{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:ROUR2GXG54JFXKI6WAIWWKP2VN","short_pith_number":"pith:ROUR2GXG","schema_version":"1.0","canonical_sha256":"8ba91d1ae6ef125ba91eb0116b29faab4fb8899db8126f686d9dc31a379131b3","source":{"kind":"arxiv","id":"2212.12915","version":2},"attestation_state":"computed","paper":{"title":"Transformers as Strong Lens Detectors- From Simulation to Surveys","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.GA","authors_text":"Adam Zadro\\.zny, Hareesh Thuruthipilly, Margherita Grespan","submitted_at":"2022-12-25T14:43:02Z","abstract_excerpt":"With the upcoming large-scale surveys like LSST, we expect to find approximately $10^5$ strong gravitational lenses among data of many orders of magnitude larger. In this scenario, the usage of non-automated techniques is too time-consuming and hence impractical for science. For this reason, machine learning techniques started becoming an alternative to previous methods. In our previous work, we proposed a new machine learning architecture based on the principle of self-attention, trained to find strong gravitational lenses on simulated data from the Bologna Lens Challenge. Self-attention-base"},"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":"2212.12915","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.GA","submitted_at":"2022-12-25T14:43:02Z","cross_cats_sorted":["astro-ph.IM"],"title_canon_sha256":"71d4cd9061eae0129d4d649e8c26305a4b123d2ea47fb15b7f881891da7eac18","abstract_canon_sha256":"554e0720e174926fc7a1f210893f8734d3b14e8a43ede36d435a6fbbab6414ff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:57:39.016779Z","signature_b64":"owkiw40g7XFbceuWIQwrxCTzHEnIi1NG4opanzRbS2MfG0n7VEK7B4R1PdTfx882Eg0o7wUCzGBhNwaGfce7AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ba91d1ae6ef125ba91eb0116b29faab4fb8899db8126f686d9dc31a379131b3","last_reissued_at":"2026-07-05T07:57:39.016310Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:57:39.016310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transformers as Strong Lens Detectors- From Simulation to Surveys","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.GA","authors_text":"Adam Zadro\\.zny, Hareesh Thuruthipilly, Margherita Grespan","submitted_at":"2022-12-25T14:43:02Z","abstract_excerpt":"With the upcoming large-scale surveys like LSST, we expect to find approximately $10^5$ strong gravitational lenses among data of many orders of magnitude larger. In this scenario, the usage of non-automated techniques is too time-consuming and hence impractical for science. For this reason, machine learning techniques started becoming an alternative to previous methods. In our previous work, we proposed a new machine learning architecture based on the principle of self-attention, trained to find strong gravitational lenses on simulated data from the Bologna Lens Challenge. Self-attention-base"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.12915","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/2212.12915/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":"2212.12915","created_at":"2026-07-05T07:57:39.016373+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.12915v2","created_at":"2026-07-05T07:57:39.016373+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.12915","created_at":"2026-07-05T07:57:39.016373+00:00"},{"alias_kind":"pith_short_12","alias_value":"ROUR2GXG54JF","created_at":"2026-07-05T07:57:39.016373+00:00"},{"alias_kind":"pith_short_16","alias_value":"ROUR2GXG54JFXKI6","created_at":"2026-07-05T07:57:39.016373+00:00"},{"alias_kind":"pith_short_8","alias_value":"ROUR2GXG","created_at":"2026-07-05T07:57:39.016373+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/ROUR2GXG54JFXKI6WAIWWKP2VN","json":"https://pith.science/pith/ROUR2GXG54JFXKI6WAIWWKP2VN.json","graph_json":"https://pith.science/api/pith-number/ROUR2GXG54JFXKI6WAIWWKP2VN/graph.json","events_json":"https://pith.science/api/pith-number/ROUR2GXG54JFXKI6WAIWWKP2VN/events.json","paper":"https://pith.science/paper/ROUR2GXG"},"agent_actions":{"view_html":"https://pith.science/pith/ROUR2GXG54JFXKI6WAIWWKP2VN","download_json":"https://pith.science/pith/ROUR2GXG54JFXKI6WAIWWKP2VN.json","view_paper":"https://pith.science/paper/ROUR2GXG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.12915&json=true","fetch_graph":"https://pith.science/api/pith-number/ROUR2GXG54JFXKI6WAIWWKP2VN/graph.json","fetch_events":"https://pith.science/api/pith-number/ROUR2GXG54JFXKI6WAIWWKP2VN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ROUR2GXG54JFXKI6WAIWWKP2VN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ROUR2GXG54JFXKI6WAIWWKP2VN/action/storage_attestation","attest_author":"https://pith.science/pith/ROUR2GXG54JFXKI6WAIWWKP2VN/action/author_attestation","sign_citation":"https://pith.science/pith/ROUR2GXG54JFXKI6WAIWWKP2VN/action/citation_signature","submit_replication":"https://pith.science/pith/ROUR2GXG54JFXKI6WAIWWKP2VN/action/replication_record"}},"created_at":"2026-07-05T07:57:39.016373+00:00","updated_at":"2026-07-05T07:57:39.016373+00:00"}