{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:743IQPHAJIACUJRSDSXS474PZW","short_pith_number":"pith:743IQPHA","canonical_record":{"source":{"id":"2012.01985","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.SR","submitted_at":"2020-12-02T07:14:11Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c22c10d2eadef0515e8f556c7435153170da28e261c04934862c516d0861bdca","abstract_canon_sha256":"6776481f693bdeb80fabb35852dfe6afecec64d4b7384b6617be2189d62e7a74"},"schema_version":"1.0"},"canonical_sha256":"ff36883ce04a002a26321caf2e7f8fcd94dc5d415a92413914b2ff48a81979fd","source":{"kind":"arxiv","id":"2012.01985","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.01985","created_at":"2026-07-05T01:56:57Z"},{"alias_kind":"arxiv_version","alias_value":"2012.01985v2","created_at":"2026-07-05T01:56:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.01985","created_at":"2026-07-05T01:56:57Z"},{"alias_kind":"pith_short_12","alias_value":"743IQPHAJIAC","created_at":"2026-07-05T01:56:57Z"},{"alias_kind":"pith_short_16","alias_value":"743IQPHAJIACUJRS","created_at":"2026-07-05T01:56:57Z"},{"alias_kind":"pith_short_8","alias_value":"743IQPHA","created_at":"2026-07-05T01:56:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:743IQPHAJIACUJRSDSXS474PZW","target":"record","payload":{"canonical_record":{"source":{"id":"2012.01985","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.SR","submitted_at":"2020-12-02T07:14:11Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c22c10d2eadef0515e8f556c7435153170da28e261c04934862c516d0861bdca","abstract_canon_sha256":"6776481f693bdeb80fabb35852dfe6afecec64d4b7384b6617be2189d62e7a74"},"schema_version":"1.0"},"canonical_sha256":"ff36883ce04a002a26321caf2e7f8fcd94dc5d415a92413914b2ff48a81979fd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:56:57.063899Z","signature_b64":"jawdbcc5CysPR5u4OP8Wd0nk/tIXafx/B9/XkQ8MBA0pbLPN8H43g5In8T69bncK+z3iq+gkuyO/nrFo6dXeAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff36883ce04a002a26321caf2e7f8fcd94dc5d415a92413914b2ff48a81979fd","last_reissued_at":"2026-07-05T01:56:57.063398Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:56:57.063398Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2012.01985","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":"2026-07-05T01:56:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Pr10sRfpIRgIDYfpWORcUAA+RlvXFfScHH+wIv3y3yFFqf4tPUjNANR/9lZgaOOplx4nax43yhHbQhLnrK91AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T10:10:06.848791Z"},"content_sha256":"9a8da986d7abd993c4d4766a96dafa155dbe7c5101e33cb76a2cf879fbe99b64","schema_version":"1.0","event_id":"sha256:9a8da986d7abd993c4d4766a96dafa155dbe7c5101e33cb76a2cf879fbe99b64"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:743IQPHAJIACUJRSDSXS474PZW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"astro-ph.SR","authors_text":"Andr\\'es Mu\\~noz-Jaramillo, Anna Jungbluth, Brett Morris, Daniel K. Giles, J. Emmanuel Johnson, Lisseth Gavilan, Sairam Sundaresan, Stela Ishitani Silva, Tansu Daylan","submitted_at":"2020-12-02T07:14:11Z","abstract_excerpt":"Magnetic activity in stars manifests as dark spots on their surfaces that modulate the brightness observed by telescopes. These light curves contain important information on stellar rotation. However, the accurate estimation of rotation periods is computationally expensive due to scarce ground truth information, noisy data, and large parameter spaces that lead to degenerate solutions. We harness the power of deep learning and successfully apply Convolutional Neural Networks to regress stellar rotation periods from Kepler light curves. Geometry-preserving time-series to image transformations of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.01985","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/2012.01985/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":"2026-07-05T01:56:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W8iO+DBel9g4ww2wpjCz1SRsTXStPjUTiGopjQTmUe2KUjiFIGUfiA2Sf4hczd5Mi70TGcansIUWeMITCuWMCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T10:10:06.849201Z"},"content_sha256":"30ba801f2b1a6b24abb94d2931ea8523accb9561b8746116b5b049eaa8461f2c","schema_version":"1.0","event_id":"sha256:30ba801f2b1a6b24abb94d2931ea8523accb9561b8746116b5b049eaa8461f2c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/743IQPHAJIACUJRSDSXS474PZW/bundle.json","state_url":"https://pith.science/pith/743IQPHAJIACUJRSDSXS474PZW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/743IQPHAJIACUJRSDSXS474PZW/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":"2026-07-07T10:10:06Z","links":{"resolver":"https://pith.science/pith/743IQPHAJIACUJRSDSXS474PZW","bundle":"https://pith.science/pith/743IQPHAJIACUJRSDSXS474PZW/bundle.json","state":"https://pith.science/pith/743IQPHAJIACUJRSDSXS474PZW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/743IQPHAJIACUJRSDSXS474PZW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:743IQPHAJIACUJRSDSXS474PZW","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":"6776481f693bdeb80fabb35852dfe6afecec64d4b7384b6617be2189d62e7a74","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.SR","submitted_at":"2020-12-02T07:14:11Z","title_canon_sha256":"c22c10d2eadef0515e8f556c7435153170da28e261c04934862c516d0861bdca"},"schema_version":"1.0","source":{"id":"2012.01985","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.01985","created_at":"2026-07-05T01:56:57Z"},{"alias_kind":"arxiv_version","alias_value":"2012.01985v2","created_at":"2026-07-05T01:56:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.01985","created_at":"2026-07-05T01:56:57Z"},{"alias_kind":"pith_short_12","alias_value":"743IQPHAJIAC","created_at":"2026-07-05T01:56:57Z"},{"alias_kind":"pith_short_16","alias_value":"743IQPHAJIACUJRS","created_at":"2026-07-05T01:56:57Z"},{"alias_kind":"pith_short_8","alias_value":"743IQPHA","created_at":"2026-07-05T01:56:57Z"}],"graph_snapshots":[{"event_id":"sha256:30ba801f2b1a6b24abb94d2931ea8523accb9561b8746116b5b049eaa8461f2c","target":"graph","created_at":"2026-07-05T01:56:57Z","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/2012.01985/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Magnetic activity in stars manifests as dark spots on their surfaces that modulate the brightness observed by telescopes. These light curves contain important information on stellar rotation. However, the accurate estimation of rotation periods is computationally expensive due to scarce ground truth information, noisy data, and large parameter spaces that lead to degenerate solutions. We harness the power of deep learning and successfully apply Convolutional Neural Networks to regress stellar rotation periods from Kepler light curves. Geometry-preserving time-series to image transformations of","authors_text":"Andr\\'es Mu\\~noz-Jaramillo, Anna Jungbluth, Brett Morris, Daniel K. Giles, J. Emmanuel Johnson, Lisseth Gavilan, Sairam Sundaresan, Stela Ishitani Silva, Tansu Daylan","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.SR","submitted_at":"2020-12-02T07:14:11Z","title":"RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.01985","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":"sha256:9a8da986d7abd993c4d4766a96dafa155dbe7c5101e33cb76a2cf879fbe99b64","target":"record","created_at":"2026-07-05T01:56:57Z","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":"6776481f693bdeb80fabb35852dfe6afecec64d4b7384b6617be2189d62e7a74","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.SR","submitted_at":"2020-12-02T07:14:11Z","title_canon_sha256":"c22c10d2eadef0515e8f556c7435153170da28e261c04934862c516d0861bdca"},"schema_version":"1.0","source":{"id":"2012.01985","kind":"arxiv","version":2}},"canonical_sha256":"ff36883ce04a002a26321caf2e7f8fcd94dc5d415a92413914b2ff48a81979fd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ff36883ce04a002a26321caf2e7f8fcd94dc5d415a92413914b2ff48a81979fd","first_computed_at":"2026-07-05T01:56:57.063398Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:56:57.063398Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jawdbcc5CysPR5u4OP8Wd0nk/tIXafx/B9/XkQ8MBA0pbLPN8H43g5In8T69bncK+z3iq+gkuyO/nrFo6dXeAg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:56:57.063899Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.01985","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a8da986d7abd993c4d4766a96dafa155dbe7c5101e33cb76a2cf879fbe99b64","sha256:30ba801f2b1a6b24abb94d2931ea8523accb9561b8746116b5b049eaa8461f2c"],"state_sha256":"f32846bf8bb0ab1e004014ec9346c3ba810c8368f16d5be79c1e03ffc95ba7bf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"am8NueR1qSn/IVlnEICmfckei1xQzjiT81e94aAsuMZ84PdJs2ZWfanvhuiGfxauLu7LqXI4496TOZeOAanTAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T10:10:06.851162Z","bundle_sha256":"bddb5f9332b5fde60d7825ea478c7eb3e5af6fddd46e4dbde1d8c8c5de977dca"}}