{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:SLT7TOEGEJ2YW4LNJC44PHZNMY","short_pith_number":"pith:SLT7TOEG","schema_version":"1.0","canonical_sha256":"92e7f9b88622758b716d48b9c79f2d660403f24add40662a048df1ce6b7d10a9","source":{"kind":"arxiv","id":"1906.03156","version":2},"attestation_state":"computed","paper":{"title":"Cosmological constraints with deep learning from KiDS-450 weak lensing maps","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Adam Amara, Alexandre Refregier, Aurelien Lucchi, Aurel Schneider (ETH Zurich), Janis Fluri, Thomas Hofmann, Tomasz Kacprzak","submitted_at":"2019-06-07T15:20:18Z","abstract_excerpt":"Convolutional Neural Networks (CNN) have recently been demonstrated on synthetic data to improve upon the precision of cosmological inference. In particular they have the potential to yield more precise cosmological constraints from weak lensing mass maps than the two-point functions. We present the cosmological results with a CNN from the KiDS-450 tomographic weak lensing dataset, constraining the total matter density $\\Omega_m$, the fluctuation amplitude $\\sigma_8$, and the intrinsic alignment amplitude $A_{\\rm{IA}}$. We use a grid of N-body simulations to generate a training set of tomograp"},"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":"1906.03156","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2019-06-07T15:20:18Z","cross_cats_sorted":[],"title_canon_sha256":"5852c537cf642d98467cc565817548858852c2dcbfba11a929554a8b9e771235","abstract_canon_sha256":"7b02ca283c8bb80a9ac6517e0a1d257d2d5e611da1044c267f3bb7e5d2e1417f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:04:42.609002Z","signature_b64":"8p7ORgfYUSyWrgosKWvoldNTJkh1nanOrXp//l5AYCnsZ+QzRVwhCFpmG5KOhp74U7eenviT6TTL6/birPpCCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92e7f9b88622758b716d48b9c79f2d660403f24add40662a048df1ce6b7d10a9","last_reissued_at":"2026-07-05T00:04:42.608535Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:04:42.608535Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cosmological constraints with deep learning from KiDS-450 weak lensing maps","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Adam Amara, Alexandre Refregier, Aurelien Lucchi, Aurel Schneider (ETH Zurich), Janis Fluri, Thomas Hofmann, Tomasz Kacprzak","submitted_at":"2019-06-07T15:20:18Z","abstract_excerpt":"Convolutional Neural Networks (CNN) have recently been demonstrated on synthetic data to improve upon the precision of cosmological inference. In particular they have the potential to yield more precise cosmological constraints from weak lensing mass maps than the two-point functions. We present the cosmological results with a CNN from the KiDS-450 tomographic weak lensing dataset, constraining the total matter density $\\Omega_m$, the fluctuation amplitude $\\sigma_8$, and the intrinsic alignment amplitude $A_{\\rm{IA}}$. We use a grid of N-body simulations to generate a training set of tomograp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.03156","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/1906.03156/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":"1906.03156","created_at":"2026-07-05T00:04:42.608593+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.03156v2","created_at":"2026-07-05T00:04:42.608593+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.03156","created_at":"2026-07-05T00:04:42.608593+00:00"},{"alias_kind":"pith_short_12","alias_value":"SLT7TOEGEJ2Y","created_at":"2026-07-05T00:04:42.608593+00:00"},{"alias_kind":"pith_short_16","alias_value":"SLT7TOEGEJ2YW4LN","created_at":"2026-07-05T00:04:42.608593+00:00"},{"alias_kind":"pith_short_8","alias_value":"SLT7TOEG","created_at":"2026-07-05T00:04:42.608593+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.23114","citing_title":"Increasing the Precision of Surrogate Models for Weak Lensing Mass Maps with Flow Matching","ref_index":35,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SLT7TOEGEJ2YW4LNJC44PHZNMY","json":"https://pith.science/pith/SLT7TOEGEJ2YW4LNJC44PHZNMY.json","graph_json":"https://pith.science/api/pith-number/SLT7TOEGEJ2YW4LNJC44PHZNMY/graph.json","events_json":"https://pith.science/api/pith-number/SLT7TOEGEJ2YW4LNJC44PHZNMY/events.json","paper":"https://pith.science/paper/SLT7TOEG"},"agent_actions":{"view_html":"https://pith.science/pith/SLT7TOEGEJ2YW4LNJC44PHZNMY","download_json":"https://pith.science/pith/SLT7TOEGEJ2YW4LNJC44PHZNMY.json","view_paper":"https://pith.science/paper/SLT7TOEG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.03156&json=true","fetch_graph":"https://pith.science/api/pith-number/SLT7TOEGEJ2YW4LNJC44PHZNMY/graph.json","fetch_events":"https://pith.science/api/pith-number/SLT7TOEGEJ2YW4LNJC44PHZNMY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SLT7TOEGEJ2YW4LNJC44PHZNMY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SLT7TOEGEJ2YW4LNJC44PHZNMY/action/storage_attestation","attest_author":"https://pith.science/pith/SLT7TOEGEJ2YW4LNJC44PHZNMY/action/author_attestation","sign_citation":"https://pith.science/pith/SLT7TOEGEJ2YW4LNJC44PHZNMY/action/citation_signature","submit_replication":"https://pith.science/pith/SLT7TOEGEJ2YW4LNJC44PHZNMY/action/replication_record"}},"created_at":"2026-07-05T00:04:42.608593+00:00","updated_at":"2026-07-05T00:04:42.608593+00:00"}