{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:ISLSW52TUYP4BIWLWGZI27GU6R","short_pith_number":"pith:ISLSW52T","schema_version":"1.0","canonical_sha256":"44972b7753a61fc0a2cbb1b28d7cd4f4400cb29494057294811693bcca755f4e","source":{"kind":"arxiv","id":"2107.00734","version":2},"attestation_state":"computed","paper":{"title":"Flow-based sampling for multimodal and extended-mode distributions in lattice field theory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","cs.LG"],"primary_cat":"hep-lat","authors_text":"Chung-Chun Hsieh, Daniel C. Hackett, Denis Boyda, Gurtej Kanwar, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Michael S. Albergo, Phiala E. Shanahan, Sahil Pontula","submitted_at":"2021-07-01T20:22:10Z","abstract_excerpt":"Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory. In this paper, we present a set of training- and architecture-based methods to construct flow models for targets with multiple separated modes (i.e.~vacua) as well as targets with extended/continuous modes. We demonstrate the application of these methods to modeling two-dimensional real and complex scalar field theories in their symmetry-broken phases. In this context we investigate different flow-based sampling algorith"},"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":"2107.00734","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-lat","submitted_at":"2021-07-01T20:22:10Z","cross_cats_sorted":["cond-mat.stat-mech","cs.LG"],"title_canon_sha256":"ff1337744dd11da1469d517d1d69b0c4d8e4c7132cedc19d2089892e43f77a2c","abstract_canon_sha256":"349d7494bc3f22a827598f0502827d3b9353b6bef67391bd8c5c7cf739a12d8a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:14:33.913603Z","signature_b64":"mr7L0so1cajVGDt8GO5EYkBI5YHHz5jaLhFVWKIyEnK9nqeh54syLqzAiOaSGf8ZqkKwD7MG4fefSm92HoSFCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"44972b7753a61fc0a2cbb1b28d7cd4f4400cb29494057294811693bcca755f4e","last_reissued_at":"2026-07-05T10:14:33.913076Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:14:33.913076Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Flow-based sampling for multimodal and extended-mode distributions in lattice field theory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","cs.LG"],"primary_cat":"hep-lat","authors_text":"Chung-Chun Hsieh, Daniel C. Hackett, Denis Boyda, Gurtej Kanwar, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Michael S. Albergo, Phiala E. Shanahan, Sahil Pontula","submitted_at":"2021-07-01T20:22:10Z","abstract_excerpt":"Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory. In this paper, we present a set of training- and architecture-based methods to construct flow models for targets with multiple separated modes (i.e.~vacua) as well as targets with extended/continuous modes. We demonstrate the application of these methods to modeling two-dimensional real and complex scalar field theories in their symmetry-broken phases. In this context we investigate different flow-based sampling algorith"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.00734","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2107.00734/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2107.00734","created_at":"2026-07-05T10:14:33.913139+00:00"},{"alias_kind":"arxiv_version","alias_value":"2107.00734v2","created_at":"2026-07-05T10:14:33.913139+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.00734","created_at":"2026-07-05T10:14:33.913139+00:00"},{"alias_kind":"pith_short_12","alias_value":"ISLSW52TUYP4","created_at":"2026-07-05T10:14:33.913139+00:00"},{"alias_kind":"pith_short_16","alias_value":"ISLSW52TUYP4BIWL","created_at":"2026-07-05T10:14:33.913139+00:00"},{"alias_kind":"pith_short_8","alias_value":"ISLSW52T","created_at":"2026-07-05T10:14:33.913139+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01354","citing_title":"Local Conformal Predictions for Calibrated Surrogates","ref_index":238,"is_internal_anchor":false},{"citing_arxiv_id":"2308.08615","citing_title":"Improvement of Heatbath Algorithm in LFT using Generative models","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17511","citing_title":"A flow-matching generative model for event-by-event jet-induced hydro response in high-energy heavy-ion collisions","ref_index":92,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07262","citing_title":"Testing machine-learned distributions against Monte Carlo data for the QCD chiral phase transition","ref_index":54,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ISLSW52TUYP4BIWLWGZI27GU6R","json":"https://pith.science/pith/ISLSW52TUYP4BIWLWGZI27GU6R.json","graph_json":"https://pith.science/api/pith-number/ISLSW52TUYP4BIWLWGZI27GU6R/graph.json","events_json":"https://pith.science/api/pith-number/ISLSW52TUYP4BIWLWGZI27GU6R/events.json","paper":"https://pith.science/paper/ISLSW52T"},"agent_actions":{"view_html":"https://pith.science/pith/ISLSW52TUYP4BIWLWGZI27GU6R","download_json":"https://pith.science/pith/ISLSW52TUYP4BIWLWGZI27GU6R.json","view_paper":"https://pith.science/paper/ISLSW52T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2107.00734&json=true","fetch_graph":"https://pith.science/api/pith-number/ISLSW52TUYP4BIWLWGZI27GU6R/graph.json","fetch_events":"https://pith.science/api/pith-number/ISLSW52TUYP4BIWLWGZI27GU6R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ISLSW52TUYP4BIWLWGZI27GU6R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ISLSW52TUYP4BIWLWGZI27GU6R/action/storage_attestation","attest_author":"https://pith.science/pith/ISLSW52TUYP4BIWLWGZI27GU6R/action/author_attestation","sign_citation":"https://pith.science/pith/ISLSW52TUYP4BIWLWGZI27GU6R/action/citation_signature","submit_replication":"https://pith.science/pith/ISLSW52TUYP4BIWLWGZI27GU6R/action/replication_record"}},"created_at":"2026-07-05T10:14:33.913139+00:00","updated_at":"2026-07-05T10:14:33.913139+00:00"}