{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FPHMGBSQMMF5CRUVADMAEZORVQ","short_pith_number":"pith:FPHMGBSQ","schema_version":"1.0","canonical_sha256":"2bcec30650630bd1469500d80265d1ac029f64f4f381e1ea1f883104933cd6b7","source":{"kind":"arxiv","id":"2403.05413","version":2},"attestation_state":"computed","paper":{"title":"Large deviation principle for the largest eigenvalue of random matrices with a variance profile","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.PR","authors_text":"Alice Guionnet, Jonathan Husson, Rapha\\\"el Ducatez","submitted_at":"2024-03-08T16:13:33Z","abstract_excerpt":"We establish large deviation principles for the largest eigenvalue of large random matrices with variance profiles. For $N \\in \\mathbb N$, we consider random $N \\times N$ symmetric matrices $H^N$ which are such that $H_{ij}^{N}=\\frac{1}{\\sqrt{N}}X_{i,j}^{N}$ for $1 \\leq i,j \\leq N$, where the $X_{i,j}^{N}$ for $1 \\leq i \\leq j \\leq N$ are independent and centered. We then denote $\\Sigma_{i,j} ^N = \\text{Var} (X_{i,j}^{N}) ( 1 + \\textbf{1}_{ i =j})^{-1}$ the variance profile of $H^N$. Our large deviation principle is then stated under the assumption that the $\\Sigma^N$ converge in a certain sen"},"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":"2403.05413","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.PR","submitted_at":"2024-03-08T16:13:33Z","cross_cats_sorted":[],"title_canon_sha256":"7259490ca9e9cdc57f4dc707106008d3debf510d0f1b2bdeb37bd7c19c680a43","abstract_canon_sha256":"4741c68a4fd73c39fda1b52edd3d02789e3df3c9db5200e0a02f88be5a2096fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:59:22.251115Z","signature_b64":"Jr7Fr/vs9fvnLElhmAXxsOw9sSGxq+wSjhwB4rRaYb3QYLdQajh759S8pSsSMisPIvdPCsZHAHI5fvSO74TOBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2bcec30650630bd1469500d80265d1ac029f64f4f381e1ea1f883104933cd6b7","last_reissued_at":"2026-07-05T07:59:22.250720Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:59:22.250720Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large deviation principle for the largest eigenvalue of random matrices with a variance profile","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.PR","authors_text":"Alice Guionnet, Jonathan Husson, Rapha\\\"el Ducatez","submitted_at":"2024-03-08T16:13:33Z","abstract_excerpt":"We establish large deviation principles for the largest eigenvalue of large random matrices with variance profiles. For $N \\in \\mathbb N$, we consider random $N \\times N$ symmetric matrices $H^N$ which are such that $H_{ij}^{N}=\\frac{1}{\\sqrt{N}}X_{i,j}^{N}$ for $1 \\leq i,j \\leq N$, where the $X_{i,j}^{N}$ for $1 \\leq i \\leq j \\leq N$ are independent and centered. We then denote $\\Sigma_{i,j} ^N = \\text{Var} (X_{i,j}^{N}) ( 1 + \\textbf{1}_{ i =j})^{-1}$ the variance profile of $H^N$. Our large deviation principle is then stated under the assumption that the $\\Sigma^N$ converge in a certain sen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.05413","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/2403.05413/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":"2403.05413","created_at":"2026-07-05T07:59:22.250777+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.05413v2","created_at":"2026-07-05T07:59:22.250777+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.05413","created_at":"2026-07-05T07:59:22.250777+00:00"},{"alias_kind":"pith_short_12","alias_value":"FPHMGBSQMMF5","created_at":"2026-07-05T07:59:22.250777+00:00"},{"alias_kind":"pith_short_16","alias_value":"FPHMGBSQMMF5CRUV","created_at":"2026-07-05T07:59:22.250777+00:00"},{"alias_kind":"pith_short_8","alias_value":"FPHMGBSQ","created_at":"2026-07-05T07:59:22.250777+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25501","citing_title":"An RDT based approach to large deviations of Wishart and Wigner matrices spectral edges","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2408.09256","citing_title":"Large deviations for the smallest eigenvalue of a deformed GOE with an outlier","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FPHMGBSQMMF5CRUVADMAEZORVQ","json":"https://pith.science/pith/FPHMGBSQMMF5CRUVADMAEZORVQ.json","graph_json":"https://pith.science/api/pith-number/FPHMGBSQMMF5CRUVADMAEZORVQ/graph.json","events_json":"https://pith.science/api/pith-number/FPHMGBSQMMF5CRUVADMAEZORVQ/events.json","paper":"https://pith.science/paper/FPHMGBSQ"},"agent_actions":{"view_html":"https://pith.science/pith/FPHMGBSQMMF5CRUVADMAEZORVQ","download_json":"https://pith.science/pith/FPHMGBSQMMF5CRUVADMAEZORVQ.json","view_paper":"https://pith.science/paper/FPHMGBSQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.05413&json=true","fetch_graph":"https://pith.science/api/pith-number/FPHMGBSQMMF5CRUVADMAEZORVQ/graph.json","fetch_events":"https://pith.science/api/pith-number/FPHMGBSQMMF5CRUVADMAEZORVQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FPHMGBSQMMF5CRUVADMAEZORVQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FPHMGBSQMMF5CRUVADMAEZORVQ/action/storage_attestation","attest_author":"https://pith.science/pith/FPHMGBSQMMF5CRUVADMAEZORVQ/action/author_attestation","sign_citation":"https://pith.science/pith/FPHMGBSQMMF5CRUVADMAEZORVQ/action/citation_signature","submit_replication":"https://pith.science/pith/FPHMGBSQMMF5CRUVADMAEZORVQ/action/replication_record"}},"created_at":"2026-07-05T07:59:22.250777+00:00","updated_at":"2026-07-05T07:59:22.250777+00:00"}