{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:AKK4V6BLPGEYKAEYFFTCZBTXDQ","short_pith_number":"pith:AKK4V6BL","canonical_record":{"source":{"id":"2011.04434","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ex","submitted_at":"2020-11-09T13:53:29Z","cross_cats_sorted":[],"title_canon_sha256":"5664d7dc5bdc6c07c7756cb429bdb8446c5e19e3dad3ca3c10cdef3ec2c3b37d","abstract_canon_sha256":"f801a4ef05d5236e490b56dcc8459fdd1e2a0789afe92ce3af39001e34abd0fb"},"schema_version":"1.0"},"canonical_sha256":"0295caf82b798985009829662c86771c356599672c38443706b7832a8bc1d759","source":{"kind":"arxiv","id":"2011.04434","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.04434","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"arxiv_version","alias_value":"2011.04434v2","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.04434","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"pith_short_12","alias_value":"AKK4V6BLPGEY","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"pith_short_16","alias_value":"AKK4V6BLPGEYKAEY","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"pith_short_8","alias_value":"AKK4V6BL","created_at":"2026-07-05T02:18:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:AKK4V6BLPGEYKAEYFFTCZBTXDQ","target":"record","payload":{"canonical_record":{"source":{"id":"2011.04434","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ex","submitted_at":"2020-11-09T13:53:29Z","cross_cats_sorted":[],"title_canon_sha256":"5664d7dc5bdc6c07c7756cb429bdb8446c5e19e3dad3ca3c10cdef3ec2c3b37d","abstract_canon_sha256":"f801a4ef05d5236e490b56dcc8459fdd1e2a0789afe92ce3af39001e34abd0fb"},"schema_version":"1.0"},"canonical_sha256":"0295caf82b798985009829662c86771c356599672c38443706b7832a8bc1d759","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:18:06.614656Z","signature_b64":"FvwIK4/D3JNU8pbwQZW5SrVz5HQhTydiHBDoV+BCblalJ0mQqx9Hb0/cBiQSo2QU74ef11E8+1H3OvNkyKcbAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0295caf82b798985009829662c86771c356599672c38443706b7832a8bc1d759","last_reissued_at":"2026-07-05T02:18:06.614172Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:18:06.614172Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2011.04434","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:18:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3Vb6KGEMo1ualloOHEVaHOSdkPxNfO0idEVa6ApZsvtUImHuxRiU15KDn3pzJbTvAnsAxr9MfgLfLV0wbnUuCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T21:36:21.146061Z"},"content_sha256":"67531c9cc881aa0335c64f3aaf3550cc01bcc0d6b66b460ef3176ba85b919ac5","schema_version":"1.0","event_id":"sha256:67531c9cc881aa0335c64f3aaf3550cc01bcc0d6b66b460ef3176ba85b919ac5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:AKK4V6BLPGEYKAEYFFTCZBTXDQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"hep-ex","authors_text":"Christian Veelken, Diana Rand, Laurits Tani, Mario Kadastik","submitted_at":"2020-11-09T13:53:29Z","abstract_excerpt":"The analysis of vast amounts of data constitutes a major challenge in modern high energy physics experiments. Machine learning (ML) methods, typically trained on simulated data, are often employed to facilitate this task. Several choices need to be made by the user when training the ML algorithm. In addition to deciding which ML algorithm to use and choosing suitable observables as inputs, users typically need to choose among a plethora of algorithm-specific parameters. We refer to parameters that need to be chosen by the user as hyperparameters. These are to be distinguished from parameters t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.04434","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/2011.04434/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:18:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SaEW0jrIo8/PZEh/iW6vn1v/6aVgqjbZWJrUVBVpR0aHoBSIwaQ7k6l7jfQohPMUAWnk3sVgyYmHUHiyT4XnAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T21:36:21.146961Z"},"content_sha256":"65f16aac5d0251bb2078b700f7ed3cc20ee428400d64eedc181036ab21c315fe","schema_version":"1.0","event_id":"sha256:65f16aac5d0251bb2078b700f7ed3cc20ee428400d64eedc181036ab21c315fe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AKK4V6BLPGEYKAEYFFTCZBTXDQ/bundle.json","state_url":"https://pith.science/pith/AKK4V6BLPGEYKAEYFFTCZBTXDQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AKK4V6BLPGEYKAEYFFTCZBTXDQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T21:36:21Z","links":{"resolver":"https://pith.science/pith/AKK4V6BLPGEYKAEYFFTCZBTXDQ","bundle":"https://pith.science/pith/AKK4V6BLPGEYKAEYFFTCZBTXDQ/bundle.json","state":"https://pith.science/pith/AKK4V6BLPGEYKAEYFFTCZBTXDQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AKK4V6BLPGEYKAEYFFTCZBTXDQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:AKK4V6BLPGEYKAEYFFTCZBTXDQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f801a4ef05d5236e490b56dcc8459fdd1e2a0789afe92ce3af39001e34abd0fb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ex","submitted_at":"2020-11-09T13:53:29Z","title_canon_sha256":"5664d7dc5bdc6c07c7756cb429bdb8446c5e19e3dad3ca3c10cdef3ec2c3b37d"},"schema_version":"1.0","source":{"id":"2011.04434","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.04434","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"arxiv_version","alias_value":"2011.04434v2","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.04434","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"pith_short_12","alias_value":"AKK4V6BLPGEY","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"pith_short_16","alias_value":"AKK4V6BLPGEYKAEY","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"pith_short_8","alias_value":"AKK4V6BL","created_at":"2026-07-05T02:18:06Z"}],"graph_snapshots":[{"event_id":"sha256:65f16aac5d0251bb2078b700f7ed3cc20ee428400d64eedc181036ab21c315fe","target":"graph","created_at":"2026-07-05T02:18:06Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2011.04434/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The analysis of vast amounts of data constitutes a major challenge in modern high energy physics experiments. Machine learning (ML) methods, typically trained on simulated data, are often employed to facilitate this task. Several choices need to be made by the user when training the ML algorithm. In addition to deciding which ML algorithm to use and choosing suitable observables as inputs, users typically need to choose among a plethora of algorithm-specific parameters. We refer to parameters that need to be chosen by the user as hyperparameters. These are to be distinguished from parameters t","authors_text":"Christian Veelken, Diana Rand, Laurits Tani, Mario Kadastik","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ex","submitted_at":"2020-11-09T13:53:29Z","title":"Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.04434","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:67531c9cc881aa0335c64f3aaf3550cc01bcc0d6b66b460ef3176ba85b919ac5","target":"record","created_at":"2026-07-05T02:18:06Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f801a4ef05d5236e490b56dcc8459fdd1e2a0789afe92ce3af39001e34abd0fb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ex","submitted_at":"2020-11-09T13:53:29Z","title_canon_sha256":"5664d7dc5bdc6c07c7756cb429bdb8446c5e19e3dad3ca3c10cdef3ec2c3b37d"},"schema_version":"1.0","source":{"id":"2011.04434","kind":"arxiv","version":2}},"canonical_sha256":"0295caf82b798985009829662c86771c356599672c38443706b7832a8bc1d759","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0295caf82b798985009829662c86771c356599672c38443706b7832a8bc1d759","first_computed_at":"2026-07-05T02:18:06.614172Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:18:06.614172Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FvwIK4/D3JNU8pbwQZW5SrVz5HQhTydiHBDoV+BCblalJ0mQqx9Hb0/cBiQSo2QU74ef11E8+1H3OvNkyKcbAw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:18:06.614656Z","signed_message":"canonical_sha256_bytes"},"source_id":"2011.04434","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:67531c9cc881aa0335c64f3aaf3550cc01bcc0d6b66b460ef3176ba85b919ac5","sha256:65f16aac5d0251bb2078b700f7ed3cc20ee428400d64eedc181036ab21c315fe"],"state_sha256":"b6bfcdfcb21138e0857fc66c36329b84d78cdcb08157568df13978fc20ed7de8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zVxfRjvbCkJFWPKvYPJdcgzxjKhMj5dfWUbZZJCoH+IdoV+N85Yd/09TZ/ovxDUzj+Zc7GPDv6vFp2yf0Lo3Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T21:36:21.152385Z","bundle_sha256":"df50c0f9d37a9a7b9ce0fbaf6255af75f289d0afd930ffd69d5a08a04846b3de"}}