{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:UPER5S7LBABCSK7367I32NXRBP","short_pith_number":"pith:UPER5S7L","schema_version":"1.0","canonical_sha256":"a3c91ecbeb0802292bfbf7d1bd36f10bfa29b0c1f667158f5cf7de559c383fdd","source":{"kind":"arxiv","id":"2302.13494","version":1},"attestation_state":"computed","paper":{"title":"X-ray Spectral Estimation using Dictionary Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Charles A. Bouman, Gregery T. Buzzard, Jean-Baptiste Forien, K. Aditya Mohan, Saransh Singh, Venkatesh Sridhar, Wenrui Li, Xin Liu","submitted_at":"2023-02-27T03:42:35Z","abstract_excerpt":"As computational tools for X-ray computed tomography (CT) become more quantitatively accurate, knowledge of the source-detector spectral response is critical for quantitative system-independent reconstruction and material characterization capabilities. Directly measuring the spectral response of a CT system is hard, which motivates spectral estimation using transmission data obtained from a collection of known homogeneous objects. However, the associated inverse problem is ill-conditioned, making accurate estimation of the spectrum challenging, particularly in the absence of a close initial gu"},"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":"2302.13494","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-02-27T03:42:35Z","cross_cats_sorted":[],"title_canon_sha256":"9696da9276a40d2d30087cfe7cf1ce639ef8cf426bb364905143eb1061d757bc","abstract_canon_sha256":"f3895600e98c2da23b7a9e9ab67841259cc0edf2aaf3ec99cb761a6b0c399ddb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:45:47.753158Z","signature_b64":"sWoNhWyM907skkSjML8/IJ8AmoT+YA9vnrtMfe7fnbP2zyNHnFmvQwSaT6EkP+lhHKDx6JUuQtf9Kbh5rEs3Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3c91ecbeb0802292bfbf7d1bd36f10bfa29b0c1f667158f5cf7de559c383fdd","last_reissued_at":"2026-07-05T05:45:47.752719Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:45:47.752719Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"X-ray Spectral Estimation using Dictionary Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Charles A. Bouman, Gregery T. Buzzard, Jean-Baptiste Forien, K. Aditya Mohan, Saransh Singh, Venkatesh Sridhar, Wenrui Li, Xin Liu","submitted_at":"2023-02-27T03:42:35Z","abstract_excerpt":"As computational tools for X-ray computed tomography (CT) become more quantitatively accurate, knowledge of the source-detector spectral response is critical for quantitative system-independent reconstruction and material characterization capabilities. Directly measuring the spectral response of a CT system is hard, which motivates spectral estimation using transmission data obtained from a collection of known homogeneous objects. However, the associated inverse problem is ill-conditioned, making accurate estimation of the spectrum challenging, particularly in the absence of a close initial gu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.13494","kind":"arxiv","version":1},"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/2302.13494/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":"2302.13494","created_at":"2026-07-05T05:45:47.752767+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.13494v1","created_at":"2026-07-05T05:45:47.752767+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.13494","created_at":"2026-07-05T05:45:47.752767+00:00"},{"alias_kind":"pith_short_12","alias_value":"UPER5S7LBABC","created_at":"2026-07-05T05:45:47.752767+00:00"},{"alias_kind":"pith_short_16","alias_value":"UPER5S7LBABCSK73","created_at":"2026-07-05T05:45:47.752767+00:00"},{"alias_kind":"pith_short_8","alias_value":"UPER5S7L","created_at":"2026-07-05T05:45:47.752767+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/UPER5S7LBABCSK7367I32NXRBP","json":"https://pith.science/pith/UPER5S7LBABCSK7367I32NXRBP.json","graph_json":"https://pith.science/api/pith-number/UPER5S7LBABCSK7367I32NXRBP/graph.json","events_json":"https://pith.science/api/pith-number/UPER5S7LBABCSK7367I32NXRBP/events.json","paper":"https://pith.science/paper/UPER5S7L"},"agent_actions":{"view_html":"https://pith.science/pith/UPER5S7LBABCSK7367I32NXRBP","download_json":"https://pith.science/pith/UPER5S7LBABCSK7367I32NXRBP.json","view_paper":"https://pith.science/paper/UPER5S7L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.13494&json=true","fetch_graph":"https://pith.science/api/pith-number/UPER5S7LBABCSK7367I32NXRBP/graph.json","fetch_events":"https://pith.science/api/pith-number/UPER5S7LBABCSK7367I32NXRBP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UPER5S7LBABCSK7367I32NXRBP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UPER5S7LBABCSK7367I32NXRBP/action/storage_attestation","attest_author":"https://pith.science/pith/UPER5S7LBABCSK7367I32NXRBP/action/author_attestation","sign_citation":"https://pith.science/pith/UPER5S7LBABCSK7367I32NXRBP/action/citation_signature","submit_replication":"https://pith.science/pith/UPER5S7LBABCSK7367I32NXRBP/action/replication_record"}},"created_at":"2026-07-05T05:45:47.752767+00:00","updated_at":"2026-07-05T05:45:47.752767+00:00"}