{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:IXS5BFQQZGFUT2BF3OVXWHPYEH","short_pith_number":"pith:IXS5BFQQ","schema_version":"1.0","canonical_sha256":"45e5d09610c98b49e825dbab7b1df821e49c97653dafdaef01c4286cbc1b9e36","source":{"kind":"arxiv","id":"2606.27021","version":1},"attestation_state":"computed","paper":{"title":"SMR: Scheduler with Multi-Channel Map-Encoded Reinforcement Learning for Radio Telescopes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Chuhao Gao, Na Wang, Zhenyang Huang, Zhiyong Liu","submitted_at":"2026-06-25T13:32:47Z","abstract_excerpt":"Observation scheduling for large single-dish radio telescopes is a multi-objective optimization problem: schedulers must maximize on-source scientific return under strict mechanical and environmental constraints. Previous dynamic scheduling relies on expert-designed heuristics, while existing reinforcement-learning (RL) approaches often struggle with variable-length target lists and lack an intrinsic representation of sky geometry. We present SMR (Scheduler with Map-encoded Reinforcement Learning), which projects discrete targets onto an azimuth--elevation (Az--El) grid in the local horizon fr"},"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":"2606.27021","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2026-06-25T13:32:47Z","cross_cats_sorted":[],"title_canon_sha256":"2990d7bfcca6b6b8482c99b6ac0aee8a193f4bde28e26c7c6a64146ee299fe8d","abstract_canon_sha256":"105e39eba299fcc6099698c471778f5c90ac04e66dab7cdb18c2a35035fa48e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-26T01:16:07.210403Z","signature_b64":"0O32ZRybKGO1RtcnNh1JUCIG4YDkwJYrbpG6lniaLJJ/Tpj+fXAWivxWy8t2lh6I55EFCutw620PFqUOCMDeBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45e5d09610c98b49e825dbab7b1df821e49c97653dafdaef01c4286cbc1b9e36","last_reissued_at":"2026-06-26T01:16:07.209989Z","signature_status":"signed_v1","first_computed_at":"2026-06-26T01:16:07.209989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SMR: Scheduler with Multi-Channel Map-Encoded Reinforcement Learning for Radio Telescopes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Chuhao Gao, Na Wang, Zhenyang Huang, Zhiyong Liu","submitted_at":"2026-06-25T13:32:47Z","abstract_excerpt":"Observation scheduling for large single-dish radio telescopes is a multi-objective optimization problem: schedulers must maximize on-source scientific return under strict mechanical and environmental constraints. Previous dynamic scheduling relies on expert-designed heuristics, while existing reinforcement-learning (RL) approaches often struggle with variable-length target lists and lack an intrinsic representation of sky geometry. We present SMR (Scheduler with Map-encoded Reinforcement Learning), which projects discrete targets onto an azimuth--elevation (Az--El) grid in the local horizon fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.27021","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/2606.27021/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":"2606.27021","created_at":"2026-06-26T01:16:07.210045+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.27021v1","created_at":"2026-06-26T01:16:07.210045+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.27021","created_at":"2026-06-26T01:16:07.210045+00:00"},{"alias_kind":"pith_short_12","alias_value":"IXS5BFQQZGFU","created_at":"2026-06-26T01:16:07.210045+00:00"},{"alias_kind":"pith_short_16","alias_value":"IXS5BFQQZGFUT2BF","created_at":"2026-06-26T01:16:07.210045+00:00"},{"alias_kind":"pith_short_8","alias_value":"IXS5BFQQ","created_at":"2026-06-26T01:16:07.210045+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/IXS5BFQQZGFUT2BF3OVXWHPYEH","json":"https://pith.science/pith/IXS5BFQQZGFUT2BF3OVXWHPYEH.json","graph_json":"https://pith.science/api/pith-number/IXS5BFQQZGFUT2BF3OVXWHPYEH/graph.json","events_json":"https://pith.science/api/pith-number/IXS5BFQQZGFUT2BF3OVXWHPYEH/events.json","paper":"https://pith.science/paper/IXS5BFQQ"},"agent_actions":{"view_html":"https://pith.science/pith/IXS5BFQQZGFUT2BF3OVXWHPYEH","download_json":"https://pith.science/pith/IXS5BFQQZGFUT2BF3OVXWHPYEH.json","view_paper":"https://pith.science/paper/IXS5BFQQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.27021&json=true","fetch_graph":"https://pith.science/api/pith-number/IXS5BFQQZGFUT2BF3OVXWHPYEH/graph.json","fetch_events":"https://pith.science/api/pith-number/IXS5BFQQZGFUT2BF3OVXWHPYEH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IXS5BFQQZGFUT2BF3OVXWHPYEH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IXS5BFQQZGFUT2BF3OVXWHPYEH/action/storage_attestation","attest_author":"https://pith.science/pith/IXS5BFQQZGFUT2BF3OVXWHPYEH/action/author_attestation","sign_citation":"https://pith.science/pith/IXS5BFQQZGFUT2BF3OVXWHPYEH/action/citation_signature","submit_replication":"https://pith.science/pith/IXS5BFQQZGFUT2BF3OVXWHPYEH/action/replication_record"}},"created_at":"2026-06-26T01:16:07.210045+00:00","updated_at":"2026-06-26T01:16:07.210045+00:00"}