{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TLXXXFI6M6IEBC3YXVCNAUSCOD","short_pith_number":"pith:TLXXXFI6","schema_version":"1.0","canonical_sha256":"9aef7b951e6790408b78bd44d0524270fb08d971cc15cd07e3559ec7c3a1cfb9","source":{"kind":"arxiv","id":"2502.01627","version":2},"attestation_state":"computed","paper":{"title":"A Poisson Process AutoDecoder for X-ray Sources","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.HE","cs.LG","stat.AP"],"primary_cat":"astro-ph.IM","authors_text":"Juan Rafael Martinez-Galarza, Steven Dillmann, Victoria Ashley Villar, Yanke Song","submitted_at":"2025-02-03T18:56:39Z","abstract_excerpt":"X-ray observing facilities, such as the Chandra X-ray Observatory and the eROSITA, have detected millions of astronomical sources associated with high-energy phenomena. The arrival of photons as a function of time follows a Poisson process and can vary by orders-of-magnitude, presenting obstacles for common tasks such as source classification, physical property derivation, and anomaly detection. Previous work has either failed to directly capture the Poisson nature of the data or only focuses on Poisson rate function reconstruction. In this work, we present Poisson Process AutoDecoder (PPAD). "},"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":"2502.01627","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2025-02-03T18:56:39Z","cross_cats_sorted":["astro-ph.HE","cs.LG","stat.AP"],"title_canon_sha256":"a9baf90bb91a238bf91d80527f49cb715086541717a86d32f3215727e2f80998","abstract_canon_sha256":"144b7244a152a0b237eb90f6d849db329b15178c756823257501bfd6c0d4ae7e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:09:52.758879Z","signature_b64":"08rUZsU6xzlzmEaGcBT7zro8UqoLosqc8uYWnGrqi06GWxRs/0T7Duh1Lit6r3wNGOnneE5Ovu1HUh8VCReRAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9aef7b951e6790408b78bd44d0524270fb08d971cc15cd07e3559ec7c3a1cfb9","last_reissued_at":"2026-07-05T10:09:52.758474Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:09:52.758474Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Poisson Process AutoDecoder for X-ray Sources","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.HE","cs.LG","stat.AP"],"primary_cat":"astro-ph.IM","authors_text":"Juan Rafael Martinez-Galarza, Steven Dillmann, Victoria Ashley Villar, Yanke Song","submitted_at":"2025-02-03T18:56:39Z","abstract_excerpt":"X-ray observing facilities, such as the Chandra X-ray Observatory and the eROSITA, have detected millions of astronomical sources associated with high-energy phenomena. The arrival of photons as a function of time follows a Poisson process and can vary by orders-of-magnitude, presenting obstacles for common tasks such as source classification, physical property derivation, and anomaly detection. Previous work has either failed to directly capture the Poisson nature of the data or only focuses on Poisson rate function reconstruction. In this work, we present Poisson Process AutoDecoder (PPAD). "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.01627","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/2502.01627/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2502.01627","created_at":"2026-07-05T10:09:52.758527+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.01627v2","created_at":"2026-07-05T10:09:52.758527+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.01627","created_at":"2026-07-05T10:09:52.758527+00:00"},{"alias_kind":"pith_short_12","alias_value":"TLXXXFI6M6IE","created_at":"2026-07-05T10:09:52.758527+00:00"},{"alias_kind":"pith_short_16","alias_value":"TLXXXFI6M6IEBC3Y","created_at":"2026-07-05T10:09:52.758527+00:00"},{"alias_kind":"pith_short_8","alias_value":"TLXXXFI6","created_at":"2026-07-05T10:09:52.758527+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2509.24954","citing_title":"Stellar flare detection in XMM-Newton with gradient boosted trees","ref_index":48,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TLXXXFI6M6IEBC3YXVCNAUSCOD","json":"https://pith.science/pith/TLXXXFI6M6IEBC3YXVCNAUSCOD.json","graph_json":"https://pith.science/api/pith-number/TLXXXFI6M6IEBC3YXVCNAUSCOD/graph.json","events_json":"https://pith.science/api/pith-number/TLXXXFI6M6IEBC3YXVCNAUSCOD/events.json","paper":"https://pith.science/paper/TLXXXFI6"},"agent_actions":{"view_html":"https://pith.science/pith/TLXXXFI6M6IEBC3YXVCNAUSCOD","download_json":"https://pith.science/pith/TLXXXFI6M6IEBC3YXVCNAUSCOD.json","view_paper":"https://pith.science/paper/TLXXXFI6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.01627&json=true","fetch_graph":"https://pith.science/api/pith-number/TLXXXFI6M6IEBC3YXVCNAUSCOD/graph.json","fetch_events":"https://pith.science/api/pith-number/TLXXXFI6M6IEBC3YXVCNAUSCOD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TLXXXFI6M6IEBC3YXVCNAUSCOD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TLXXXFI6M6IEBC3YXVCNAUSCOD/action/storage_attestation","attest_author":"https://pith.science/pith/TLXXXFI6M6IEBC3YXVCNAUSCOD/action/author_attestation","sign_citation":"https://pith.science/pith/TLXXXFI6M6IEBC3YXVCNAUSCOD/action/citation_signature","submit_replication":"https://pith.science/pith/TLXXXFI6M6IEBC3YXVCNAUSCOD/action/replication_record"}},"created_at":"2026-07-05T10:09:52.758527+00:00","updated_at":"2026-07-05T10:09:52.758527+00:00"}