{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:CPCZNXI7RASMYMMVVBWFGFYYAO","short_pith_number":"pith:CPCZNXI7","schema_version":"1.0","canonical_sha256":"13c596dd1f8824cc3195a86c5317180385a09a4c95feb0883c1f6cae82d7ec88","source":{"kind":"arxiv","id":"1910.14654","version":2},"attestation_state":"computed","paper":{"title":"Comparison of unfolding methods using RooFitUnfold","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ex"],"primary_cat":"physics.data-an","authors_text":"Carsten Burgard, Glen Cowan, Lydia Brenner, Pim Verschuuren, Rahul Balasubramanian, Vincent Croft, Wouter Verkerke","submitted_at":"2019-10-31T17:46:17Z","abstract_excerpt":"In this paper we describe RooFitUnfold, an extension of the RooFit statistical software package to treat unfolding problems, and which includes most of the unfolding methods that commonly used in particle physics. The package provides a common interface to these algorithms as well as common uniform methods to evaluate their performance in terms of bias, variance and coverage. In this paper we exploit this common interface of RooFitUnfold to compare the performance of unfolding with the Richardson-Lucy, Iterative Dynamically Stabilized, Tikhonov, Gaussian Process, Bin-by-bin and inversion metho"},"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":"1910.14654","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2019-10-31T17:46:17Z","cross_cats_sorted":["hep-ex"],"title_canon_sha256":"a74ef68642b4b522745ce2ce524c1891e7857f49755971d6e55794d65af1718f","abstract_canon_sha256":"31375447cb08c64e06dd7deee79155d6a62fc6813b58803f01425d2d91adcc77"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:02:28.102435Z","signature_b64":"KtKBlcmKlRaov4e2xHXIu8nePyW1LLz5MSyfC4RV7QlDqNNSUXBYShTOWGCqH939/TFY+dr+UXtXeFIv19fNBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"13c596dd1f8824cc3195a86c5317180385a09a4c95feb0883c1f6cae82d7ec88","last_reissued_at":"2026-07-05T01:02:28.102031Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:02:28.102031Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Comparison of unfolding methods using RooFitUnfold","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ex"],"primary_cat":"physics.data-an","authors_text":"Carsten Burgard, Glen Cowan, Lydia Brenner, Pim Verschuuren, Rahul Balasubramanian, Vincent Croft, Wouter Verkerke","submitted_at":"2019-10-31T17:46:17Z","abstract_excerpt":"In this paper we describe RooFitUnfold, an extension of the RooFit statistical software package to treat unfolding problems, and which includes most of the unfolding methods that commonly used in particle physics. The package provides a common interface to these algorithms as well as common uniform methods to evaluate their performance in terms of bias, variance and coverage. In this paper we exploit this common interface of RooFitUnfold to compare the performance of unfolding with the Richardson-Lucy, Iterative Dynamically Stabilized, Tikhonov, Gaussian Process, Bin-by-bin and inversion metho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.14654","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/1910.14654/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":"1910.14654","created_at":"2026-07-05T01:02:28.102086+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.14654v2","created_at":"2026-07-05T01:02:28.102086+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.14654","created_at":"2026-07-05T01:02:28.102086+00:00"},{"alias_kind":"pith_short_12","alias_value":"CPCZNXI7RASM","created_at":"2026-07-05T01:02:28.102086+00:00"},{"alias_kind":"pith_short_16","alias_value":"CPCZNXI7RASMYMMV","created_at":"2026-07-05T01:02:28.102086+00:00"},{"alias_kind":"pith_short_8","alias_value":"CPCZNXI7","created_at":"2026-07-05T01:02:28.102086+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.18093","citing_title":"Probing jet evolution with charged energy correlators in small systems","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2606.13527","citing_title":"Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06603","citing_title":"Reweighting Adversarial Networks for Unbinned Unfolding","ref_index":55,"is_internal_anchor":false},{"citing_arxiv_id":"2511.05807","citing_title":"Measurement of $\\pi^0$ Production in $\\bar{\\nu}_{\\mu}$ Charged-Current Interactions in the NOvA Near Detector","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CPCZNXI7RASMYMMVVBWFGFYYAO","json":"https://pith.science/pith/CPCZNXI7RASMYMMVVBWFGFYYAO.json","graph_json":"https://pith.science/api/pith-number/CPCZNXI7RASMYMMVVBWFGFYYAO/graph.json","events_json":"https://pith.science/api/pith-number/CPCZNXI7RASMYMMVVBWFGFYYAO/events.json","paper":"https://pith.science/paper/CPCZNXI7"},"agent_actions":{"view_html":"https://pith.science/pith/CPCZNXI7RASMYMMVVBWFGFYYAO","download_json":"https://pith.science/pith/CPCZNXI7RASMYMMVVBWFGFYYAO.json","view_paper":"https://pith.science/paper/CPCZNXI7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.14654&json=true","fetch_graph":"https://pith.science/api/pith-number/CPCZNXI7RASMYMMVVBWFGFYYAO/graph.json","fetch_events":"https://pith.science/api/pith-number/CPCZNXI7RASMYMMVVBWFGFYYAO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CPCZNXI7RASMYMMVVBWFGFYYAO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CPCZNXI7RASMYMMVVBWFGFYYAO/action/storage_attestation","attest_author":"https://pith.science/pith/CPCZNXI7RASMYMMVVBWFGFYYAO/action/author_attestation","sign_citation":"https://pith.science/pith/CPCZNXI7RASMYMMVVBWFGFYYAO/action/citation_signature","submit_replication":"https://pith.science/pith/CPCZNXI7RASMYMMVVBWFGFYYAO/action/replication_record"}},"created_at":"2026-07-05T01:02:28.102086+00:00","updated_at":"2026-07-05T01:02:28.102086+00:00"}