{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:C5EJD7DVU2MBGNYBOJYRBIYEVJ","merge_version":"pith-open-graph-merge-v1","event_count":3,"valid_event_count":3,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ce1226a6738c413844610aace7ef0e742f5efc49344e7967c5c46f11f8e741c3","cross_cats_sorted":["physics.data-an"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.ins-det","submitted_at":"2026-05-13T14:53:47Z","title_canon_sha256":"51b78325251b5c79e036753c805087a9e3163e7bd0ccc33e19f44bdb265fe9ac"},"schema_version":"1.0","source":{"id":"2605.13627","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.13627","created_at":"2026-05-18T02:44:17Z"},{"alias_kind":"arxiv_version","alias_value":"2605.13627v1","created_at":"2026-05-18T02:44:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13627","created_at":"2026-05-18T02:44:17Z"},{"alias_kind":"pith_short_12","alias_value":"C5EJD7DVU2MB","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"C5EJD7DVU2MBGNYB","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"C5EJD7DV","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:e2a4fb5b32fab1932e659d00092777dc8ee1d2e48dc3055995838bfc2f949d5f","target":"graph","created_at":"2026-05-18T02:44:17Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"SINAPSE achieves superior denoising performance compared to conventional digital signal processing techniques, and outputs well-calibrated probabilities, consistent with traditional graphical cuts."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That random augmentations applied to high-SNR waveforms faithfully reproduce the noise statistics and pulse-shape distortions present in real low-charge experimental data."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"SINAPSE uses a dual-branch neural network with a 1D convolutional autoencoder for denoising and a classifier for neutron-gamma discrimination, trained via random augmentations on high-SNR data and validated with SHAP explanations."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A lightweight dual-branch neural network denoises low-charge waveforms and classifies neutrons versus gammas with calibrated probabilities."}],"snapshot_sha256":"084fe8c56749b22611a1a54b8e0a7f21499ad7ac8e0bd1f6a5c5a9b1251db1c3"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Traditionally, neutron-$\\gamma$ discrimination in organic scintillators relies on techniques such as time-of-flight (ToF) selection and pulse-shape discrimination (PSD). However, particle identification through graphical cuts remains challenging in the low-charge regime due to poor signal-to-noise ratios (SNR). In this work, we propose SINAPSE, a lightweight deep learning framework for accurate and explainable neutron-$\\gamma$ discrimination in the low-charge regime. The framework employs a dual-branch architecture that combines a 1-dimensional convolutional autoencoder for waveform denoising ","authors_text":"Adrien Matta, Audrey Chatillon, Beno\\^it Mauss, Charl\\`ene Surault, Cyril Lenain, David Etasse, David Regnier, Jason Surbrook, Julien Taieb, Matthew Devlin, Owen Syrett, Patrick Copp, Pierre Morfouace, Thomas Carreau","cross_cats":["physics.data-an"],"headline":"A lightweight dual-branch neural network denoises low-charge waveforms and classifies neutrons versus gammas with calibrated probabilities.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.ins-det","submitted_at":"2026-05-13T14:53:47Z","title":"SINAPSE: A lightweight deep learning framework for accurate and explainable neutron-$\\gamma$ discrimination"},"references":{"count":34,"internal_anchors":1,"resolved_work":34,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"R. C. Haight, H. Y. Lee, T. N. Taddeucci, J. M. O’Donnell, B. A. Perdue, N. Fotiades, M. Devlin, J. L. Ullmann, A. Laptev, T. Bredeweg,et al., Two detector arrays for fast neutrons at lansce, Journal ","work_id":"00c9259b-ffe7-4b3b-a126-2eb552bded29","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"T. Mart´ ınez, D. Cano-Ott, J. Castilla, A. Garcia, J. Marin, G. Martinez, E. Mendoza, C. Santos, F. Tera, D. Villamarin,et al., Monster: a tof spectrometer forβ- delayed neutron spectroscopy, Nuclear","work_id":"1bc42e63-a69c-476f-8211-8cad910f9136","year":2014},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"P. Talou and R. Vogt,Nuclear fission: theories, experi- ments and applications(Springer Nature, 2023)","work_id":"b9ccb4e4-b4f8-4c78-99d9-03c6a71d8575","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Brooks, Development of organic scintillators, Nuclear Instruments and Methods162, 477 (1979)","work_id":"9de5ede2-7b29-4176-a521-addfc6ed40e8","year":1979},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"X. Fabian, G. Baulieu, L. Ducroux, O. St´ ezowski, A. Boujrad, E. Cl´ ement, S. Coudert, G. de France, N. Er- duran, S. Ert¨ urk,et al., Artificial neural networks for neutron/γdiscrimination in the n","work_id":"7accc238-bca1-4b4c-921c-5e35fac0ddd4","year":2021}],"snapshot_sha256":"3d0e35028af51791f54338478b3ccef8b43afa025484bbe57b5d1ad3743bfbae"},"source":{"id":"2605.13627","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T17:47:40.108538Z","id":"3fe24b94-bd77-4efd-b404-1984840a2d6b","model_set":{"reader":"grok-4.3"},"one_line_summary":"SINAPSE uses a dual-branch neural network with a 1D convolutional autoencoder for denoising and a classifier for neutron-gamma discrimination, trained via random augmentations on high-SNR data and validated with SHAP explanations.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A lightweight dual-branch neural network denoises low-charge waveforms and classifies neutrons versus gammas with calibrated probabilities.","strongest_claim":"SINAPSE achieves superior denoising performance compared to conventional digital signal processing techniques, and outputs well-calibrated probabilities, consistent with traditional graphical cuts.","weakest_assumption":"That random augmentations applied to high-SNR waveforms faithfully reproduce the noise statistics and pulse-shape distortions present in real low-charge experimental data."}},"verdict_id":"3fe24b94-bd77-4efd-b404-1984840a2d6b"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2d73a7086bbf6f92225e90cb9f4b86931da30d519090137560a92f5836dd8970","target":"record","created_at":"2026-05-18T02:44:17Z","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":"ce1226a6738c413844610aace7ef0e742f5efc49344e7967c5c46f11f8e741c3","cross_cats_sorted":["physics.data-an"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.ins-det","submitted_at":"2026-05-13T14:53:47Z","title_canon_sha256":"51b78325251b5c79e036753c805087a9e3163e7bd0ccc33e19f44bdb265fe9ac"},"schema_version":"1.0","source":{"id":"2605.13627","kind":"arxiv","version":1}},"canonical_sha256":"174891fc75a698133701727110a304aa6617b5939a3785e18e3b3260e357fd39","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"174891fc75a698133701727110a304aa6617b5939a3785e18e3b3260e357fd39","first_computed_at":"2026-05-18T02:44:17.789261Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:44:17.789261Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tky3HLy752kvJF84gs/MJzs71mNqJ+XnWbPId5vXn8rAtPAABJ8K9dj6kGWqXyxeTB6D1DNnT6csd45hqVmTDg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:44:17.789688Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.13627","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2d73a7086bbf6f92225e90cb9f4b86931da30d519090137560a92f5836dd8970","sha256:e2a4fb5b32fab1932e659d00092777dc8ee1d2e48dc3055995838bfc2f949d5f","sha256:9a872247878d6c41a05f2772906b0cfd084e680c1e1652d4df5bc67331e9bcae"],"state_sha256":"62439f446c67e27533c0c81c685a2c098f95c963e33f4415f90281d6eb24183a"}