{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:WAKO5NUF7JJJ37QCV4QCTQPMGD","short_pith_number":"pith:WAKO5NUF","schema_version":"1.0","canonical_sha256":"b014eeb685fa529dfe02af2029c1ec30fb209c2a94da16695a6566eb9bc5151b","source":{"kind":"arxiv","id":"2305.08674","version":2},"attestation_state":"computed","paper":{"title":"Application of Graph Networks to background rejection in Imaging Air Cherenkov Telescopes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Benedetta Bruno, Jonas Glombitza, Stefan Funk, Vikas Joshi","submitted_at":"2023-05-15T14:29:15Z","abstract_excerpt":"Imaging Air Cherenkov Telescopes (IACTs) are essential to ground-based observations of gamma rays in the GeV to TeV regime. One particular challenge of ground-based gamma-ray astronomy is an effective rejection of the hadronic background. We propose a new deep-learning-based algorithm for classifying images measured using single or multiple Imaging Air Cherenkov Telescopes. We interpret the detected images as a collection of triggered sensors that can be represented by graphs and analyzed by graph convolutional networks. For images cleaned of the light from the night sky, this allows for an ef"},"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":"2305.08674","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2023-05-15T14:29:15Z","cross_cats_sorted":[],"title_canon_sha256":"b15ad90875765288da82fe926c1087a7b279c2fdbb4e56c5088d8051d5eaaf7e","abstract_canon_sha256":"69547ecbe79a3f8f6413e75228e26e8a1236cb257ce2a629939512f301d3d20b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:09:48.043546Z","signature_b64":"JPRU/lZt/Oqp4yOvy//vJ3kbuqzVrH490qv4vyPqnEcgXhwWoVTLDnbBFhywfRpnwx1JJBjrh7LmCcMvIXvfAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b014eeb685fa529dfe02af2029c1ec30fb209c2a94da16695a6566eb9bc5151b","last_reissued_at":"2026-07-05T07:09:48.043031Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:09:48.043031Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Application of Graph Networks to background rejection in Imaging Air Cherenkov Telescopes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Benedetta Bruno, Jonas Glombitza, Stefan Funk, Vikas Joshi","submitted_at":"2023-05-15T14:29:15Z","abstract_excerpt":"Imaging Air Cherenkov Telescopes (IACTs) are essential to ground-based observations of gamma rays in the GeV to TeV regime. One particular challenge of ground-based gamma-ray astronomy is an effective rejection of the hadronic background. We propose a new deep-learning-based algorithm for classifying images measured using single or multiple Imaging Air Cherenkov Telescopes. We interpret the detected images as a collection of triggered sensors that can be represented by graphs and analyzed by graph convolutional networks. For images cleaned of the light from the night sky, this allows for an ef"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.08674","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/2305.08674/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":"2305.08674","created_at":"2026-07-05T07:09:48.043091+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.08674v2","created_at":"2026-07-05T07:09:48.043091+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.08674","created_at":"2026-07-05T07:09:48.043091+00:00"},{"alias_kind":"pith_short_12","alias_value":"WAKO5NUF7JJJ","created_at":"2026-07-05T07:09:48.043091+00:00"},{"alias_kind":"pith_short_16","alias_value":"WAKO5NUF7JJJ37QC","created_at":"2026-07-05T07:09:48.043091+00:00"},{"alias_kind":"pith_short_8","alias_value":"WAKO5NUF","created_at":"2026-07-05T07:09:48.043091+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.09402","citing_title":"Enhancing event reconstruction for $\\gamma$-ray particle detector arrays using transformers","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WAKO5NUF7JJJ37QCV4QCTQPMGD","json":"https://pith.science/pith/WAKO5NUF7JJJ37QCV4QCTQPMGD.json","graph_json":"https://pith.science/api/pith-number/WAKO5NUF7JJJ37QCV4QCTQPMGD/graph.json","events_json":"https://pith.science/api/pith-number/WAKO5NUF7JJJ37QCV4QCTQPMGD/events.json","paper":"https://pith.science/paper/WAKO5NUF"},"agent_actions":{"view_html":"https://pith.science/pith/WAKO5NUF7JJJ37QCV4QCTQPMGD","download_json":"https://pith.science/pith/WAKO5NUF7JJJ37QCV4QCTQPMGD.json","view_paper":"https://pith.science/paper/WAKO5NUF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.08674&json=true","fetch_graph":"https://pith.science/api/pith-number/WAKO5NUF7JJJ37QCV4QCTQPMGD/graph.json","fetch_events":"https://pith.science/api/pith-number/WAKO5NUF7JJJ37QCV4QCTQPMGD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WAKO5NUF7JJJ37QCV4QCTQPMGD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WAKO5NUF7JJJ37QCV4QCTQPMGD/action/storage_attestation","attest_author":"https://pith.science/pith/WAKO5NUF7JJJ37QCV4QCTQPMGD/action/author_attestation","sign_citation":"https://pith.science/pith/WAKO5NUF7JJJ37QCV4QCTQPMGD/action/citation_signature","submit_replication":"https://pith.science/pith/WAKO5NUF7JJJ37QCV4QCTQPMGD/action/replication_record"}},"created_at":"2026-07-05T07:09:48.043091+00:00","updated_at":"2026-07-05T07:09:48.043091+00:00"}