{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:DLVWPDPZN6AJIUBFF4RTAMQRXK","short_pith_number":"pith:DLVWPDPZ","canonical_record":{"source":{"id":"2504.06174","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2025-04-08T16:18:39Z","cross_cats_sorted":[],"title_canon_sha256":"69aab55a6fc628ece3b3a001ff6c65d40c5e9eb7cc3d035bc7b6308876c42ac8","abstract_canon_sha256":"07d3da0f8f08f714f7daa26a8d59dcb1a7713b775880485b9dd2a39e931b7d33"},"schema_version":"1.0"},"canonical_sha256":"1aeb678df96f809450252f23303211baa93c7a9a5b18c753a79382f4f146e066","source":{"kind":"arxiv","id":"2504.06174","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.06174","created_at":"2026-07-05T12:10:57Z"},{"alias_kind":"arxiv_version","alias_value":"2504.06174v3","created_at":"2026-07-05T12:10:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.06174","created_at":"2026-07-05T12:10:57Z"},{"alias_kind":"pith_short_12","alias_value":"DLVWPDPZN6AJ","created_at":"2026-07-05T12:10:57Z"},{"alias_kind":"pith_short_16","alias_value":"DLVWPDPZN6AJIUBF","created_at":"2026-07-05T12:10:57Z"},{"alias_kind":"pith_short_8","alias_value":"DLVWPDPZ","created_at":"2026-07-05T12:10:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:DLVWPDPZN6AJIUBFF4RTAMQRXK","target":"record","payload":{"canonical_record":{"source":{"id":"2504.06174","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2025-04-08T16:18:39Z","cross_cats_sorted":[],"title_canon_sha256":"69aab55a6fc628ece3b3a001ff6c65d40c5e9eb7cc3d035bc7b6308876c42ac8","abstract_canon_sha256":"07d3da0f8f08f714f7daa26a8d59dcb1a7713b775880485b9dd2a39e931b7d33"},"schema_version":"1.0"},"canonical_sha256":"1aeb678df96f809450252f23303211baa93c7a9a5b18c753a79382f4f146e066","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:10:57.129369Z","signature_b64":"f544MwhxwBtrXqCka6rbJyjRT96WhxUvSKpzDCVJl7JS88nybjbptzGH6uNx+Q7UT59jf7Pq4HSOSFOaQjM8Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1aeb678df96f809450252f23303211baa93c7a9a5b18c753a79382f4f146e066","last_reissued_at":"2026-07-05T12:10:57.128847Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:10:57.128847Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.06174","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T12:10:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1EB7FK1BfWFMQ/w8JBFCv6b/NMS5xCYjw2y9mLt6cjasue8BWG6SzX9n2GqzYm+xL0QbvmPZWkOzCsB3iUe+Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-13T22:22:57.318435Z"},"content_sha256":"b318813286058351d9c0735cc74eff0d30d617d09575ef9a320d070cc4da5d38","schema_version":"1.0","event_id":"sha256:b318813286058351d9c0735cc74eff0d30d617d09575ef9a320d070cc4da5d38"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:DLVWPDPZN6AJIUBFF4RTAMQRXK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On Soft Clustering For Correlation Estimators","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Andreas Faisst, Anton Koekemoer, Caitlin Casey, COSMOS-Web: The JWST Cosmic Origins Survey, Diana Scognamiglio, Edward Berman, Ghassem Gozaliasl, Jacqueline McCleary, Jeyhan Kartaltepe, Marko Shuntov, Natalie Hogg, Nicole Drakos, Sneh Pandya, Steven Gillman, Wilfried Mercier","submitted_at":"2025-04-08T16:18:39Z","abstract_excerpt":"Properly estimating correlations between objects at different spatial scales necessitates $\\mathcal{O}(n^2)$ distance calculations. For this reason, most widely adopted packages for estimating correlations use clustering algorithms to approximate local trends. However, methods for quantifying the error introduced by this clustering have been understudied. In response, we present an algorithm for estimating correlations that is probabilistic in the way that it clusters objects, enabling us to quantify the uncertainty caused by clustering simply through model inference. These soft clustering ass"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.06174","kind":"arxiv","version":3},"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/2504.06174/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":"2026-07-05T12:10:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XOIemHepB6BxmK1H4a3Yqy39nmIOFNb07Jw5uxi3CPCuWhJCUV4pVcRN4jZwz9248TaIO24pcxAMCz9OHKLwAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-13T22:22:57.318808Z"},"content_sha256":"9665143d5bf8c974571e104c1dbfea152c7f05c800aa52084f926fe7ebb3d2f1","schema_version":"1.0","event_id":"sha256:9665143d5bf8c974571e104c1dbfea152c7f05c800aa52084f926fe7ebb3d2f1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DLVWPDPZN6AJIUBFF4RTAMQRXK/bundle.json","state_url":"https://pith.science/pith/DLVWPDPZN6AJIUBFF4RTAMQRXK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DLVWPDPZN6AJIUBFF4RTAMQRXK/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":"2026-07-13T22:22:57Z","links":{"resolver":"https://pith.science/pith/DLVWPDPZN6AJIUBFF4RTAMQRXK","bundle":"https://pith.science/pith/DLVWPDPZN6AJIUBFF4RTAMQRXK/bundle.json","state":"https://pith.science/pith/DLVWPDPZN6AJIUBFF4RTAMQRXK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DLVWPDPZN6AJIUBFF4RTAMQRXK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:DLVWPDPZN6AJIUBFF4RTAMQRXK","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":"07d3da0f8f08f714f7daa26a8d59dcb1a7713b775880485b9dd2a39e931b7d33","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2025-04-08T16:18:39Z","title_canon_sha256":"69aab55a6fc628ece3b3a001ff6c65d40c5e9eb7cc3d035bc7b6308876c42ac8"},"schema_version":"1.0","source":{"id":"2504.06174","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.06174","created_at":"2026-07-05T12:10:57Z"},{"alias_kind":"arxiv_version","alias_value":"2504.06174v3","created_at":"2026-07-05T12:10:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.06174","created_at":"2026-07-05T12:10:57Z"},{"alias_kind":"pith_short_12","alias_value":"DLVWPDPZN6AJ","created_at":"2026-07-05T12:10:57Z"},{"alias_kind":"pith_short_16","alias_value":"DLVWPDPZN6AJIUBF","created_at":"2026-07-05T12:10:57Z"},{"alias_kind":"pith_short_8","alias_value":"DLVWPDPZ","created_at":"2026-07-05T12:10:57Z"}],"graph_snapshots":[{"event_id":"sha256:9665143d5bf8c974571e104c1dbfea152c7f05c800aa52084f926fe7ebb3d2f1","target":"graph","created_at":"2026-07-05T12:10:57Z","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/2504.06174/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Properly estimating correlations between objects at different spatial scales necessitates $\\mathcal{O}(n^2)$ distance calculations. For this reason, most widely adopted packages for estimating correlations use clustering algorithms to approximate local trends. However, methods for quantifying the error introduced by this clustering have been understudied. In response, we present an algorithm for estimating correlations that is probabilistic in the way that it clusters objects, enabling us to quantify the uncertainty caused by clustering simply through model inference. These soft clustering ass","authors_text":"Andreas Faisst, Anton Koekemoer, Caitlin Casey, COSMOS-Web: The JWST Cosmic Origins Survey, Diana Scognamiglio, Edward Berman, Ghassem Gozaliasl, Jacqueline McCleary, Jeyhan Kartaltepe, Marko Shuntov, Natalie Hogg, Nicole Drakos, Sneh Pandya, Steven Gillman, Wilfried Mercier","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2025-04-08T16:18:39Z","title":"On Soft Clustering For Correlation Estimators"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.06174","kind":"arxiv","version":3},"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:b318813286058351d9c0735cc74eff0d30d617d09575ef9a320d070cc4da5d38","target":"record","created_at":"2026-07-05T12:10:57Z","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":"07d3da0f8f08f714f7daa26a8d59dcb1a7713b775880485b9dd2a39e931b7d33","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2025-04-08T16:18:39Z","title_canon_sha256":"69aab55a6fc628ece3b3a001ff6c65d40c5e9eb7cc3d035bc7b6308876c42ac8"},"schema_version":"1.0","source":{"id":"2504.06174","kind":"arxiv","version":3}},"canonical_sha256":"1aeb678df96f809450252f23303211baa93c7a9a5b18c753a79382f4f146e066","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1aeb678df96f809450252f23303211baa93c7a9a5b18c753a79382f4f146e066","first_computed_at":"2026-07-05T12:10:57.128847Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:10:57.128847Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"f544MwhxwBtrXqCka6rbJyjRT96WhxUvSKpzDCVJl7JS88nybjbptzGH6uNx+Q7UT59jf7Pq4HSOSFOaQjM8Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T12:10:57.129369Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.06174","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b318813286058351d9c0735cc74eff0d30d617d09575ef9a320d070cc4da5d38","sha256:9665143d5bf8c974571e104c1dbfea152c7f05c800aa52084f926fe7ebb3d2f1"],"state_sha256":"6069c92994cfeab5d2a7f124926a64a643cd3ef550a4e6172aa0ce0d5a897f13"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/rUH69H2M1U8kUv49lUBiFfoDu+RvVMuiU1p8bj6klSW3iIbZY4CtHTtiKs4nWk9pv/TInPj5hK8VDyearZQDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-13T22:22:57.320811Z","bundle_sha256":"e3edf158c3f4cb6010dec4c1c0f148e375c5cc07f18db23a34451e22b0c61887"}}