{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:NJPOIJQBMBSRG47H6M3HLSZBKB","short_pith_number":"pith:NJPOIJQB","schema_version":"1.0","canonical_sha256":"6a5ee4260160651373e7f33675cb21506e0e14a58f5aa3cb0333a6dd9133eee2","source":{"kind":"arxiv","id":"1407.1783","version":2},"attestation_state":"computed","paper":{"title":"Simulating X-ray Observations with Python","license":"http://creativecommons.org/licenses/by/3.0/","headline":"","cross_cats":["astro-ph.CO"],"primary_cat":"astro-ph.IM","authors_text":"Adam R. Foster (CfA), Christian Schmid (Dr. Karl Remeis-Sternwarte, ECAP), Eric J. Hallman (U. Colorado-Boulder), John A. ZuHone (NASA/GSFC), Scott W. Randall (CfA), Veronica Biffi (SISSA)","submitted_at":"2014-07-07T17:41:05Z","abstract_excerpt":"X-ray astronomy is an important tool in the astrophysicist's toolkit to investigate high-energy astrophysical phenomena. Theoretical numerical simulations of astrophysical sources are fully three-dimensional representations of physical quantities such as density, temperature, and pressure, whereas astronomical observations are two-dimensional projections of the emission generated via mechanisms dependent on these quantities. To bridge the gap between simulations and observations, algorithms for generating synthetic observations of simulated data have been developed. We present an implementatio"},"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":"1407.1783","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"astro-ph.IM","submitted_at":"2014-07-07T17:41:05Z","cross_cats_sorted":["astro-ph.CO"],"title_canon_sha256":"d431a0c565c94c0f5fa360e1f590e47e733ed3e23600f856f3e5c2d90119499e","abstract_canon_sha256":"dadb08a45ad7ca17c9424a5c1bc1096db12f3bf188f235bffc41b3a8b3de2011"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:26:58.886313Z","signature_b64":"SzopgJye4GuhocXEXlOgPK6+9uiXRG9nj/6PCdw46O7t4rDjXIL48ZRR4xO5dSA05YzjUmJQ6ne7joPrK2UVBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6a5ee4260160651373e7f33675cb21506e0e14a58f5aa3cb0333a6dd9133eee2","last_reissued_at":"2026-05-18T02:26:58.885928Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:26:58.885928Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Simulating X-ray Observations with Python","license":"http://creativecommons.org/licenses/by/3.0/","headline":"","cross_cats":["astro-ph.CO"],"primary_cat":"astro-ph.IM","authors_text":"Adam R. Foster (CfA), Christian Schmid (Dr. Karl Remeis-Sternwarte, ECAP), Eric J. Hallman (U. Colorado-Boulder), John A. ZuHone (NASA/GSFC), Scott W. Randall (CfA), Veronica Biffi (SISSA)","submitted_at":"2014-07-07T17:41:05Z","abstract_excerpt":"X-ray astronomy is an important tool in the astrophysicist's toolkit to investigate high-energy astrophysical phenomena. Theoretical numerical simulations of astrophysical sources are fully three-dimensional representations of physical quantities such as density, temperature, and pressure, whereas astronomical observations are two-dimensional projections of the emission generated via mechanisms dependent on these quantities. To bridge the gap between simulations and observations, algorithms for generating synthetic observations of simulated data have been developed. We present an implementatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1407.1783","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":"1407.1783","created_at":"2026-05-18T02:26:58.885991+00:00"},{"alias_kind":"arxiv_version","alias_value":"1407.1783v2","created_at":"2026-05-18T02:26:58.885991+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1407.1783","created_at":"2026-05-18T02:26:58.885991+00:00"},{"alias_kind":"pith_short_12","alias_value":"NJPOIJQBMBSR","created_at":"2026-05-18T12:28:41.024544+00:00"},{"alias_kind":"pith_short_16","alias_value":"NJPOIJQBMBSRG47H","created_at":"2026-05-18T12:28:41.024544+00:00"},{"alias_kind":"pith_short_8","alias_value":"NJPOIJQB","created_at":"2026-05-18T12:28:41.024544+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2511.20429","citing_title":"Estimating the triaxiality of massive clusters from 2D observables in MillenniumTNG with machine learning","ref_index":77,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NJPOIJQBMBSRG47H6M3HLSZBKB","json":"https://pith.science/pith/NJPOIJQBMBSRG47H6M3HLSZBKB.json","graph_json":"https://pith.science/api/pith-number/NJPOIJQBMBSRG47H6M3HLSZBKB/graph.json","events_json":"https://pith.science/api/pith-number/NJPOIJQBMBSRG47H6M3HLSZBKB/events.json","paper":"https://pith.science/paper/NJPOIJQB"},"agent_actions":{"view_html":"https://pith.science/pith/NJPOIJQBMBSRG47H6M3HLSZBKB","download_json":"https://pith.science/pith/NJPOIJQBMBSRG47H6M3HLSZBKB.json","view_paper":"https://pith.science/paper/NJPOIJQB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1407.1783&json=true","fetch_graph":"https://pith.science/api/pith-number/NJPOIJQBMBSRG47H6M3HLSZBKB/graph.json","fetch_events":"https://pith.science/api/pith-number/NJPOIJQBMBSRG47H6M3HLSZBKB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NJPOIJQBMBSRG47H6M3HLSZBKB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NJPOIJQBMBSRG47H6M3HLSZBKB/action/storage_attestation","attest_author":"https://pith.science/pith/NJPOIJQBMBSRG47H6M3HLSZBKB/action/author_attestation","sign_citation":"https://pith.science/pith/NJPOIJQBMBSRG47H6M3HLSZBKB/action/citation_signature","submit_replication":"https://pith.science/pith/NJPOIJQBMBSRG47H6M3HLSZBKB/action/replication_record"}},"created_at":"2026-05-18T02:26:58.885991+00:00","updated_at":"2026-05-18T02:26:58.885991+00:00"}