{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:5U223THOCDW5FOWCXE5B7Z47QJ","short_pith_number":"pith:5U223THO","schema_version":"1.0","canonical_sha256":"ed35adccee10edd2bac2b93a1fe79f825da7586f9e06db3595797faf96c5f4fd","source":{"kind":"arxiv","id":"1607.07299","version":2},"attestation_state":"computed","paper":{"title":"BaTMAn: Bayesian Technique for Multi-image Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.GA"],"primary_cat":"astro-ph.IM","authors_text":"A.I. D\\'iaz, E. Bellocchi, G. Guidi, J. Casado, O. S. Choudhury, R. Garc\\'ia-Benito, S. F. S\\'anchez, Y. Ascasibar","submitted_at":"2016-07-25T14:55:55Z","abstract_excerpt":"This paper describes the Bayesian Technique for Multi-image Analysis (BaTMAn), a novel image-segmentation technique based on Bayesian statistics that characterizes any astronomical dataset containing spatial information and performs a tessellation based on the measurements and errors provided as input. The algorithm iteratively merges spatial elements as long as they are statistically consistent with carrying the same information (i.e. identical signal within the errors). We illustrate its operation and performance with a set of test cases including both synthetic and real Integral-Field Spect"},"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":"1607.07299","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2016-07-25T14:55:55Z","cross_cats_sorted":["astro-ph.GA"],"title_canon_sha256":"2bc506a1c0bcb8073fb1618c4aa26f8ef9755e66f189978acd52e4f28dd78749","abstract_canon_sha256":"9bbb057ad438c9bef599a6d14a425900bb3ae64bbf7929efe51761f8b2dc163e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:52:39.171770Z","signature_b64":"uRvSBAXLMsiVKLCPm1g9CtDR09TJ9Ncu2oGNIgNNMAuQYecB6QHqErZ/+b5w47OJweaJcrokRTY9wAieJ4ICDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ed35adccee10edd2bac2b93a1fe79f825da7586f9e06db3595797faf96c5f4fd","last_reissued_at":"2026-05-18T00:52:39.171079Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:52:39.171079Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BaTMAn: Bayesian Technique for Multi-image Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.GA"],"primary_cat":"astro-ph.IM","authors_text":"A.I. D\\'iaz, E. Bellocchi, G. Guidi, J. Casado, O. S. Choudhury, R. Garc\\'ia-Benito, S. F. S\\'anchez, Y. Ascasibar","submitted_at":"2016-07-25T14:55:55Z","abstract_excerpt":"This paper describes the Bayesian Technique for Multi-image Analysis (BaTMAn), a novel image-segmentation technique based on Bayesian statistics that characterizes any astronomical dataset containing spatial information and performs a tessellation based on the measurements and errors provided as input. The algorithm iteratively merges spatial elements as long as they are statistically consistent with carrying the same information (i.e. identical signal within the errors). We illustrate its operation and performance with a set of test cases including both synthetic and real Integral-Field Spect"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.07299","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1607.07299","created_at":"2026-05-18T00:52:39.171194+00:00"},{"alias_kind":"arxiv_version","alias_value":"1607.07299v2","created_at":"2026-05-18T00:52:39.171194+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.07299","created_at":"2026-05-18T00:52:39.171194+00:00"},{"alias_kind":"pith_short_12","alias_value":"5U223THOCDW5","created_at":"2026-05-18T12:30:01.593930+00:00"},{"alias_kind":"pith_short_16","alias_value":"5U223THOCDW5FOWC","created_at":"2026-05-18T12:30:01.593930+00:00"},{"alias_kind":"pith_short_8","alias_value":"5U223THO","created_at":"2026-05-18T12:30:01.593930+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5U223THOCDW5FOWCXE5B7Z47QJ","json":"https://pith.science/pith/5U223THOCDW5FOWCXE5B7Z47QJ.json","graph_json":"https://pith.science/api/pith-number/5U223THOCDW5FOWCXE5B7Z47QJ/graph.json","events_json":"https://pith.science/api/pith-number/5U223THOCDW5FOWCXE5B7Z47QJ/events.json","paper":"https://pith.science/paper/5U223THO"},"agent_actions":{"view_html":"https://pith.science/pith/5U223THOCDW5FOWCXE5B7Z47QJ","download_json":"https://pith.science/pith/5U223THOCDW5FOWCXE5B7Z47QJ.json","view_paper":"https://pith.science/paper/5U223THO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1607.07299&json=true","fetch_graph":"https://pith.science/api/pith-number/5U223THOCDW5FOWCXE5B7Z47QJ/graph.json","fetch_events":"https://pith.science/api/pith-number/5U223THOCDW5FOWCXE5B7Z47QJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5U223THOCDW5FOWCXE5B7Z47QJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5U223THOCDW5FOWCXE5B7Z47QJ/action/storage_attestation","attest_author":"https://pith.science/pith/5U223THOCDW5FOWCXE5B7Z47QJ/action/author_attestation","sign_citation":"https://pith.science/pith/5U223THOCDW5FOWCXE5B7Z47QJ/action/citation_signature","submit_replication":"https://pith.science/pith/5U223THOCDW5FOWCXE5B7Z47QJ/action/replication_record"}},"created_at":"2026-05-18T00:52:39.171194+00:00","updated_at":"2026-05-18T00:52:39.171194+00:00"}