{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:I7VTJZ6SA4Q7GLO5HADT4UQ7CJ","short_pith_number":"pith:I7VTJZ6S","schema_version":"1.0","canonical_sha256":"47eb34e7d20721f32ddd38073e521f126f31a1f78d66d0fa3eaf0836236ef37c","source":{"kind":"arxiv","id":"1810.11030","version":2},"attestation_state":"computed","paper":{"title":"Distinguishing standard and modified gravity cosmologies with machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Austin Peel, Carlo Giocoli, Florian Lalande, Jean-Luc Starck, Julian Merten, Marco Baldi, Massimo Meneghetti, Valeria Pettorino","submitted_at":"2018-10-25T18:00:06Z","abstract_excerpt":"We present a convolutional neural network to classify distinct cosmological scenarios based on the statistically similar weak-lensing maps they generate. Modified gravity (MG) models that include massive neutrinos can mimic the standard concordance model ($\\Lambda$CDM) in terms of Gaussian weak-lensing observables. An inability to distinguish viable models that are based on different physics potentially limits a deeper understanding of the fundamental nature of cosmic acceleration. For a fixed redshift of sources, we demonstrate that a machine learning network trained on simulated convergence "},"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":"1810.11030","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2018-10-25T18:00:06Z","cross_cats_sorted":[],"title_canon_sha256":"bb60e9fc422dd8aae216f0b03852c2703d3660e08f0a6f386307176739b2213b","abstract_canon_sha256":"42fd0aaddb8e9835c446e4375df311129c5ad98810492738e2cc6de7d7905e70"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:34.237207Z","signature_b64":"v39g41jF4MfFF3cutX/FJCcc6btLnYDIi8/V538BoHAWjYXBB6aIsMU54+q1Ti/TJnP0TXYjTCk98T+Y0grZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47eb34e7d20721f32ddd38073e521f126f31a1f78d66d0fa3eaf0836236ef37c","last_reissued_at":"2026-05-17T23:40:34.236797Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:34.236797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distinguishing standard and modified gravity cosmologies with machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Austin Peel, Carlo Giocoli, Florian Lalande, Jean-Luc Starck, Julian Merten, Marco Baldi, Massimo Meneghetti, Valeria Pettorino","submitted_at":"2018-10-25T18:00:06Z","abstract_excerpt":"We present a convolutional neural network to classify distinct cosmological scenarios based on the statistically similar weak-lensing maps they generate. Modified gravity (MG) models that include massive neutrinos can mimic the standard concordance model ($\\Lambda$CDM) in terms of Gaussian weak-lensing observables. An inability to distinguish viable models that are based on different physics potentially limits a deeper understanding of the fundamental nature of cosmic acceleration. For a fixed redshift of sources, we demonstrate that a machine learning network trained on simulated convergence "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.11030","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":""},"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":"1810.11030","created_at":"2026-05-17T23:40:34.236862+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.11030v2","created_at":"2026-05-17T23:40:34.236862+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.11030","created_at":"2026-05-17T23:40:34.236862+00:00"},{"alias_kind":"pith_short_12","alias_value":"I7VTJZ6SA4Q7","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"I7VTJZ6SA4Q7GLO5","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"I7VTJZ6S","created_at":"2026-05-18T12:32:28.185984+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.22462","citing_title":"Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective","ref_index":251,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I7VTJZ6SA4Q7GLO5HADT4UQ7CJ","json":"https://pith.science/pith/I7VTJZ6SA4Q7GLO5HADT4UQ7CJ.json","graph_json":"https://pith.science/api/pith-number/I7VTJZ6SA4Q7GLO5HADT4UQ7CJ/graph.json","events_json":"https://pith.science/api/pith-number/I7VTJZ6SA4Q7GLO5HADT4UQ7CJ/events.json","paper":"https://pith.science/paper/I7VTJZ6S"},"agent_actions":{"view_html":"https://pith.science/pith/I7VTJZ6SA4Q7GLO5HADT4UQ7CJ","download_json":"https://pith.science/pith/I7VTJZ6SA4Q7GLO5HADT4UQ7CJ.json","view_paper":"https://pith.science/paper/I7VTJZ6S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.11030&json=true","fetch_graph":"https://pith.science/api/pith-number/I7VTJZ6SA4Q7GLO5HADT4UQ7CJ/graph.json","fetch_events":"https://pith.science/api/pith-number/I7VTJZ6SA4Q7GLO5HADT4UQ7CJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I7VTJZ6SA4Q7GLO5HADT4UQ7CJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I7VTJZ6SA4Q7GLO5HADT4UQ7CJ/action/storage_attestation","attest_author":"https://pith.science/pith/I7VTJZ6SA4Q7GLO5HADT4UQ7CJ/action/author_attestation","sign_citation":"https://pith.science/pith/I7VTJZ6SA4Q7GLO5HADT4UQ7CJ/action/citation_signature","submit_replication":"https://pith.science/pith/I7VTJZ6SA4Q7GLO5HADT4UQ7CJ/action/replication_record"}},"created_at":"2026-05-17T23:40:34.236862+00:00","updated_at":"2026-05-17T23:40:34.236862+00:00"}