{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QYWYANICYKOEY7ROBHMMM6VSUW","short_pith_number":"pith:QYWYANIC","schema_version":"1.0","canonical_sha256":"862d803502c29c4c7e2e09d8c67ab2a5830956e54777e855946a47f83a55629a","source":{"kind":"arxiv","id":"1907.06081","version":3},"attestation_state":"computed","paper":{"title":"Preliminary study on the modal decomposition of Hermite Gaussian beams via deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"physics.optics","authors_text":"Jinyong Leng, Jun Li, Liangjin Huang, Lijia Yang, Pu Zhou, Tianyue Hou, Yi An","submitted_at":"2019-07-13T14:02:09Z","abstract_excerpt":"The Hermite-Gaussian (HG) modes make up a complete and orthonormal basis, which have been extensively used to describe optical fields. Here, we demonstrate, for the first time to our knowledge, deep learning-based modal decomposition (MD) of HG beams. This method offers a fast, economical and robust way to acquire both the power content and phase information through a single-shot beam intensity image, which will be beneficial for the beam shaping, beam quality assessment, studies of resonator perturbations, and other further research on the HG beams."},"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":"1907.06081","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.optics","submitted_at":"2019-07-13T14:02:09Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"8700e3d41b132c0b31fbeff2eec3507b601f35ea7596da378b9a867e87af7201","abstract_canon_sha256":"cabcf380595cda516b51255ecdf6c8d01d63c08c56585ce4c9065468e49cbe9a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:22.984648Z","signature_b64":"G80JYPAef9xF8X0X94z0+ZdFEJKgdzT1wnkT43np5FnYIF3yk4bsJgrGe6Rnx3yaD8sV1AJ1n9fEGUSOmTuVDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"862d803502c29c4c7e2e09d8c67ab2a5830956e54777e855946a47f83a55629a","last_reissued_at":"2026-05-17T23:40:22.983982Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:22.983982Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Preliminary study on the modal decomposition of Hermite Gaussian beams via deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"physics.optics","authors_text":"Jinyong Leng, Jun Li, Liangjin Huang, Lijia Yang, Pu Zhou, Tianyue Hou, Yi An","submitted_at":"2019-07-13T14:02:09Z","abstract_excerpt":"The Hermite-Gaussian (HG) modes make up a complete and orthonormal basis, which have been extensively used to describe optical fields. Here, we demonstrate, for the first time to our knowledge, deep learning-based modal decomposition (MD) of HG beams. This method offers a fast, economical and robust way to acquire both the power content and phase information through a single-shot beam intensity image, which will be beneficial for the beam shaping, beam quality assessment, studies of resonator perturbations, and other further research on the HG beams."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.06081","kind":"arxiv","version":3},"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":"1907.06081","created_at":"2026-05-17T23:40:22.984062+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.06081v3","created_at":"2026-05-17T23:40:22.984062+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.06081","created_at":"2026-05-17T23:40:22.984062+00:00"},{"alias_kind":"pith_short_12","alias_value":"QYWYANICYKOE","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"QYWYANICYKOEY7RO","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"QYWYANIC","created_at":"2026-05-18T12:33:27.125529+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/QYWYANICYKOEY7ROBHMMM6VSUW","json":"https://pith.science/pith/QYWYANICYKOEY7ROBHMMM6VSUW.json","graph_json":"https://pith.science/api/pith-number/QYWYANICYKOEY7ROBHMMM6VSUW/graph.json","events_json":"https://pith.science/api/pith-number/QYWYANICYKOEY7ROBHMMM6VSUW/events.json","paper":"https://pith.science/paper/QYWYANIC"},"agent_actions":{"view_html":"https://pith.science/pith/QYWYANICYKOEY7ROBHMMM6VSUW","download_json":"https://pith.science/pith/QYWYANICYKOEY7ROBHMMM6VSUW.json","view_paper":"https://pith.science/paper/QYWYANIC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.06081&json=true","fetch_graph":"https://pith.science/api/pith-number/QYWYANICYKOEY7ROBHMMM6VSUW/graph.json","fetch_events":"https://pith.science/api/pith-number/QYWYANICYKOEY7ROBHMMM6VSUW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QYWYANICYKOEY7ROBHMMM6VSUW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QYWYANICYKOEY7ROBHMMM6VSUW/action/storage_attestation","attest_author":"https://pith.science/pith/QYWYANICYKOEY7ROBHMMM6VSUW/action/author_attestation","sign_citation":"https://pith.science/pith/QYWYANICYKOEY7ROBHMMM6VSUW/action/citation_signature","submit_replication":"https://pith.science/pith/QYWYANICYKOEY7ROBHMMM6VSUW/action/replication_record"}},"created_at":"2026-05-17T23:40:22.984062+00:00","updated_at":"2026-05-17T23:40:22.984062+00:00"}