{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:SNVX7ZGCNRR7YJND3S5LEDNNGS","short_pith_number":"pith:SNVX7ZGC","canonical_record":{"source":{"id":"2508.03400","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2025-08-05T12:48:44Z","cross_cats_sorted":[],"title_canon_sha256":"82106faa1675544e45da566fe35992727a0f003e0df8ca052cd4fd57e89de52b","abstract_canon_sha256":"0e8d9bd5e4f3fe76121c2102fca71318e3193553269a2145e9f8c72ab4eb5410"},"schema_version":"1.0"},"canonical_sha256":"936b7fe4c26c63fc25a3dcbab20dad349a23610b91041203cba4c435097757f6","source":{"kind":"arxiv","id":"2508.03400","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.03400","created_at":"2026-07-05T11:48:55Z"},{"alias_kind":"arxiv_version","alias_value":"2508.03400v1","created_at":"2026-07-05T11:48:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.03400","created_at":"2026-07-05T11:48:55Z"},{"alias_kind":"pith_short_12","alias_value":"SNVX7ZGCNRR7","created_at":"2026-07-05T11:48:55Z"},{"alias_kind":"pith_short_16","alias_value":"SNVX7ZGCNRR7YJND","created_at":"2026-07-05T11:48:55Z"},{"alias_kind":"pith_short_8","alias_value":"SNVX7ZGC","created_at":"2026-07-05T11:48:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:SNVX7ZGCNRR7YJND3S5LEDNNGS","target":"record","payload":{"canonical_record":{"source":{"id":"2508.03400","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2025-08-05T12:48:44Z","cross_cats_sorted":[],"title_canon_sha256":"82106faa1675544e45da566fe35992727a0f003e0df8ca052cd4fd57e89de52b","abstract_canon_sha256":"0e8d9bd5e4f3fe76121c2102fca71318e3193553269a2145e9f8c72ab4eb5410"},"schema_version":"1.0"},"canonical_sha256":"936b7fe4c26c63fc25a3dcbab20dad349a23610b91041203cba4c435097757f6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:48:55.861129Z","signature_b64":"RavJmneMCNHM9S90QQ4DYOwW09+qw6yu4xbOoOqdqrTwr8LRwkVaiSn+FXCUxhY8MyTP8GObcjampx8IJjeqAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"936b7fe4c26c63fc25a3dcbab20dad349a23610b91041203cba4c435097757f6","last_reissued_at":"2026-07-05T11:48:55.860523Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:48:55.860523Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.03400","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:48:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7luhatj/GTfSF9iXI6h+7iEwvCyslV3+SKVFp7q6glkZNgE5I0zbJlvUXiPG5gf39nW4C7e1qUpXUbKfPlKXCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T02:36:48.091233Z"},"content_sha256":"1c208d337cf14ce7a57c69a349b0ba841ec2bacf14284eedca3bd54d1385faf1","schema_version":"1.0","event_id":"sha256:1c208d337cf14ce7a57c69a349b0ba841ec2bacf14284eedca3bd54d1385faf1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:SNVX7ZGCNRR7YJND3S5LEDNNGS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DeepAP: Deep Learning-based Aperture Photometry Feasibility Assessment and Aperture Size Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Bing-Feng Seng, Dong Li, Fa-bo Feng, Qing-Quan Li, Yi-Cheng Rui, Yi-Fan Xuan, Yu-Li Liu, Yu-Ting Wu, Zheng-Jun Du","submitted_at":"2025-08-05T12:48:44Z","abstract_excerpt":"Aperture photometry is a fundamental technique widely used to obtain high-precision light curves in optical survey projects like Tianyu. However, its effectiveness is limited in crowded fields, and the choice of aperture size critically impacts photometric precision. To address these challenges, we propose DeepAP, an efficient and accurate two-stage deep learning framework for aperture photometry. Specifically, for a given source, we first train a Vision Transformer (ViT) model to assess its feasibility of aperture photometry. We then train the Residual Neural Network (ResNet) to predict its o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.03400","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/2508.03400/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:48:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dHVJaxzCRTrTxNaqvJJ6E/tAEloN/6da+P3fwGgfMzfhE2Ujrs3Z+V6LPffH/KI66guqsfTXvAwCVWr51WZEDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T02:36:48.092189Z"},"content_sha256":"b9c5a48edc77c7be3467bff1f6a4d23f53b43498ed566488b01cde37b702c7db","schema_version":"1.0","event_id":"sha256:b9c5a48edc77c7be3467bff1f6a4d23f53b43498ed566488b01cde37b702c7db"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SNVX7ZGCNRR7YJND3S5LEDNNGS/bundle.json","state_url":"https://pith.science/pith/SNVX7ZGCNRR7YJND3S5LEDNNGS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SNVX7ZGCNRR7YJND3S5LEDNNGS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T02:36:48Z","links":{"resolver":"https://pith.science/pith/SNVX7ZGCNRR7YJND3S5LEDNNGS","bundle":"https://pith.science/pith/SNVX7ZGCNRR7YJND3S5LEDNNGS/bundle.json","state":"https://pith.science/pith/SNVX7ZGCNRR7YJND3S5LEDNNGS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SNVX7ZGCNRR7YJND3S5LEDNNGS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:SNVX7ZGCNRR7YJND3S5LEDNNGS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0e8d9bd5e4f3fe76121c2102fca71318e3193553269a2145e9f8c72ab4eb5410","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2025-08-05T12:48:44Z","title_canon_sha256":"82106faa1675544e45da566fe35992727a0f003e0df8ca052cd4fd57e89de52b"},"schema_version":"1.0","source":{"id":"2508.03400","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.03400","created_at":"2026-07-05T11:48:55Z"},{"alias_kind":"arxiv_version","alias_value":"2508.03400v1","created_at":"2026-07-05T11:48:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.03400","created_at":"2026-07-05T11:48:55Z"},{"alias_kind":"pith_short_12","alias_value":"SNVX7ZGCNRR7","created_at":"2026-07-05T11:48:55Z"},{"alias_kind":"pith_short_16","alias_value":"SNVX7ZGCNRR7YJND","created_at":"2026-07-05T11:48:55Z"},{"alias_kind":"pith_short_8","alias_value":"SNVX7ZGC","created_at":"2026-07-05T11:48:55Z"}],"graph_snapshots":[{"event_id":"sha256:b9c5a48edc77c7be3467bff1f6a4d23f53b43498ed566488b01cde37b702c7db","target":"graph","created_at":"2026-07-05T11:48:55Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2508.03400/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Aperture photometry is a fundamental technique widely used to obtain high-precision light curves in optical survey projects like Tianyu. However, its effectiveness is limited in crowded fields, and the choice of aperture size critically impacts photometric precision. To address these challenges, we propose DeepAP, an efficient and accurate two-stage deep learning framework for aperture photometry. Specifically, for a given source, we first train a Vision Transformer (ViT) model to assess its feasibility of aperture photometry. We then train the Residual Neural Network (ResNet) to predict its o","authors_text":"Bing-Feng Seng, Dong Li, Fa-bo Feng, Qing-Quan Li, Yi-Cheng Rui, Yi-Fan Xuan, Yu-Li Liu, Yu-Ting Wu, Zheng-Jun Du","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2025-08-05T12:48:44Z","title":"DeepAP: Deep Learning-based Aperture Photometry Feasibility Assessment and Aperture Size Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.03400","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1c208d337cf14ce7a57c69a349b0ba841ec2bacf14284eedca3bd54d1385faf1","target":"record","created_at":"2026-07-05T11:48:55Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0e8d9bd5e4f3fe76121c2102fca71318e3193553269a2145e9f8c72ab4eb5410","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2025-08-05T12:48:44Z","title_canon_sha256":"82106faa1675544e45da566fe35992727a0f003e0df8ca052cd4fd57e89de52b"},"schema_version":"1.0","source":{"id":"2508.03400","kind":"arxiv","version":1}},"canonical_sha256":"936b7fe4c26c63fc25a3dcbab20dad349a23610b91041203cba4c435097757f6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"936b7fe4c26c63fc25a3dcbab20dad349a23610b91041203cba4c435097757f6","first_computed_at":"2026-07-05T11:48:55.860523Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:48:55.860523Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RavJmneMCNHM9S90QQ4DYOwW09+qw6yu4xbOoOqdqrTwr8LRwkVaiSn+FXCUxhY8MyTP8GObcjampx8IJjeqAg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:48:55.861129Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.03400","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1c208d337cf14ce7a57c69a349b0ba841ec2bacf14284eedca3bd54d1385faf1","sha256:b9c5a48edc77c7be3467bff1f6a4d23f53b43498ed566488b01cde37b702c7db"],"state_sha256":"85c6bdb7f9f77ce3955761ea6375d66c26efb451cff38831f7c5f6a639918454"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VrlPQL4gv8uZdPqdOEra9Gf4r7rbtoKhceMCBZJSnpD3eB3Ukd/U741s9ufl4JrLu0UB3ukTHrDr/pI7ZG7SDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T02:36:48.096509Z","bundle_sha256":"38f4e1d759a8d6231cd5c77f11b2a7b9d01e71f9957f2ce7671179b60afb83d0"}}