{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:C5EJD7DVU2MBGNYBOJYRBIYEVJ","short_pith_number":"pith:C5EJD7DV","schema_version":"1.0","canonical_sha256":"174891fc75a698133701727110a304aa6617b5939a3785e18e3b3260e357fd39","source":{"kind":"arxiv","id":"2605.13627","version":1},"attestation_state":"computed","paper":{"title":"SINAPSE: A lightweight deep learning framework for accurate and explainable neutron-$\\gamma$ discrimination","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A lightweight dual-branch neural network denoises low-charge waveforms and classifies neutrons versus gammas with calibrated probabilities.","cross_cats":["physics.data-an"],"primary_cat":"physics.ins-det","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","submitted_at":"2026-05-13T14:53:47Z","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 "},"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":true,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.13627","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.ins-det","submitted_at":"2026-05-13T14:53:47Z","cross_cats_sorted":["physics.data-an"],"title_canon_sha256":"51b78325251b5c79e036753c805087a9e3163e7bd0ccc33e19f44bdb265fe9ac","abstract_canon_sha256":"ce1226a6738c413844610aace7ef0e742f5efc49344e7967c5c46f11f8e741c3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:44:17.789688Z","signature_b64":"tky3HLy752kvJF84gs/MJzs71mNqJ+XnWbPId5vXn8rAtPAABJ8K9dj6kGWqXyxeTB6D1DNnT6csd45hqVmTDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"174891fc75a698133701727110a304aa6617b5939a3785e18e3b3260e357fd39","last_reissued_at":"2026-05-18T02:44:17.789261Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:44:17.789261Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SINAPSE: A lightweight deep learning framework for accurate and explainable neutron-$\\gamma$ discrimination","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A lightweight dual-branch neural network denoises low-charge waveforms and classifies neutrons versus gammas with calibrated probabilities.","cross_cats":["physics.data-an"],"primary_cat":"physics.ins-det","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","submitted_at":"2026-05-13T14:53:47Z","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 "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"SINAPSE achieves superior denoising performance compared to conventional digital signal processing techniques, and outputs well-calibrated probabilities, consistent with traditional graphical cuts.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A lightweight dual-branch neural network denoises low-charge waveforms and classifies neutrons versus gammas with calibrated probabilities.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"084fe8c56749b22611a1a54b8e0a7f21499ad7ac8e0bd1f6a5c5a9b1251db1c3"},"source":{"id":"2605.13627","kind":"arxiv","version":1},"verdict":{"id":"3fe24b94-bd77-4efd-b404-1984840a2d6b","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T17:47:40.108538Z","strongest_claim":"SINAPSE achieves superior denoising performance compared to conventional digital signal processing techniques, and outputs well-calibrated probabilities, consistent with traditional graphical cuts.","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","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.","pith_extraction_headline":"A lightweight dual-branch neural network denoises low-charge waveforms and classifies neutrons versus gammas with calibrated probabilities."},"references":{"count":34,"sample":[{"doi":"","year":null,"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","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2014,"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","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"P. Talou and R. Vogt,Nuclear fission: theories, experi- ments and applications(Springer Nature, 2023)","work_id":"b9ccb4e4-b4f8-4c78-99d9-03c6a71d8575","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1979,"title":"Brooks, Development of organic scintillators, Nuclear Instruments and Methods162, 477 (1979)","work_id":"9de5ede2-7b29-4176-a521-addfc6ed40e8","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"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","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":34,"snapshot_sha256":"3d0e35028af51791f54338478b3ccef8b43afa025484bbe57b5d1ad3743bfbae","internal_anchors":1},"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":"2605.13627","created_at":"2026-05-18T02:44:17.789322+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.13627v1","created_at":"2026-05-18T02:44:17.789322+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13627","created_at":"2026-05-18T02:44:17.789322+00:00"},{"alias_kind":"pith_short_12","alias_value":"C5EJD7DVU2MB","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"C5EJD7DVU2MBGNYB","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"C5EJD7DV","created_at":"2026-05-18T12:33:37.589309+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/C5EJD7DVU2MBGNYBOJYRBIYEVJ","json":"https://pith.science/pith/C5EJD7DVU2MBGNYBOJYRBIYEVJ.json","graph_json":"https://pith.science/api/pith-number/C5EJD7DVU2MBGNYBOJYRBIYEVJ/graph.json","events_json":"https://pith.science/api/pith-number/C5EJD7DVU2MBGNYBOJYRBIYEVJ/events.json","paper":"https://pith.science/paper/C5EJD7DV"},"agent_actions":{"view_html":"https://pith.science/pith/C5EJD7DVU2MBGNYBOJYRBIYEVJ","download_json":"https://pith.science/pith/C5EJD7DVU2MBGNYBOJYRBIYEVJ.json","view_paper":"https://pith.science/paper/C5EJD7DV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.13627&json=true","fetch_graph":"https://pith.science/api/pith-number/C5EJD7DVU2MBGNYBOJYRBIYEVJ/graph.json","fetch_events":"https://pith.science/api/pith-number/C5EJD7DVU2MBGNYBOJYRBIYEVJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C5EJD7DVU2MBGNYBOJYRBIYEVJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C5EJD7DVU2MBGNYBOJYRBIYEVJ/action/storage_attestation","attest_author":"https://pith.science/pith/C5EJD7DVU2MBGNYBOJYRBIYEVJ/action/author_attestation","sign_citation":"https://pith.science/pith/C5EJD7DVU2MBGNYBOJYRBIYEVJ/action/citation_signature","submit_replication":"https://pith.science/pith/C5EJD7DVU2MBGNYBOJYRBIYEVJ/action/replication_record"}},"created_at":"2026-05-18T02:44:17.789322+00:00","updated_at":"2026-05-18T02:44:17.789322+00:00"}