{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:DWLROPF666RAJO3JSGYV325JFH","short_pith_number":"pith:DWLROPF6","schema_version":"1.0","canonical_sha256":"1d97173cbef7a204bb6991b15deba929c51487cb0fcc7148fc340888a1227e1e","source":{"kind":"arxiv","id":"2606.05103","version":1},"attestation_state":"computed","paper":{"title":"Identifying Gems from Roman RAPIDly","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ashish A. Mahabal, Ben Rusholme, Jacob E. Jencson, Karan Gandhi, Lin Yan, Mansi M. Kasliwal, Russ R. Laher, Ryan M. Lau, Schuyler D. Van Dyk","submitted_at":"2026-06-03T17:06:30Z","abstract_excerpt":"The Nancy Grace Roman Space Telescope (Roman), set for launch as early as September 2026, will conduct wide-field infrared imaging surveys with unprecedented spatial resolution and cadence, enabling the discovery of millions of astronomical transients. Hence, it is necessary to have automated pipelines for generating alerts in place so that the telescope can begin discovering reliable transients and variable objects soon after it is launched. However, no real Roman data currently exist, making the development of such pipelines difficult. In this work, we present a machine learning model $RuBR$"},"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":"2606.05103","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-03T17:06:30Z","cross_cats_sorted":["astro-ph.IM","cs.CV","stat.ML"],"title_canon_sha256":"377d0c46fc0166cba00c69a040f53d131b418dd8160119ed6eae33c51d38e227","abstract_canon_sha256":"985d2bb75937522fde759e9578a2f2b96d37d4929b31e62f847bea3658c29e4b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T01:10:06.190186Z","signature_b64":"KdxeLzBCOQUZj2lkf/2bXoJ9VFMB/EzefY0Up7HO+pF+NAICNKIjc89SZKunSZEq15tuXC36Hu5eSFUqaBsKAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d97173cbef7a204bb6991b15deba929c51487cb0fcc7148fc340888a1227e1e","last_reissued_at":"2026-06-04T01:10:06.189599Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T01:10:06.189599Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Identifying Gems from Roman RAPIDly","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ashish A. Mahabal, Ben Rusholme, Jacob E. Jencson, Karan Gandhi, Lin Yan, Mansi M. Kasliwal, Russ R. Laher, Ryan M. Lau, Schuyler D. Van Dyk","submitted_at":"2026-06-03T17:06:30Z","abstract_excerpt":"The Nancy Grace Roman Space Telescope (Roman), set for launch as early as September 2026, will conduct wide-field infrared imaging surveys with unprecedented spatial resolution and cadence, enabling the discovery of millions of astronomical transients. Hence, it is necessary to have automated pipelines for generating alerts in place so that the telescope can begin discovering reliable transients and variable objects soon after it is launched. However, no real Roman data currently exist, making the development of such pipelines difficult. In this work, we present a machine learning model $RuBR$"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.05103","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/2606.05103/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":"2606.05103","created_at":"2026-06-04T01:10:06.189718+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.05103v1","created_at":"2026-06-04T01:10:06.189718+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.05103","created_at":"2026-06-04T01:10:06.189718+00:00"},{"alias_kind":"pith_short_12","alias_value":"DWLROPF666RA","created_at":"2026-06-04T01:10:06.189718+00:00"},{"alias_kind":"pith_short_16","alias_value":"DWLROPF666RAJO3J","created_at":"2026-06-04T01:10:06.189718+00:00"},{"alias_kind":"pith_short_8","alias_value":"DWLROPF6","created_at":"2026-06-04T01:10:06.189718+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/DWLROPF666RAJO3JSGYV325JFH","json":"https://pith.science/pith/DWLROPF666RAJO3JSGYV325JFH.json","graph_json":"https://pith.science/api/pith-number/DWLROPF666RAJO3JSGYV325JFH/graph.json","events_json":"https://pith.science/api/pith-number/DWLROPF666RAJO3JSGYV325JFH/events.json","paper":"https://pith.science/paper/DWLROPF6"},"agent_actions":{"view_html":"https://pith.science/pith/DWLROPF666RAJO3JSGYV325JFH","download_json":"https://pith.science/pith/DWLROPF666RAJO3JSGYV325JFH.json","view_paper":"https://pith.science/paper/DWLROPF6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.05103&json=true","fetch_graph":"https://pith.science/api/pith-number/DWLROPF666RAJO3JSGYV325JFH/graph.json","fetch_events":"https://pith.science/api/pith-number/DWLROPF666RAJO3JSGYV325JFH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DWLROPF666RAJO3JSGYV325JFH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DWLROPF666RAJO3JSGYV325JFH/action/storage_attestation","attest_author":"https://pith.science/pith/DWLROPF666RAJO3JSGYV325JFH/action/author_attestation","sign_citation":"https://pith.science/pith/DWLROPF666RAJO3JSGYV325JFH/action/citation_signature","submit_replication":"https://pith.science/pith/DWLROPF666RAJO3JSGYV325JFH/action/replication_record"}},"created_at":"2026-06-04T01:10:06.189718+00:00","updated_at":"2026-06-04T01:10:06.189718+00:00"}