{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:MWIDCEAFQGCLJVGWQH4OU2Z4J6","short_pith_number":"pith:MWIDCEAF","schema_version":"1.0","canonical_sha256":"65903110058184b4d4d681f8ea6b3c4f811278cb4f61040e9b5300a8ebb8c79f","source":{"kind":"arxiv","id":"2209.04505","version":2},"attestation_state":"computed","paper":{"title":"Phase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable Simulations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.acc-ph","authors_text":"Auralee Edelen, Christopher Mayes, Daniel Ratner, Eric Wisniewski, John Power, Juan Pablo Gonzalez-Aguilera, Ryan Roussel, Seongyeol Kim","submitted_at":"2022-09-09T20:04:09Z","abstract_excerpt":"Characterizing the phase space distribution of particle beams in accelerators is a central part of accelerator understanding and performance optimization. However, conventional reconstruction-based techniques either use simplifying assumptions or require specialized diagnostics to infer high-dimensional ($>$ 2D) beam properties. In this Letter, we introduce a general-purpose algorithm that combines neural networks with differentiable particle tracking to efficiently reconstruct high-dimensional phase space distributions without using specialized beam diagnostics or beam manipulations. We demon"},"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":"2209.04505","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.acc-ph","submitted_at":"2022-09-09T20:04:09Z","cross_cats_sorted":[],"title_canon_sha256":"5818bdefa137253a9e97832dc6fb6e16fd13947b778180ceb5f684f8f45f0ed9","abstract_canon_sha256":"a269b857c90bf78974fb7d2004d76cef0c89bdb2f8eb5f7fbcf8beb9c866962e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:02:23.292758Z","signature_b64":"6u5GpKWLfZ3eE1VyY4gyM7OZF92pBudsKIKbnw7qblZQbFnxwwX4ykh4mB+BfUgOzzKnV8Ci7Trxph4JppBMAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"65903110058184b4d4d681f8ea6b3c4f811278cb4f61040e9b5300a8ebb8c79f","last_reissued_at":"2026-07-05T06:02:23.292252Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:02:23.292252Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Phase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable Simulations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.acc-ph","authors_text":"Auralee Edelen, Christopher Mayes, Daniel Ratner, Eric Wisniewski, John Power, Juan Pablo Gonzalez-Aguilera, Ryan Roussel, Seongyeol Kim","submitted_at":"2022-09-09T20:04:09Z","abstract_excerpt":"Characterizing the phase space distribution of particle beams in accelerators is a central part of accelerator understanding and performance optimization. However, conventional reconstruction-based techniques either use simplifying assumptions or require specialized diagnostics to infer high-dimensional ($>$ 2D) beam properties. In this Letter, we introduce a general-purpose algorithm that combines neural networks with differentiable particle tracking to efficiently reconstruct high-dimensional phase space distributions without using specialized beam diagnostics or beam manipulations. We demon"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.04505","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/2209.04505/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":"2209.04505","created_at":"2026-07-05T06:02:23.292309+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.04505v2","created_at":"2026-07-05T06:02:23.292309+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.04505","created_at":"2026-07-05T06:02:23.292309+00:00"},{"alias_kind":"pith_short_12","alias_value":"MWIDCEAFQGCL","created_at":"2026-07-05T06:02:23.292309+00:00"},{"alias_kind":"pith_short_16","alias_value":"MWIDCEAFQGCLJVGW","created_at":"2026-07-05T06:02:23.292309+00:00"},{"alias_kind":"pith_short_8","alias_value":"MWIDCEAF","created_at":"2026-07-05T06:02:23.292309+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/MWIDCEAFQGCLJVGWQH4OU2Z4J6","json":"https://pith.science/pith/MWIDCEAFQGCLJVGWQH4OU2Z4J6.json","graph_json":"https://pith.science/api/pith-number/MWIDCEAFQGCLJVGWQH4OU2Z4J6/graph.json","events_json":"https://pith.science/api/pith-number/MWIDCEAFQGCLJVGWQH4OU2Z4J6/events.json","paper":"https://pith.science/paper/MWIDCEAF"},"agent_actions":{"view_html":"https://pith.science/pith/MWIDCEAFQGCLJVGWQH4OU2Z4J6","download_json":"https://pith.science/pith/MWIDCEAFQGCLJVGWQH4OU2Z4J6.json","view_paper":"https://pith.science/paper/MWIDCEAF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.04505&json=true","fetch_graph":"https://pith.science/api/pith-number/MWIDCEAFQGCLJVGWQH4OU2Z4J6/graph.json","fetch_events":"https://pith.science/api/pith-number/MWIDCEAFQGCLJVGWQH4OU2Z4J6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MWIDCEAFQGCLJVGWQH4OU2Z4J6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MWIDCEAFQGCLJVGWQH4OU2Z4J6/action/storage_attestation","attest_author":"https://pith.science/pith/MWIDCEAFQGCLJVGWQH4OU2Z4J6/action/author_attestation","sign_citation":"https://pith.science/pith/MWIDCEAFQGCLJVGWQH4OU2Z4J6/action/citation_signature","submit_replication":"https://pith.science/pith/MWIDCEAFQGCLJVGWQH4OU2Z4J6/action/replication_record"}},"created_at":"2026-07-05T06:02:23.292309+00:00","updated_at":"2026-07-05T06:02:23.292309+00:00"}