{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:CATJ7WRE7UDVDXK43T35AAEOD7","short_pith_number":"pith:CATJ7WRE","schema_version":"1.0","canonical_sha256":"10269fda24fd0751dd5cdcf7d0008e1fc067180e374a9e30a57f0c2e5325d536","source":{"kind":"arxiv","id":"1807.10733","version":1},"attestation_state":"computed","paper":{"title":"On the use of machine learning algorithms in the measurement of stellar magnetic fields","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.SR","authors_text":"C. Ya\\~nez-Marquez, J.C. Ramirez-Velez, J.P. Cordova-Barbosa","submitted_at":"2018-07-27T17:08:09Z","abstract_excerpt":"Regression methods based in Machine Learning Algorithms (MLA) have become an important tool for data analysis in many different disciplines.\n  In this work, we use MLA in an astrophysical context; our goal is to measure the mean longitudinal magnetic field in stars (H_ eff) from polarized spectra of high resolution, through the inversion of the so-called multi-line profiles.\n  Using synthetic data, we tested the performance of our technique considering different noise levels: In an ideal scenario of noise-free multi-line profiles, the inversion results are excellent; however, the accuracy of t"},"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":"1807.10733","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.SR","submitted_at":"2018-07-27T17:08:09Z","cross_cats_sorted":[],"title_canon_sha256":"b72376cd1e729de78df15426f14517754b02756b76bef2906bf6bcd489927a66","abstract_canon_sha256":"e50cf081b2f2ce025cf561c1c3f1c9bf76877729244590b650275d0a6df2d811"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:28.960367Z","signature_b64":"Fd1aYQtWvxS9SAsAy3FAfgBwSXbYDq4XloVHbsxm6Ux/PVBDpjv2GQDANBIwypAqRznFDnTFi0b7u/CZ735bAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10269fda24fd0751dd5cdcf7d0008e1fc067180e374a9e30a57f0c2e5325d536","last_reissued_at":"2026-05-18T00:01:28.959904Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:28.959904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the use of machine learning algorithms in the measurement of stellar magnetic fields","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.SR","authors_text":"C. Ya\\~nez-Marquez, J.C. Ramirez-Velez, J.P. Cordova-Barbosa","submitted_at":"2018-07-27T17:08:09Z","abstract_excerpt":"Regression methods based in Machine Learning Algorithms (MLA) have become an important tool for data analysis in many different disciplines.\n  In this work, we use MLA in an astrophysical context; our goal is to measure the mean longitudinal magnetic field in stars (H_ eff) from polarized spectra of high resolution, through the inversion of the so-called multi-line profiles.\n  Using synthetic data, we tested the performance of our technique considering different noise levels: In an ideal scenario of noise-free multi-line profiles, the inversion results are excellent; however, the accuracy of t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.10733","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":""},"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":"1807.10733","created_at":"2026-05-18T00:01:28.959973+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.10733v1","created_at":"2026-05-18T00:01:28.959973+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.10733","created_at":"2026-05-18T00:01:28.959973+00:00"},{"alias_kind":"pith_short_12","alias_value":"CATJ7WRE7UDV","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"CATJ7WRE7UDVDXK4","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"CATJ7WRE","created_at":"2026-05-18T12:32:16.446611+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/CATJ7WRE7UDVDXK43T35AAEOD7","json":"https://pith.science/pith/CATJ7WRE7UDVDXK43T35AAEOD7.json","graph_json":"https://pith.science/api/pith-number/CATJ7WRE7UDVDXK43T35AAEOD7/graph.json","events_json":"https://pith.science/api/pith-number/CATJ7WRE7UDVDXK43T35AAEOD7/events.json","paper":"https://pith.science/paper/CATJ7WRE"},"agent_actions":{"view_html":"https://pith.science/pith/CATJ7WRE7UDVDXK43T35AAEOD7","download_json":"https://pith.science/pith/CATJ7WRE7UDVDXK43T35AAEOD7.json","view_paper":"https://pith.science/paper/CATJ7WRE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.10733&json=true","fetch_graph":"https://pith.science/api/pith-number/CATJ7WRE7UDVDXK43T35AAEOD7/graph.json","fetch_events":"https://pith.science/api/pith-number/CATJ7WRE7UDVDXK43T35AAEOD7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CATJ7WRE7UDVDXK43T35AAEOD7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CATJ7WRE7UDVDXK43T35AAEOD7/action/storage_attestation","attest_author":"https://pith.science/pith/CATJ7WRE7UDVDXK43T35AAEOD7/action/author_attestation","sign_citation":"https://pith.science/pith/CATJ7WRE7UDVDXK43T35AAEOD7/action/citation_signature","submit_replication":"https://pith.science/pith/CATJ7WRE7UDVDXK43T35AAEOD7/action/replication_record"}},"created_at":"2026-05-18T00:01:28.959973+00:00","updated_at":"2026-05-18T00:01:28.959973+00:00"}