{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:QGG4VY7GKLNGDTKHBT36VKA6SY","short_pith_number":"pith:QGG4VY7G","schema_version":"1.0","canonical_sha256":"818dcae3e652da61cd470cf7eaa81e9616ffc0b3acc30874dbab28472da3489d","source":{"kind":"arxiv","id":"1811.05993","version":2},"attestation_state":"computed","paper":{"title":"Deep learning in the heterotic orbifold landscape","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ph"],"primary_cat":"hep-th","authors_text":"Andreas M\\\"utter, Erik Parr, Patrick K.S. Vaudrevange","submitted_at":"2018-11-14T19:01:03Z","abstract_excerpt":"We use deep autoencoder neural networks to draw a chart of the heterotic $\\mathbb{Z}_6$-II orbifold landscape. Even though the autoencoder is trained without knowing the phenomenological properties of the $\\mathbb{Z}_6$-II orbifold models, we are able to identify fertile islands in this chart where phenomenologically promising models cluster. Then, we apply a decision tree to our chart in order to extract the defining properties of the fertile islands. Based on this information we propose a new search strategy for phenomenologically promising string models."},"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":"1811.05993","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-th","submitted_at":"2018-11-14T19:01:03Z","cross_cats_sorted":["hep-ph"],"title_canon_sha256":"5d9e0d56d437278ebf527a556be53fcad7e18e731832415626df4c74ae0f58dd","abstract_canon_sha256":"6e791b89d19899e1300fd7be571501f252cb3145595b594ff71084b26ad93196"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:22.041038Z","signature_b64":"DFxGUTpIYsRGk1scaw+z4YOHDaL1/gA/eqlZYv9mjLoSZUg/ZBx6qD4XUST5k0OdOYPIs2apO3Et6C5AxQ9ADQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"818dcae3e652da61cd470cf7eaa81e9616ffc0b3acc30874dbab28472da3489d","last_reissued_at":"2026-05-17T23:54:22.040382Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:22.040382Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep learning in the heterotic orbifold landscape","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ph"],"primary_cat":"hep-th","authors_text":"Andreas M\\\"utter, Erik Parr, Patrick K.S. Vaudrevange","submitted_at":"2018-11-14T19:01:03Z","abstract_excerpt":"We use deep autoencoder neural networks to draw a chart of the heterotic $\\mathbb{Z}_6$-II orbifold landscape. Even though the autoencoder is trained without knowing the phenomenological properties of the $\\mathbb{Z}_6$-II orbifold models, we are able to identify fertile islands in this chart where phenomenologically promising models cluster. Then, we apply a decision tree to our chart in order to extract the defining properties of the fertile islands. Based on this information we propose a new search strategy for phenomenologically promising string models."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.05993","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":"1811.05993","created_at":"2026-05-17T23:54:22.040490+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.05993v2","created_at":"2026-05-17T23:54:22.040490+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.05993","created_at":"2026-05-17T23:54:22.040490+00:00"},{"alias_kind":"pith_short_12","alias_value":"QGG4VY7GKLNG","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"QGG4VY7GKLNGDTKH","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"QGG4VY7G","created_at":"2026-05-18T12:32:46.962924+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/QGG4VY7GKLNGDTKHBT36VKA6SY","json":"https://pith.science/pith/QGG4VY7GKLNGDTKHBT36VKA6SY.json","graph_json":"https://pith.science/api/pith-number/QGG4VY7GKLNGDTKHBT36VKA6SY/graph.json","events_json":"https://pith.science/api/pith-number/QGG4VY7GKLNGDTKHBT36VKA6SY/events.json","paper":"https://pith.science/paper/QGG4VY7G"},"agent_actions":{"view_html":"https://pith.science/pith/QGG4VY7GKLNGDTKHBT36VKA6SY","download_json":"https://pith.science/pith/QGG4VY7GKLNGDTKHBT36VKA6SY.json","view_paper":"https://pith.science/paper/QGG4VY7G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.05993&json=true","fetch_graph":"https://pith.science/api/pith-number/QGG4VY7GKLNGDTKHBT36VKA6SY/graph.json","fetch_events":"https://pith.science/api/pith-number/QGG4VY7GKLNGDTKHBT36VKA6SY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QGG4VY7GKLNGDTKHBT36VKA6SY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QGG4VY7GKLNGDTKHBT36VKA6SY/action/storage_attestation","attest_author":"https://pith.science/pith/QGG4VY7GKLNGDTKHBT36VKA6SY/action/author_attestation","sign_citation":"https://pith.science/pith/QGG4VY7GKLNGDTKHBT36VKA6SY/action/citation_signature","submit_replication":"https://pith.science/pith/QGG4VY7GKLNGDTKHBT36VKA6SY/action/replication_record"}},"created_at":"2026-05-17T23:54:22.040490+00:00","updated_at":"2026-05-17T23:54:22.040490+00:00"}