{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:7T7JG3ETMPTYH5UWYKDHHLRAK6","short_pith_number":"pith:7T7JG3ET","canonical_record":{"source":{"id":"2211.04890","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2022-11-09T13:50:45Z","cross_cats_sorted":["cs.LG","hep-ex"],"title_canon_sha256":"dda9420b75be905201dcef4e496d30e0040318a71e6875559faac1551f9b9825","abstract_canon_sha256":"057ba36ca9cdb41336da841903172b35c70a5d78a17bba98218a80202514a31a"},"schema_version":"1.0"},"canonical_sha256":"fcfe936c9363e783f696c28673ae2057bd6a0b7f119d379e15285865269ee9c0","source":{"kind":"arxiv","id":"2211.04890","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.04890","created_at":"2026-07-05T06:21:54Z"},{"alias_kind":"arxiv_version","alias_value":"2211.04890v1","created_at":"2026-07-05T06:21:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.04890","created_at":"2026-07-05T06:21:54Z"},{"alias_kind":"pith_short_12","alias_value":"7T7JG3ETMPTY","created_at":"2026-07-05T06:21:54Z"},{"alias_kind":"pith_short_16","alias_value":"7T7JG3ETMPTYH5UW","created_at":"2026-07-05T06:21:54Z"},{"alias_kind":"pith_short_8","alias_value":"7T7JG3ET","created_at":"2026-07-05T06:21:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:7T7JG3ETMPTYH5UWYKDHHLRAK6","target":"record","payload":{"canonical_record":{"source":{"id":"2211.04890","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2022-11-09T13:50:45Z","cross_cats_sorted":["cs.LG","hep-ex"],"title_canon_sha256":"dda9420b75be905201dcef4e496d30e0040318a71e6875559faac1551f9b9825","abstract_canon_sha256":"057ba36ca9cdb41336da841903172b35c70a5d78a17bba98218a80202514a31a"},"schema_version":"1.0"},"canonical_sha256":"fcfe936c9363e783f696c28673ae2057bd6a0b7f119d379e15285865269ee9c0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:21:54.932035Z","signature_b64":"ESfaoAG+l+aGVnuFfH8ANcxz6od6ieLD5adZNk6V+tSS8HjbLYKjIRd69mL1ZQIEFvfhmiwWC058X8PEwv21BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fcfe936c9363e783f696c28673ae2057bd6a0b7f119d379e15285865269ee9c0","last_reissued_at":"2026-07-05T06:21:54.931576Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:21:54.931576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.04890","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:21:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z+JcW3ERl9LYKeErqzN7lDg92ayIpwMBexZo7J9/I+j8LoX2nQzaW0UV0fJDjlRFnt/Oua/YzoDE7a/EonuSCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T14:48:04.448149Z"},"content_sha256":"9273e7fb22e1f87d16b551cf14251d8a0e9f00c8a2364e0130d07acebf3563b5","schema_version":"1.0","event_id":"sha256:9273e7fb22e1f87d16b551cf14251d8a0e9f00c8a2364e0130d07acebf3563b5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:7T7JG3ETMPTYH5UWYKDHHLRAK6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic field","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","hep-ex"],"primary_cat":"physics.data-an","authors_text":"Andr\\'e Rubbia, Clark McGrew, Davide Sgalaberna, Sa\\'ul Alonso-Monsalve, Xingyu Zhao","submitted_at":"2022-11-09T13:50:45Z","abstract_excerpt":"In this article, we use artificial intelligence algorithms to show how to enhance the resolution of the elementary particle track fitting in inhomogeneous dense detectors, such as plastic scintillators. We use deep learning to replace more traditional Bayesian filtering methods, drastically improving the reconstruction of the interacting particle kinematics. We show that a specific form of neural network, inherited from the field of natural language processing, is very close to the concept of a Bayesian filter that adopts a hyper-informative prior. Such a paradigm change can influence the desi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.04890","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2211.04890/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:21:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oBqA7d9SwGuDuA/7t4A/s5N7h0qQZxOicmhSihE4vICB/De4Y/3URJb3EnUBxKewPTocNybooOPIKIf8YYmRAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T14:48:04.448810Z"},"content_sha256":"91bea9a699d18476e39dc9ef37b391460007e65aa30da2a5f8816fc9c8e9141f","schema_version":"1.0","event_id":"sha256:91bea9a699d18476e39dc9ef37b391460007e65aa30da2a5f8816fc9c8e9141f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7T7JG3ETMPTYH5UWYKDHHLRAK6/bundle.json","state_url":"https://pith.science/pith/7T7JG3ETMPTYH5UWYKDHHLRAK6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7T7JG3ETMPTYH5UWYKDHHLRAK6/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-16T14:48:04Z","links":{"resolver":"https://pith.science/pith/7T7JG3ETMPTYH5UWYKDHHLRAK6","bundle":"https://pith.science/pith/7T7JG3ETMPTYH5UWYKDHHLRAK6/bundle.json","state":"https://pith.science/pith/7T7JG3ETMPTYH5UWYKDHHLRAK6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7T7JG3ETMPTYH5UWYKDHHLRAK6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:7T7JG3ETMPTYH5UWYKDHHLRAK6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"057ba36ca9cdb41336da841903172b35c70a5d78a17bba98218a80202514a31a","cross_cats_sorted":["cs.LG","hep-ex"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2022-11-09T13:50:45Z","title_canon_sha256":"dda9420b75be905201dcef4e496d30e0040318a71e6875559faac1551f9b9825"},"schema_version":"1.0","source":{"id":"2211.04890","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.04890","created_at":"2026-07-05T06:21:54Z"},{"alias_kind":"arxiv_version","alias_value":"2211.04890v1","created_at":"2026-07-05T06:21:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.04890","created_at":"2026-07-05T06:21:54Z"},{"alias_kind":"pith_short_12","alias_value":"7T7JG3ETMPTY","created_at":"2026-07-05T06:21:54Z"},{"alias_kind":"pith_short_16","alias_value":"7T7JG3ETMPTYH5UW","created_at":"2026-07-05T06:21:54Z"},{"alias_kind":"pith_short_8","alias_value":"7T7JG3ET","created_at":"2026-07-05T06:21:54Z"}],"graph_snapshots":[{"event_id":"sha256:91bea9a699d18476e39dc9ef37b391460007e65aa30da2a5f8816fc9c8e9141f","target":"graph","created_at":"2026-07-05T06:21:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2211.04890/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this article, we use artificial intelligence algorithms to show how to enhance the resolution of the elementary particle track fitting in inhomogeneous dense detectors, such as plastic scintillators. We use deep learning to replace more traditional Bayesian filtering methods, drastically improving the reconstruction of the interacting particle kinematics. We show that a specific form of neural network, inherited from the field of natural language processing, is very close to the concept of a Bayesian filter that adopts a hyper-informative prior. Such a paradigm change can influence the desi","authors_text":"Andr\\'e Rubbia, Clark McGrew, Davide Sgalaberna, Sa\\'ul Alonso-Monsalve, Xingyu Zhao","cross_cats":["cs.LG","hep-ex"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2022-11-09T13:50:45Z","title":"Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic field"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.04890","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9273e7fb22e1f87d16b551cf14251d8a0e9f00c8a2364e0130d07acebf3563b5","target":"record","created_at":"2026-07-05T06:21:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"057ba36ca9cdb41336da841903172b35c70a5d78a17bba98218a80202514a31a","cross_cats_sorted":["cs.LG","hep-ex"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2022-11-09T13:50:45Z","title_canon_sha256":"dda9420b75be905201dcef4e496d30e0040318a71e6875559faac1551f9b9825"},"schema_version":"1.0","source":{"id":"2211.04890","kind":"arxiv","version":1}},"canonical_sha256":"fcfe936c9363e783f696c28673ae2057bd6a0b7f119d379e15285865269ee9c0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fcfe936c9363e783f696c28673ae2057bd6a0b7f119d379e15285865269ee9c0","first_computed_at":"2026-07-05T06:21:54.931576Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:21:54.931576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ESfaoAG+l+aGVnuFfH8ANcxz6od6ieLD5adZNk6V+tSS8HjbLYKjIRd69mL1ZQIEFvfhmiwWC058X8PEwv21BA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:21:54.932035Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.04890","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9273e7fb22e1f87d16b551cf14251d8a0e9f00c8a2364e0130d07acebf3563b5","sha256:91bea9a699d18476e39dc9ef37b391460007e65aa30da2a5f8816fc9c8e9141f"],"state_sha256":"8e4be46e5b2fac1dc5e48a7306f341d98bdbebd4d85ae1c6155273cf693fb7d4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/XMGn2eGmkESQysj6+NkA8ClvpAXVriIJAj2qTUycwUNFWeeHnutOpm66OqiXAkNivbtiBTfePb0jy3qlrlXCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T14:48:04.453849Z","bundle_sha256":"784043ab6b54236eb41855ef40cc624eeeaedfc627bdf2ed0ac7f6e5d6efe70d"}}