{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PJYKEGXPHVSSWWVQDA46E6OUEK","short_pith_number":"pith:PJYKEGXP","schema_version":"1.0","canonical_sha256":"7a70a21aef3d652b5ab01839e279d422961bdd5eaf182283853ac8f489c2d953","source":{"kind":"arxiv","id":"2406.07538","version":2},"attestation_state":"computed","paper":{"title":"Transforming a rare event search into a not-so-rare event search in real-time with deep learning-based object detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.ins-det"],"primary_cat":"hep-ex","authors_text":"A. C. Kaboth, A. Cottle, A. F. Mills, A. Khazov, A. Lindote, C. Brew, C. Cazzaniga, C. D. Frost, C. McCabe, D. Hunt, D. Loomba, E. Lopez Asamar, E. Oliveri, E. Tilly, F. Garcia, F. M. Brunbauer, F. Neves, H. Kraus, H. M. Ara\\'ujo, I. Katsioulas, J. E. Borg, J. Schueler, J. Tarrant, K. Nikolopoulos, L. Millins, L. Ropelewski, M. I. Lopes, M. Kastriotou, M. Lisowska, M. Nakhostin, P. A. Majewski, P. Knights, P. Luna Dapica, R. Nandakumar, R. Turnley, R. Veenhof, S. Lilley, S. N. Balashov, T. J. Sumner, T. Marley, T. Neep, V. A. Kudryavtsev, V. N. Solovov","submitted_at":"2024-06-11T17:58:53Z","abstract_excerpt":"Deep learning-based object detection algorithms enable the simultaneous classification and localization of any number of objects in image data. Many of these algorithms are capable of operating in real-time on high resolution images, attributing to their widespread usage across many fields. We present an end-to-end object detection pipeline designed for real-time rare event searches for the Migdal effect, using high-resolution image data from a state-of-the-art scientific CMOS camera in the MIGDAL experiment. The Migdal effect in nuclear scattering, crucial for sub-GeV dark matter searches, ha"},"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":"2406.07538","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ex","submitted_at":"2024-06-11T17:58:53Z","cross_cats_sorted":["physics.ins-det"],"title_canon_sha256":"e5e630a6d3d45a062e2a13fe417a47a593b71dce8059001cec3faf698a4820b1","abstract_canon_sha256":"ae9bf34bd962033502424c79c2ff5990cd9b60bcdd4721882f421237e99114d0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:46:54.882859Z","signature_b64":"V3nYYoBjkGIJhPA8cDDjlPw2Kxgrb5R3eTf3TFqU5l02esuqQgli/SLshjBKHh7N0RvpsWWdCzJVya1CudXvCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a70a21aef3d652b5ab01839e279d422961bdd5eaf182283853ac8f489c2d953","last_reissued_at":"2026-07-05T10:46:54.882367Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:46:54.882367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transforming a rare event search into a not-so-rare event search in real-time with deep learning-based object detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.ins-det"],"primary_cat":"hep-ex","authors_text":"A. C. Kaboth, A. Cottle, A. F. Mills, A. Khazov, A. Lindote, C. Brew, C. Cazzaniga, C. D. Frost, C. McCabe, D. Hunt, D. Loomba, E. Lopez Asamar, E. Oliveri, E. Tilly, F. Garcia, F. M. Brunbauer, F. Neves, H. Kraus, H. M. Ara\\'ujo, I. Katsioulas, J. E. Borg, J. Schueler, J. Tarrant, K. Nikolopoulos, L. Millins, L. Ropelewski, M. I. Lopes, M. Kastriotou, M. Lisowska, M. Nakhostin, P. A. Majewski, P. Knights, P. Luna Dapica, R. Nandakumar, R. Turnley, R. Veenhof, S. Lilley, S. N. Balashov, T. J. Sumner, T. Marley, T. Neep, V. A. Kudryavtsev, V. N. Solovov","submitted_at":"2024-06-11T17:58:53Z","abstract_excerpt":"Deep learning-based object detection algorithms enable the simultaneous classification and localization of any number of objects in image data. Many of these algorithms are capable of operating in real-time on high resolution images, attributing to their widespread usage across many fields. We present an end-to-end object detection pipeline designed for real-time rare event searches for the Migdal effect, using high-resolution image data from a state-of-the-art scientific CMOS camera in the MIGDAL experiment. The Migdal effect in nuclear scattering, crucial for sub-GeV dark matter searches, ha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.07538","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/2406.07538/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":"2406.07538","created_at":"2026-07-05T10:46:54.882426+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.07538v2","created_at":"2026-07-05T10:46:54.882426+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.07538","created_at":"2026-07-05T10:46:54.882426+00:00"},{"alias_kind":"pith_short_12","alias_value":"PJYKEGXPHVSS","created_at":"2026-07-05T10:46:54.882426+00:00"},{"alias_kind":"pith_short_16","alias_value":"PJYKEGXPHVSSWWVQ","created_at":"2026-07-05T10:46:54.882426+00:00"},{"alias_kind":"pith_short_8","alias_value":"PJYKEGXP","created_at":"2026-07-05T10:46:54.882426+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/PJYKEGXPHVSSWWVQDA46E6OUEK","json":"https://pith.science/pith/PJYKEGXPHVSSWWVQDA46E6OUEK.json","graph_json":"https://pith.science/api/pith-number/PJYKEGXPHVSSWWVQDA46E6OUEK/graph.json","events_json":"https://pith.science/api/pith-number/PJYKEGXPHVSSWWVQDA46E6OUEK/events.json","paper":"https://pith.science/paper/PJYKEGXP"},"agent_actions":{"view_html":"https://pith.science/pith/PJYKEGXPHVSSWWVQDA46E6OUEK","download_json":"https://pith.science/pith/PJYKEGXPHVSSWWVQDA46E6OUEK.json","view_paper":"https://pith.science/paper/PJYKEGXP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.07538&json=true","fetch_graph":"https://pith.science/api/pith-number/PJYKEGXPHVSSWWVQDA46E6OUEK/graph.json","fetch_events":"https://pith.science/api/pith-number/PJYKEGXPHVSSWWVQDA46E6OUEK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PJYKEGXPHVSSWWVQDA46E6OUEK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PJYKEGXPHVSSWWVQDA46E6OUEK/action/storage_attestation","attest_author":"https://pith.science/pith/PJYKEGXPHVSSWWVQDA46E6OUEK/action/author_attestation","sign_citation":"https://pith.science/pith/PJYKEGXPHVSSWWVQDA46E6OUEK/action/citation_signature","submit_replication":"https://pith.science/pith/PJYKEGXPHVSSWWVQDA46E6OUEK/action/replication_record"}},"created_at":"2026-07-05T10:46:54.882426+00:00","updated_at":"2026-07-05T10:46:54.882426+00:00"}