{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:S5K3JIY2E6ZUB7YSQGAZ3RPX7D","short_pith_number":"pith:S5K3JIY2","schema_version":"1.0","canonical_sha256":"9755b4a31a27b340ff1281819dc5f7f8c18c9898c93f1ac8344eb41b098a7f18","source":{"kind":"arxiv","id":"1709.05681","version":1},"attestation_state":"computed","paper":{"title":"Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ex","hep-ph"],"primary_cat":"physics.data-an","authors_text":"Alexander Vandenberg-Rodes, Daniel Whiteson, Kyle Cranmer, Meghan Frate, Saarik Kalia","submitted_at":"2017-09-17T15:35:42Z","abstract_excerpt":"We describe a procedure for constructing a model of a smooth data spectrum using Gaussian processes rather than the historical parametric description. This approach considers a fuller space of possible functions, is robust at increasing luminosity, and allows us to incorporate our understanding of the underlying physics. We demonstrate the application of this approach to modeling the background to searches for dijet resonances at the Large Hadron Collider and describe how the approach can be used in the search for generic localized signals."},"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":"1709.05681","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2017-09-17T15:35:42Z","cross_cats_sorted":["hep-ex","hep-ph"],"title_canon_sha256":"943966ee0ae6134ed128981a3110daf86f83a6a10b9d5a888765979ef8fd0125","abstract_canon_sha256":"6461c43d3641c8d8d6f3001732a4c23056caef59b7b961feadbff18af0b8d240"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:59.827948Z","signature_b64":"98obcAfp+loiHXLMmILUgB/Rv51N2J8jeg51nuHeR7I885/69bN0M/eH3EvCnlwKGyaMvZ5RrS4L3FVNgnPQAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9755b4a31a27b340ff1281819dc5f7f8c18c9898c93f1ac8344eb41b098a7f18","last_reissued_at":"2026-05-18T00:34:59.827248Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:59.827248Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ex","hep-ph"],"primary_cat":"physics.data-an","authors_text":"Alexander Vandenberg-Rodes, Daniel Whiteson, Kyle Cranmer, Meghan Frate, Saarik Kalia","submitted_at":"2017-09-17T15:35:42Z","abstract_excerpt":"We describe a procedure for constructing a model of a smooth data spectrum using Gaussian processes rather than the historical parametric description. This approach considers a fuller space of possible functions, is robust at increasing luminosity, and allows us to incorporate our understanding of the underlying physics. We demonstrate the application of this approach to modeling the background to searches for dijet resonances at the Large Hadron Collider and describe how the approach can be used in the search for generic localized signals."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.05681","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":""},"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":"1709.05681","created_at":"2026-05-18T00:34:59.827356+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.05681v1","created_at":"2026-05-18T00:34:59.827356+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.05681","created_at":"2026-05-18T00:34:59.827356+00:00"},{"alias_kind":"pith_short_12","alias_value":"S5K3JIY2E6ZU","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_16","alias_value":"S5K3JIY2E6ZUB7YS","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_8","alias_value":"S5K3JIY2","created_at":"2026-05-18T12:31:43.269735+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2605.20048","citing_title":"Gaussian Process Eigenmodes for Statistical and Systematic Uncertainties in Template Fits","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2510.15151","citing_title":"Enhancing di-jet resonance searches via a final-state radiation jet tagging algorithm","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10378","citing_title":"Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/S5K3JIY2E6ZUB7YSQGAZ3RPX7D","json":"https://pith.science/pith/S5K3JIY2E6ZUB7YSQGAZ3RPX7D.json","graph_json":"https://pith.science/api/pith-number/S5K3JIY2E6ZUB7YSQGAZ3RPX7D/graph.json","events_json":"https://pith.science/api/pith-number/S5K3JIY2E6ZUB7YSQGAZ3RPX7D/events.json","paper":"https://pith.science/paper/S5K3JIY2"},"agent_actions":{"view_html":"https://pith.science/pith/S5K3JIY2E6ZUB7YSQGAZ3RPX7D","download_json":"https://pith.science/pith/S5K3JIY2E6ZUB7YSQGAZ3RPX7D.json","view_paper":"https://pith.science/paper/S5K3JIY2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.05681&json=true","fetch_graph":"https://pith.science/api/pith-number/S5K3JIY2E6ZUB7YSQGAZ3RPX7D/graph.json","fetch_events":"https://pith.science/api/pith-number/S5K3JIY2E6ZUB7YSQGAZ3RPX7D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S5K3JIY2E6ZUB7YSQGAZ3RPX7D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S5K3JIY2E6ZUB7YSQGAZ3RPX7D/action/storage_attestation","attest_author":"https://pith.science/pith/S5K3JIY2E6ZUB7YSQGAZ3RPX7D/action/author_attestation","sign_citation":"https://pith.science/pith/S5K3JIY2E6ZUB7YSQGAZ3RPX7D/action/citation_signature","submit_replication":"https://pith.science/pith/S5K3JIY2E6ZUB7YSQGAZ3RPX7D/action/replication_record"}},"created_at":"2026-05-18T00:34:59.827356+00:00","updated_at":"2026-05-18T00:34:59.827356+00:00"}