{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:7GWK2FVGR6QUHSTHX33SHMYCDI","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":"519e46609168f009d677bcdb698330c27fbb993e1f0cc5ee494d49f4e1ae5ea7","cross_cats_sorted":["astro-ph.IM","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-19T15:03:21Z","title_canon_sha256":"3a0fe2ddfe7c096d401b08aef5d207bf62f766eb94986acd142e9ee0162031af"},"schema_version":"1.0","source":{"id":"1911.08327","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.08327","created_at":"2026-07-05T00:22:43Z"},{"alias_kind":"arxiv_version","alias_value":"1911.08327v1","created_at":"2026-07-05T00:22:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.08327","created_at":"2026-07-05T00:22:43Z"},{"alias_kind":"pith_short_12","alias_value":"7GWK2FVGR6QU","created_at":"2026-07-05T00:22:43Z"},{"alias_kind":"pith_short_16","alias_value":"7GWK2FVGR6QUHSTH","created_at":"2026-07-05T00:22:43Z"},{"alias_kind":"pith_short_8","alias_value":"7GWK2FVG","created_at":"2026-07-05T00:22:43Z"}],"graph_snapshots":[{"event_id":"sha256:502179630c73c7988dfe07f5779b03ec80dda23a695114cf16de9caf46760cfb","target":"graph","created_at":"2026-07-05T00:22:43Z","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/1911.08327/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Polarization measurements done using Imaging Polarimeters such as the Robotic Polarimeter are very sensitive to the presence of artefacts in images. Artefacts can range from internal reflections in a telescope to satellite trails that could contaminate an area of interest in the image. With the advent of wide-field polarimetry surveys, it is imperative to develop methods that automatically flag artefacts in images. In this paper, we implement a Convolutional Neural Network to identify the most dominant artefacts in the images. We find that our model can successfully classify sources with 98\\% ","authors_text":"A.N. Ramaprakash, Anthony Readhead, Ashish Mahabal, Dhruv Paranjpye, Dmitry Blinov, Gina Panopoulou, Kieran Cleary, Kostas Tassis","cross_cats":["astro-ph.IM","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-19T15:03:21Z","title":"Eliminating artefacts in Polarimetric Images using Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.08327","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:bcc744286573b9cb9b0a165b1f0d45fd06a3012c039eca83d1770c2ba2999758","target":"record","created_at":"2026-07-05T00:22:43Z","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":"519e46609168f009d677bcdb698330c27fbb993e1f0cc5ee494d49f4e1ae5ea7","cross_cats_sorted":["astro-ph.IM","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-19T15:03:21Z","title_canon_sha256":"3a0fe2ddfe7c096d401b08aef5d207bf62f766eb94986acd142e9ee0162031af"},"schema_version":"1.0","source":{"id":"1911.08327","kind":"arxiv","version":1}},"canonical_sha256":"f9acad16a68fa143ca67bef723b3021a05cc215fe9cf5bc8df30be4fd0ea317a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f9acad16a68fa143ca67bef723b3021a05cc215fe9cf5bc8df30be4fd0ea317a","first_computed_at":"2026-07-05T00:22:43.290794Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:22:43.290794Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bSq9a1Ve2tBXK0CvIy5KxUPeyl8kaQFUl8rxxGDd9ryfAP/6cirBMuMKWzyXGC1k5sSBfJGVrrzcKb8lTEBqCg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:22:43.291179Z","signed_message":"canonical_sha256_bytes"},"source_id":"1911.08327","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bcc744286573b9cb9b0a165b1f0d45fd06a3012c039eca83d1770c2ba2999758","sha256:502179630c73c7988dfe07f5779b03ec80dda23a695114cf16de9caf46760cfb"],"state_sha256":"3fc20bcb1805e415e4ef5f749b7632fb722b46f4493bbe8f0f6a436d0694d08b"}