{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:BEXWZ2XCGDZ4E23ZIC7L5GXJLY","short_pith_number":"pith:BEXWZ2XC","schema_version":"1.0","canonical_sha256":"092f6ceae230f3c26b7940bebe9ae95e36f0484dfcb77ff300ae0aeebab1200f","source":{"kind":"arxiv","id":"2601.10420","version":1},"attestation_state":"computed","paper":{"title":"On the reconstruction of kinematic distributions computed with Monte Carlo methods using orthogonal basis functions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Ivan Novikov, Ivan Pedron, Kirill Melnikov","submitted_at":"2026-01-15T14:12:26Z","abstract_excerpt":"Reconstruction of one-dimensional kinematic distributions from calculations based on high-dimensional Monte-Carlo integration is a standard problem in high-energy physics. Traditionally, this is done by collecting randomly-generated events in histograms. In this article, we explore an alternative approach, whose main idea is to approximate the target distribution by a weighted sum of orthogonal basis functions whose coefficients are calculated using the Monte-Carlo integration. This method has the advantage of directly yielding smooth approximations to target distributions. Furthermore, in 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":"2601.10420","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ph","submitted_at":"2026-01-15T14:12:26Z","cross_cats_sorted":[],"title_canon_sha256":"06578430e8b326fb42c870cf1e57f2c2f13e6f294f0f239dcc4be095c0fad9d5","abstract_canon_sha256":"ef6082729c1de6d02ab3c11e7122eb48ed5a5abdab5dee0cf002d102938cceaf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T14:03:24.283012Z","signature_b64":"QiIBurWrR24Nx1Etsm+feumUI8Yd6+sFEo00ZQi//fFJM1vGkWn9Cn/KxA+i9WgkF2ktQL9m0LYwQSnoIYqEDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"092f6ceae230f3c26b7940bebe9ae95e36f0484dfcb77ff300ae0aeebab1200f","last_reissued_at":"2026-05-20T14:03:24.282543Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T14:03:24.282543Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the reconstruction of kinematic distributions computed with Monte Carlo methods using orthogonal basis functions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Ivan Novikov, Ivan Pedron, Kirill Melnikov","submitted_at":"2026-01-15T14:12:26Z","abstract_excerpt":"Reconstruction of one-dimensional kinematic distributions from calculations based on high-dimensional Monte-Carlo integration is a standard problem in high-energy physics. Traditionally, this is done by collecting randomly-generated events in histograms. In this article, we explore an alternative approach, whose main idea is to approximate the target distribution by a weighted sum of orthogonal basis functions whose coefficients are calculated using the Monte-Carlo integration. This method has the advantage of directly yielding smooth approximations to target distributions. Furthermore, in the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.10420","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/2601.10420/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":"2601.10420","created_at":"2026-05-20T14:03:24.282592+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.10420v1","created_at":"2026-05-20T14:03:24.282592+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.10420","created_at":"2026-05-20T14:03:24.282592+00:00"},{"alias_kind":"pith_short_12","alias_value":"BEXWZ2XCGDZ4","created_at":"2026-05-20T14:03:24.282592+00:00"},{"alias_kind":"pith_short_16","alias_value":"BEXWZ2XCGDZ4E23Z","created_at":"2026-05-20T14:03:24.282592+00:00"},{"alias_kind":"pith_short_8","alias_value":"BEXWZ2XC","created_at":"2026-05-20T14:03:24.282592+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/BEXWZ2XCGDZ4E23ZIC7L5GXJLY","json":"https://pith.science/pith/BEXWZ2XCGDZ4E23ZIC7L5GXJLY.json","graph_json":"https://pith.science/api/pith-number/BEXWZ2XCGDZ4E23ZIC7L5GXJLY/graph.json","events_json":"https://pith.science/api/pith-number/BEXWZ2XCGDZ4E23ZIC7L5GXJLY/events.json","paper":"https://pith.science/paper/BEXWZ2XC"},"agent_actions":{"view_html":"https://pith.science/pith/BEXWZ2XCGDZ4E23ZIC7L5GXJLY","download_json":"https://pith.science/pith/BEXWZ2XCGDZ4E23ZIC7L5GXJLY.json","view_paper":"https://pith.science/paper/BEXWZ2XC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.10420&json=true","fetch_graph":"https://pith.science/api/pith-number/BEXWZ2XCGDZ4E23ZIC7L5GXJLY/graph.json","fetch_events":"https://pith.science/api/pith-number/BEXWZ2XCGDZ4E23ZIC7L5GXJLY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BEXWZ2XCGDZ4E23ZIC7L5GXJLY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BEXWZ2XCGDZ4E23ZIC7L5GXJLY/action/storage_attestation","attest_author":"https://pith.science/pith/BEXWZ2XCGDZ4E23ZIC7L5GXJLY/action/author_attestation","sign_citation":"https://pith.science/pith/BEXWZ2XCGDZ4E23ZIC7L5GXJLY/action/citation_signature","submit_replication":"https://pith.science/pith/BEXWZ2XCGDZ4E23ZIC7L5GXJLY/action/replication_record"}},"created_at":"2026-05-20T14:03:24.282592+00:00","updated_at":"2026-05-20T14:03:24.282592+00:00"}