{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:6SWMF6BEUE54DZWN4W4ONKGICQ","short_pith_number":"pith:6SWMF6BE","canonical_record":{"source":{"id":"1705.05857","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2017-05-16T18:00:09Z","cross_cats_sorted":["astro-ph.GA"],"title_canon_sha256":"f472dd7391081213217e9b4175387a0d9579ebdc88cbfb9f1b698c68c6f661a7","abstract_canon_sha256":"8e3054f98867fbb8918021fe161f548622a05a22d509e8dafd8d1bbac1e19702"},"schema_version":"1.0"},"canonical_sha256":"f4acc2f824a13bc1e6cde5b8e6a8c8140b8cf428dc1dc8a01b09ad7abee9f471","source":{"kind":"arxiv","id":"1705.05857","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.05857","created_at":"2026-05-18T00:18:53Z"},{"alias_kind":"arxiv_version","alias_value":"1705.05857v2","created_at":"2026-05-18T00:18:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.05857","created_at":"2026-05-18T00:18:53Z"},{"alias_kind":"pith_short_12","alias_value":"6SWMF6BEUE54","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6SWMF6BEUE54DZWN","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6SWMF6BE","created_at":"2026-05-18T12:31:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:6SWMF6BEUE54DZWN4W4ONKGICQ","target":"record","payload":{"canonical_record":{"source":{"id":"1705.05857","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2017-05-16T18:00:09Z","cross_cats_sorted":["astro-ph.GA"],"title_canon_sha256":"f472dd7391081213217e9b4175387a0d9579ebdc88cbfb9f1b698c68c6f661a7","abstract_canon_sha256":"8e3054f98867fbb8918021fe161f548622a05a22d509e8dafd8d1bbac1e19702"},"schema_version":"1.0"},"canonical_sha256":"f4acc2f824a13bc1e6cde5b8e6a8c8140b8cf428dc1dc8a01b09ad7abee9f471","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:53.790599Z","signature_b64":"WKZsyz3CgpsY2nTcM86muNA8+goF1UM/po9J4SMO8mG4Q1UK4icd89BNC5j431oQ149OH9RXmsaWpqgZMIVRAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4acc2f824a13bc1e6cde5b8e6a8c8140b8cf428dc1dc8a01b09ad7abee9f471","last_reissued_at":"2026-05-18T00:18:53.790036Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:53.790036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.05857","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:18:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"py1QMYqH2G3Fqqc08DjSctYQ2s6MIuCizzrmsfcLhiPNTMvQ7Q704rek5iMcqHIA1Gn+stsxPZxd7BmK6IwbAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T07:04:33.842760Z"},"content_sha256":"1459d623a201946fe24e36e59c4600bc9ae175fff69555b313bd23f250edd44e","schema_version":"1.0","event_id":"sha256:1459d623a201946fe24e36e59c4600bc9ae175fff69555b313bd23f250edd44e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:6SWMF6BEUE54DZWN4W4ONKGICQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LensExtractor: A Convolutional Neural Network in Search of Strong Gravitational Lenses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.GA"],"primary_cat":"astro-ph.IM","authors_text":"Asantha Cooray, Hooshang Nayyeri, Milad Pourrahmani","submitted_at":"2017-05-16T18:00:09Z","abstract_excerpt":"In this work, we present our classification algorithm to identify strong gravitational lenses from wide-area surveys using machine learning convolutional neural network; LensExtractor. We train and test the algorithm using a wide variety of strong gravitational lens configurations from simulations of lensing events. Images are processed through multiple convolutional layers which extract feature maps necessary to assign a lens probability to each image. LensExtractor provides a ranking scheme for all sources which could be used to identify potential gravitational lens candidates significantly "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.05857","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":""},"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":"2026-05-18T00:18:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7rYDIJ5+lpP65zA5lTr6OUlXl9muxFe673psiCKxc5DUnCRBkM1dbB9dX0DPPBLLOAyOkr3wo9j6wOVOwYbrBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T07:04:33.843117Z"},"content_sha256":"207415897f95427476fae648ebcf4bbd6c387422d0f936e37cb1899dc26c2be6","schema_version":"1.0","event_id":"sha256:207415897f95427476fae648ebcf4bbd6c387422d0f936e37cb1899dc26c2be6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6SWMF6BEUE54DZWN4W4ONKGICQ/bundle.json","state_url":"https://pith.science/pith/6SWMF6BEUE54DZWN4W4ONKGICQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6SWMF6BEUE54DZWN4W4ONKGICQ/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":"2026-05-25T07:04:33Z","links":{"resolver":"https://pith.science/pith/6SWMF6BEUE54DZWN4W4ONKGICQ","bundle":"https://pith.science/pith/6SWMF6BEUE54DZWN4W4ONKGICQ/bundle.json","state":"https://pith.science/pith/6SWMF6BEUE54DZWN4W4ONKGICQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6SWMF6BEUE54DZWN4W4ONKGICQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:6SWMF6BEUE54DZWN4W4ONKGICQ","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":"8e3054f98867fbb8918021fe161f548622a05a22d509e8dafd8d1bbac1e19702","cross_cats_sorted":["astro-ph.GA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2017-05-16T18:00:09Z","title_canon_sha256":"f472dd7391081213217e9b4175387a0d9579ebdc88cbfb9f1b698c68c6f661a7"},"schema_version":"1.0","source":{"id":"1705.05857","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.05857","created_at":"2026-05-18T00:18:53Z"},{"alias_kind":"arxiv_version","alias_value":"1705.05857v2","created_at":"2026-05-18T00:18:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.05857","created_at":"2026-05-18T00:18:53Z"},{"alias_kind":"pith_short_12","alias_value":"6SWMF6BEUE54","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6SWMF6BEUE54DZWN","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6SWMF6BE","created_at":"2026-05-18T12:31:03Z"}],"graph_snapshots":[{"event_id":"sha256:207415897f95427476fae648ebcf4bbd6c387422d0f936e37cb1899dc26c2be6","target":"graph","created_at":"2026-05-18T00:18:53Z","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"},"paper":{"abstract_excerpt":"In this work, we present our classification algorithm to identify strong gravitational lenses from wide-area surveys using machine learning convolutional neural network; LensExtractor. We train and test the algorithm using a wide variety of strong gravitational lens configurations from simulations of lensing events. Images are processed through multiple convolutional layers which extract feature maps necessary to assign a lens probability to each image. LensExtractor provides a ranking scheme for all sources which could be used to identify potential gravitational lens candidates significantly ","authors_text":"Asantha Cooray, Hooshang Nayyeri, Milad Pourrahmani","cross_cats":["astro-ph.GA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2017-05-16T18:00:09Z","title":"LensExtractor: A Convolutional Neural Network in Search of Strong Gravitational Lenses"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.05857","kind":"arxiv","version":2},"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":"sha256:1459d623a201946fe24e36e59c4600bc9ae175fff69555b313bd23f250edd44e","target":"record","created_at":"2026-05-18T00:18:53Z","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":"8e3054f98867fbb8918021fe161f548622a05a22d509e8dafd8d1bbac1e19702","cross_cats_sorted":["astro-ph.GA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2017-05-16T18:00:09Z","title_canon_sha256":"f472dd7391081213217e9b4175387a0d9579ebdc88cbfb9f1b698c68c6f661a7"},"schema_version":"1.0","source":{"id":"1705.05857","kind":"arxiv","version":2}},"canonical_sha256":"f4acc2f824a13bc1e6cde5b8e6a8c8140b8cf428dc1dc8a01b09ad7abee9f471","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f4acc2f824a13bc1e6cde5b8e6a8c8140b8cf428dc1dc8a01b09ad7abee9f471","first_computed_at":"2026-05-18T00:18:53.790036Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:18:53.790036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WKZsyz3CgpsY2nTcM86muNA8+goF1UM/po9J4SMO8mG4Q1UK4icd89BNC5j431oQ149OH9RXmsaWpqgZMIVRAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:18:53.790599Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.05857","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1459d623a201946fe24e36e59c4600bc9ae175fff69555b313bd23f250edd44e","sha256:207415897f95427476fae648ebcf4bbd6c387422d0f936e37cb1899dc26c2be6"],"state_sha256":"eb14aa1a7b2f4a922fc55255870c0c8b862485379b55ec75649260a80bd66784"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NhH3rP8kM9I9fHTEJ2RNXEeX/DlKWCAfReEEDXA0D9JF+B0VujJGX+BFHCxcOhPbvJB0lCNdgiQTodtDQA2zAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T07:04:33.845742Z","bundle_sha256":"5496a75a8b14d1d76d6a01b05e197f2e4fedacc619d3fd1e5109ffdc4acc9469"}}