686e1b40d057f71dff89ab68604fc961e61","last_reissued_at":"2026-05-18T00:38:56.743304Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:56.743304Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DeepVel: deep learning for the estimation of horizontal velocities at the solar surface","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"astro-ph.SR","authors_text":"2), 2) ((1) Instituto de Astrofisica de Canarias, (2) Universidad de La Laguna), A. Asensio Ramos (1, I. S. Requerey (1, N. Vitas (1","submitted_at":"2017-03-15T12:49:07Z","abstract_excerpt":"Many phenomena taking place in the solar photosphere are controlled by plasma motions. Although the line-of-sight component of the velocity can be estimated using the Doppler effect, we do not have direct spectroscopic access to the components that are perpendicular to the line-of-sight. These components are typically estimated using methods based on local correlation tracking. We have designed DeepVel, an end-to-end deep neural network that produces an estimation of the velocity at every single pixel and at every time step and at three different heights in the atmosphere from just two consecu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.05128","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":""},"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":"1703.05128","created_at":"2026-05-18T00:38:56.743392+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.05128v2","created_at":"2026-05-18T00:38:56.743392+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.05128","created_at":"2026-05-18T00:38:56.743392+00:00"},{"alias_kind":"pith_short_12","alias_value":"7D2O4PCVFNBH","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"7D2O4PCVFNBHMO7U","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"7D2O4PCV","created_at":"2026-05-18T12:31:03.183658+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7D2O4PCVFNBHMO7UNXXG3DSWQ3","json":"https://pith.science/pith/7D2O4PCVFNBHMO7UNXXG3DSWQ3.json","graph_json":"https://pith.science/api/pith-number/7D2O4PCVFNBHMO7UNXXG3DSWQ3/graph.json","events_json":"https://pith.science/api/pith-number/7D2O4PCVFNBHMO7UNXXG3DSWQ3/events.json","paper":"https://pith.science/paper/7D2O4PCV"},"agent_actions":{"view_html":"https://pith.science/pith/7D2O4PCVFNBHMO7UNXXG3DSWQ3","download_json":"https://pith.science/pith/7D2O4PCVFNBHMO7UNXXG3DSWQ3.json","view_paper":"https://pith.science/paper/7D2O4PCV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.05128&json=true","fetch_graph":"https://pith.science/api/pith-number/7D2O4PCVFNBHMO7UNXXG3DSWQ3/graph.json","fetch_events":"https://pith.science/api/pith-number/7D2O4PCVFNBHMO7UNXXG3DSWQ3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7D2O4PCVFNBHMO7UNXXG3DSWQ3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7D2O4PCVFNBHMO7UNXXG3DSWQ3/action/storage_attestation","attest_author":"https://pith.science/pith/7D2O4PCVFNBHMO7UNXXG3DSWQ3/action/author_attestation","sign_citation":"https://pith.science/pith/7D2O4PCVFNBHMO7UNXXG3DSWQ3/action/citation_signature","submit_replication":"https://pith.science/pith/7D2O4PCVFNBHMO7UNXXG3DSWQ3/action/replication_record"}},"created_at":"2026-05-18T00:38:56.743392+00:00","updated_at":"2026-05-18T00:38:56.743392+00:00"}