{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:JKONZVAPQMGY6QDAYXLBWRIDDG","short_pith_number":"pith:JKONZVAP","schema_version":"1.0","canonical_sha256":"4a9cdcd40f830d8f4060c5d61b450319919c690287da81ffa5696492b9090839","source":{"kind":"arxiv","id":"1706.06097","version":2},"attestation_state":"computed","paper":{"title":"Control of accuracy in the Wang-Landau algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"cond-mat.stat-mech","authors_text":"L. N. Shchur, L. Yu. Barash, M. A. Fadeeva","submitted_at":"2017-06-19T18:00:00Z","abstract_excerpt":"The Wang-Landau (WL) algorithm has been widely used for simulations in many areas of physics. Our analysis of the WL algorithm explains its properties and shows that the difference of the largest eigenvalue of the transition matrix in the energy space from unity can be used to control the accuracy of estimating the density of states. Analytic expressions for the matrix elements are given in the case of the one-dimensional Ising model. The proposed method is further confirmed by numerical results for the one-dimensional and two-dimensional Ising models and also the two-dimensional Potts model."},"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":"1706.06097","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2017-06-19T18:00:00Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"6df713baf079976b767e6877d71ffd6c5ba3af1ae927678d14abf83368d722f9","abstract_canon_sha256":"7f7dac6ce54fcde5134d0c2e7ac86433a27e7b40d6e60c9abfb1612c72696631"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:32.478947Z","signature_b64":"0MQFVNC4M8sgW5agmvZ+3aP6pbjt7uu6oGdIrxqiV4EI0dE7AajJwpftDWDblrSjn2Oyqi2qjRckmICqRiW1DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a9cdcd40f830d8f4060c5d61b450319919c690287da81ffa5696492b9090839","last_reissued_at":"2026-05-18T00:32:32.478342Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:32.478342Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Control of accuracy in the Wang-Landau algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"cond-mat.stat-mech","authors_text":"L. N. Shchur, L. Yu. Barash, M. A. Fadeeva","submitted_at":"2017-06-19T18:00:00Z","abstract_excerpt":"The Wang-Landau (WL) algorithm has been widely used for simulations in many areas of physics. Our analysis of the WL algorithm explains its properties and shows that the difference of the largest eigenvalue of the transition matrix in the energy space from unity can be used to control the accuracy of estimating the density of states. Analytic expressions for the matrix elements are given in the case of the one-dimensional Ising model. The proposed method is further confirmed by numerical results for the one-dimensional and two-dimensional Ising models and also the two-dimensional Potts model."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.06097","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":"1706.06097","created_at":"2026-05-18T00:32:32.478427+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.06097v2","created_at":"2026-05-18T00:32:32.478427+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.06097","created_at":"2026-05-18T00:32:32.478427+00:00"},{"alias_kind":"pith_short_12","alias_value":"JKONZVAPQMGY","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_16","alias_value":"JKONZVAPQMGY6QDA","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_8","alias_value":"JKONZVAP","created_at":"2026-05-18T12:31:24.725408+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.10254","citing_title":"Algorithmic overlaps as thermodynamic variables: from local to cluster Monte Carlo dynamics in critical phenomena","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JKONZVAPQMGY6QDAYXLBWRIDDG","json":"https://pith.science/pith/JKONZVAPQMGY6QDAYXLBWRIDDG.json","graph_json":"https://pith.science/api/pith-number/JKONZVAPQMGY6QDAYXLBWRIDDG/graph.json","events_json":"https://pith.science/api/pith-number/JKONZVAPQMGY6QDAYXLBWRIDDG/events.json","paper":"https://pith.science/paper/JKONZVAP"},"agent_actions":{"view_html":"https://pith.science/pith/JKONZVAPQMGY6QDAYXLBWRIDDG","download_json":"https://pith.science/pith/JKONZVAPQMGY6QDAYXLBWRIDDG.json","view_paper":"https://pith.science/paper/JKONZVAP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.06097&json=true","fetch_graph":"https://pith.science/api/pith-number/JKONZVAPQMGY6QDAYXLBWRIDDG/graph.json","fetch_events":"https://pith.science/api/pith-number/JKONZVAPQMGY6QDAYXLBWRIDDG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JKONZVAPQMGY6QDAYXLBWRIDDG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JKONZVAPQMGY6QDAYXLBWRIDDG/action/storage_attestation","attest_author":"https://pith.science/pith/JKONZVAPQMGY6QDAYXLBWRIDDG/action/author_attestation","sign_citation":"https://pith.science/pith/JKONZVAPQMGY6QDAYXLBWRIDDG/action/citation_signature","submit_replication":"https://pith.science/pith/JKONZVAPQMGY6QDAYXLBWRIDDG/action/replication_record"}},"created_at":"2026-05-18T00:32:32.478427+00:00","updated_at":"2026-05-18T00:32:32.478427+00:00"}