{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:JJBC6JLSYFSVYUBRNPL4BC5EWT","short_pith_number":"pith:JJBC6JLS","schema_version":"1.0","canonical_sha256":"4a422f2572c1655c50316bd7c08ba4b4c74626f3ac3a2744fa522bd047e4b3e9","source":{"kind":"arxiv","id":"1903.01247","version":1},"attestation_state":"computed","paper":{"title":"Review of High-Quality Random Number Generators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ex","hep-lat","physics.data-an"],"primary_cat":"physics.comp-ph","authors_text":"Frederick James, Lorenzo Moneta","submitted_at":"2019-03-04T14:02:38Z","abstract_excerpt":"This is a review of pseudorandom number generators (RNG's) of the highest quality, suitable for use in the most demanding Monte Carlo calculations. All the RNG's we recommend here are based on the Kolmogorov-Anosov theory of mixing in classical mechanical systems, which guarantees under certain conditions and in certain asymptotic limits, that points on the trajectories of these systems can be used to produce random number sequences of exceptional quality. We outline this theory of mixing and establish criteria for deciding which RNG's are sufficiently good approximations to the ideal mathemat"},"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":"1903.01247","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2019-03-04T14:02:38Z","cross_cats_sorted":["hep-ex","hep-lat","physics.data-an"],"title_canon_sha256":"e5d4829a5a86e1241079e8bbeb6c6318d1546098671c9dfea55565c6b8fd22e5","abstract_canon_sha256":"9b2afe48e90733ab9688c97c52b379506209895ac7f161c9513071d23fb757a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:47.488607Z","signature_b64":"RIgzhWKPYoCIkcSk3/9ODfY75cIWUpUchdLgkN4P50VkOpwuuGVfV3f4uKikyFWGIJWXTPeKGQybmdm09mPlDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a422f2572c1655c50316bd7c08ba4b4c74626f3ac3a2744fa522bd047e4b3e9","last_reissued_at":"2026-05-17T23:44:47.487785Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:47.487785Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Review of High-Quality Random Number Generators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ex","hep-lat","physics.data-an"],"primary_cat":"physics.comp-ph","authors_text":"Frederick James, Lorenzo Moneta","submitted_at":"2019-03-04T14:02:38Z","abstract_excerpt":"This is a review of pseudorandom number generators (RNG's) of the highest quality, suitable for use in the most demanding Monte Carlo calculations. All the RNG's we recommend here are based on the Kolmogorov-Anosov theory of mixing in classical mechanical systems, which guarantees under certain conditions and in certain asymptotic limits, that points on the trajectories of these systems can be used to produce random number sequences of exceptional quality. We outline this theory of mixing and establish criteria for deciding which RNG's are sufficiently good approximations to the ideal mathemat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.01247","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":"1903.01247","created_at":"2026-05-17T23:44:47.487889+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.01247v1","created_at":"2026-05-17T23:44:47.487889+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.01247","created_at":"2026-05-17T23:44:47.487889+00:00"},{"alias_kind":"pith_short_12","alias_value":"JJBC6JLSYFSV","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"JJBC6JLSYFSVYUBR","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"JJBC6JLS","created_at":"2026-05-18T12:33:21.387695+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2203.11601","citing_title":"A comprehensive guide to the physics and usage of PYTHIA 8.3","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JJBC6JLSYFSVYUBRNPL4BC5EWT","json":"https://pith.science/pith/JJBC6JLSYFSVYUBRNPL4BC5EWT.json","graph_json":"https://pith.science/api/pith-number/JJBC6JLSYFSVYUBRNPL4BC5EWT/graph.json","events_json":"https://pith.science/api/pith-number/JJBC6JLSYFSVYUBRNPL4BC5EWT/events.json","paper":"https://pith.science/paper/JJBC6JLS"},"agent_actions":{"view_html":"https://pith.science/pith/JJBC6JLSYFSVYUBRNPL4BC5EWT","download_json":"https://pith.science/pith/JJBC6JLSYFSVYUBRNPL4BC5EWT.json","view_paper":"https://pith.science/paper/JJBC6JLS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.01247&json=true","fetch_graph":"https://pith.science/api/pith-number/JJBC6JLSYFSVYUBRNPL4BC5EWT/graph.json","fetch_events":"https://pith.science/api/pith-number/JJBC6JLSYFSVYUBRNPL4BC5EWT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JJBC6JLSYFSVYUBRNPL4BC5EWT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JJBC6JLSYFSVYUBRNPL4BC5EWT/action/storage_attestation","attest_author":"https://pith.science/pith/JJBC6JLSYFSVYUBRNPL4BC5EWT/action/author_attestation","sign_citation":"https://pith.science/pith/JJBC6JLSYFSVYUBRNPL4BC5EWT/action/citation_signature","submit_replication":"https://pith.science/pith/JJBC6JLSYFSVYUBRNPL4BC5EWT/action/replication_record"}},"created_at":"2026-05-17T23:44:47.487889+00:00","updated_at":"2026-05-17T23:44:47.487889+00:00"}