{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XU5WV2RZKW72M63PG3SVPNJGSP","short_pith_number":"pith:XU5WV2RZ","schema_version":"1.0","canonical_sha256":"bd3b6aea3955bfa67b6f36e557b52693ccfcc19bd75a451f43eeb5a899c81df4","source":{"kind":"arxiv","id":"2410.02936","version":1},"attestation_state":"computed","paper":{"title":"Galaxy-Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting 2-Dimensional Information from Multi-Band Images in Ground and Space Observations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.GA","authors_text":"Crescenzo Tortora, Fucheng Zhong, Giuseppe Longo, L. V. E. Koopmans, Nicola R. Napolitano, Ruibiao Luo, Rui Li, Valerio Busillo, Xincheng Zhu","submitted_at":"2024-10-03T19:29:52Z","abstract_excerpt":"We present a novel deep learning method to separately extract the two-dimensional flux information of the foreground galaxy (deflector) and background system (source) of Galaxy-Galaxy Strong Lensing events using U-Net (GGSL-Unet for short). In particular, the segmentation of the source image is found to enhance the performance of the lens modeling, especially for ground-based images. By combining mock lens foreground+background components with real sky survey noise to train the GGSL-Unet, we show it can correctly model the input image noise and extract the lens signal. However, the most import"},"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":"2410.02936","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.GA","submitted_at":"2024-10-03T19:29:52Z","cross_cats_sorted":[],"title_canon_sha256":"31d4a39941f2492e5288c7a29f122d5aab21ba7fde9c8a6d9c038c1291c2a3e0","abstract_canon_sha256":"041d56ec1098c6133d25cd64fd1000cb5784e202ad38dd77f6cf1603f87a6060"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:15:41.739672Z","signature_b64":"ejxZ0P9as+tAr2vWOOYwxEAWWSqaNJXpj6WLwJwCytKkBa8HU6s3gTukZFyEk7dtWpgHaOljhGjhQRCaLSFSCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bd3b6aea3955bfa67b6f36e557b52693ccfcc19bd75a451f43eeb5a899c81df4","last_reissued_at":"2026-07-05T09:15:41.739228Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:15:41.739228Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Galaxy-Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting 2-Dimensional Information from Multi-Band Images in Ground and Space Observations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.GA","authors_text":"Crescenzo Tortora, Fucheng Zhong, Giuseppe Longo, L. V. E. Koopmans, Nicola R. Napolitano, Ruibiao Luo, Rui Li, Valerio Busillo, Xincheng Zhu","submitted_at":"2024-10-03T19:29:52Z","abstract_excerpt":"We present a novel deep learning method to separately extract the two-dimensional flux information of the foreground galaxy (deflector) and background system (source) of Galaxy-Galaxy Strong Lensing events using U-Net (GGSL-Unet for short). In particular, the segmentation of the source image is found to enhance the performance of the lens modeling, especially for ground-based images. By combining mock lens foreground+background components with real sky survey noise to train the GGSL-Unet, we show it can correctly model the input image noise and extract the lens signal. However, the most import"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.02936","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/2410.02936/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":"2410.02936","created_at":"2026-07-05T09:15:41.739302+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.02936v1","created_at":"2026-07-05T09:15:41.739302+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.02936","created_at":"2026-07-05T09:15:41.739302+00:00"},{"alias_kind":"pith_short_12","alias_value":"XU5WV2RZKW72","created_at":"2026-07-05T09:15:41.739302+00:00"},{"alias_kind":"pith_short_16","alias_value":"XU5WV2RZKW72M63P","created_at":"2026-07-05T09:15:41.739302+00:00"},{"alias_kind":"pith_short_8","alias_value":"XU5WV2RZ","created_at":"2026-07-05T09:15:41.739302+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.06959","citing_title":"KAN-LSTM-Transformer Neural Networks, MFV and Cosmological Parameters","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XU5WV2RZKW72M63PG3SVPNJGSP","json":"https://pith.science/pith/XU5WV2RZKW72M63PG3SVPNJGSP.json","graph_json":"https://pith.science/api/pith-number/XU5WV2RZKW72M63PG3SVPNJGSP/graph.json","events_json":"https://pith.science/api/pith-number/XU5WV2RZKW72M63PG3SVPNJGSP/events.json","paper":"https://pith.science/paper/XU5WV2RZ"},"agent_actions":{"view_html":"https://pith.science/pith/XU5WV2RZKW72M63PG3SVPNJGSP","download_json":"https://pith.science/pith/XU5WV2RZKW72M63PG3SVPNJGSP.json","view_paper":"https://pith.science/paper/XU5WV2RZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.02936&json=true","fetch_graph":"https://pith.science/api/pith-number/XU5WV2RZKW72M63PG3SVPNJGSP/graph.json","fetch_events":"https://pith.science/api/pith-number/XU5WV2RZKW72M63PG3SVPNJGSP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XU5WV2RZKW72M63PG3SVPNJGSP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XU5WV2RZKW72M63PG3SVPNJGSP/action/storage_attestation","attest_author":"https://pith.science/pith/XU5WV2RZKW72M63PG3SVPNJGSP/action/author_attestation","sign_citation":"https://pith.science/pith/XU5WV2RZKW72M63PG3SVPNJGSP/action/citation_signature","submit_replication":"https://pith.science/pith/XU5WV2RZKW72M63PG3SVPNJGSP/action/replication_record"}},"created_at":"2026-07-05T09:15:41.739302+00:00","updated_at":"2026-07-05T09:15:41.739302+00:00"}