{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:AXS7N7K35F4WXCLZX2BKWEY46T","short_pith_number":"pith:AXS7N7K3","schema_version":"1.0","canonical_sha256":"05e5f6fd5be9796b8979be82ab131cf4d94aadc35fef9fc36af13d59709970b8","source":{"kind":"arxiv","id":"2010.09049","version":2},"attestation_state":"computed","paper":{"title":"Testing the Quasar Hubble Diagram with LISA Standard Sirens","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["gr-qc"],"primary_cat":"astro-ph.CO","authors_text":"Benjamin Wang, Jonathan R. Gair, Lorenzo Speri, Nicola Tamanini, Robert R. Caldwell","submitted_at":"2020-10-18T18:00:01Z","abstract_excerpt":"Quasars have recently been used as an absolute distance indicator, extending the Hubble diagram to high redshift to reveal a deviation from the expansion history predicted for the standard, $\\Lambda$CDM cosmology. Here we show that the Laser Interferometer Space Antenna (LISA) will efficiently test this claim with standard sirens at high redshift, defined by the coincident gravitational wave (GW) and electromagnetic (EM) observations of the merger of massive black hole binaries (MBHBs). Assuming a fiducial $\\Lambda$CDM cosmology for generating mock standard siren datasets, the evidence for the"},"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":"2010.09049","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2020-10-18T18:00:01Z","cross_cats_sorted":["gr-qc"],"title_canon_sha256":"e53b209da642b43997208b0e57c20ac4cad0c037a7a9919c23e99b6fbd3ef147","abstract_canon_sha256":"b5a66b919d5a3665a156e4e1d78550e0068bb24650c3cefe066359a10c6641f1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:48:19.415082Z","signature_b64":"LQBSbrnZa6NIq+FX0V7fOoGrC+LVIhlFr3TZKiW8VtEP/UihX6A26p3TwnxfYD0BFARWkRMsbHsSBCRpTl82BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"05e5f6fd5be9796b8979be82ab131cf4d94aadc35fef9fc36af13d59709970b8","last_reissued_at":"2026-07-05T02:48:19.414582Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:48:19.414582Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Testing the Quasar Hubble Diagram with LISA Standard Sirens","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["gr-qc"],"primary_cat":"astro-ph.CO","authors_text":"Benjamin Wang, Jonathan R. Gair, Lorenzo Speri, Nicola Tamanini, Robert R. Caldwell","submitted_at":"2020-10-18T18:00:01Z","abstract_excerpt":"Quasars have recently been used as an absolute distance indicator, extending the Hubble diagram to high redshift to reveal a deviation from the expansion history predicted for the standard, $\\Lambda$CDM cosmology. Here we show that the Laser Interferometer Space Antenna (LISA) will efficiently test this claim with standard sirens at high redshift, defined by the coincident gravitational wave (GW) and electromagnetic (EM) observations of the merger of massive black hole binaries (MBHBs). Assuming a fiducial $\\Lambda$CDM cosmology for generating mock standard siren datasets, the evidence for the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.09049","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/2010.09049/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":"2010.09049","created_at":"2026-07-05T02:48:19.414658+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.09049v2","created_at":"2026-07-05T02:48:19.414658+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.09049","created_at":"2026-07-05T02:48:19.414658+00:00"},{"alias_kind":"pith_short_12","alias_value":"AXS7N7K35F4W","created_at":"2026-07-05T02:48:19.414658+00:00"},{"alias_kind":"pith_short_16","alias_value":"AXS7N7K35F4WXCLZ","created_at":"2026-07-05T02:48:19.414658+00:00"},{"alias_kind":"pith_short_8","alias_value":"AXS7N7K3","created_at":"2026-07-05T02:48:19.414658+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.17413","citing_title":"Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows","ref_index":220,"is_internal_anchor":false},{"citing_arxiv_id":"2606.21080","citing_title":"Bayesian Model Averaging under Predictor Redundancy via Density-Ratio Posterior Compression","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03152","citing_title":"Scalable generative modeling of non-Gaussian spatio-temporal fields via autoregressive Gaussian processes","ref_index":181,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AXS7N7K35F4WXCLZX2BKWEY46T","json":"https://pith.science/pith/AXS7N7K35F4WXCLZX2BKWEY46T.json","graph_json":"https://pith.science/api/pith-number/AXS7N7K35F4WXCLZX2BKWEY46T/graph.json","events_json":"https://pith.science/api/pith-number/AXS7N7K35F4WXCLZX2BKWEY46T/events.json","paper":"https://pith.science/paper/AXS7N7K3"},"agent_actions":{"view_html":"https://pith.science/pith/AXS7N7K35F4WXCLZX2BKWEY46T","download_json":"https://pith.science/pith/AXS7N7K35F4WXCLZX2BKWEY46T.json","view_paper":"https://pith.science/paper/AXS7N7K3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.09049&json=true","fetch_graph":"https://pith.science/api/pith-number/AXS7N7K35F4WXCLZX2BKWEY46T/graph.json","fetch_events":"https://pith.science/api/pith-number/AXS7N7K35F4WXCLZX2BKWEY46T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AXS7N7K35F4WXCLZX2BKWEY46T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AXS7N7K35F4WXCLZX2BKWEY46T/action/storage_attestation","attest_author":"https://pith.science/pith/AXS7N7K35F4WXCLZX2BKWEY46T/action/author_attestation","sign_citation":"https://pith.science/pith/AXS7N7K35F4WXCLZX2BKWEY46T/action/citation_signature","submit_replication":"https://pith.science/pith/AXS7N7K35F4WXCLZX2BKWEY46T/action/replication_record"}},"created_at":"2026-07-05T02:48:19.414658+00:00","updated_at":"2026-07-05T02:48:19.414658+00:00"}