{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:HIHO2YGBKV4ZRTDDHB43KVU5ES","short_pith_number":"pith:HIHO2YGB","canonical_record":{"source":{"id":"2205.07953","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-16T19:29:37Z","cross_cats_sorted":["nucl-th"],"title_canon_sha256":"70bd2c1d509765fd7dd00bf443663e1ab3917e283eab4484446a20c876009d19","abstract_canon_sha256":"3a21f61862dbbcad1a6b5030a0f6a66ba0718923b92dd360aafbe6b82bdab56f"},"schema_version":"1.0"},"canonical_sha256":"3a0eed60c1557998cc633879b5569d248548071cff3ff94ba63e92b7549e8841","source":{"kind":"arxiv","id":"2205.07953","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.07953","created_at":"2026-07-05T05:01:39Z"},{"alias_kind":"arxiv_version","alias_value":"2205.07953v2","created_at":"2026-07-05T05:01:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.07953","created_at":"2026-07-05T05:01:39Z"},{"alias_kind":"pith_short_12","alias_value":"HIHO2YGBKV4Z","created_at":"2026-07-05T05:01:39Z"},{"alias_kind":"pith_short_16","alias_value":"HIHO2YGBKV4ZRTDD","created_at":"2026-07-05T05:01:39Z"},{"alias_kind":"pith_short_8","alias_value":"HIHO2YGB","created_at":"2026-07-05T05:01:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:HIHO2YGBKV4ZRTDDHB43KVU5ES","target":"record","payload":{"canonical_record":{"source":{"id":"2205.07953","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-16T19:29:37Z","cross_cats_sorted":["nucl-th"],"title_canon_sha256":"70bd2c1d509765fd7dd00bf443663e1ab3917e283eab4484446a20c876009d19","abstract_canon_sha256":"3a21f61862dbbcad1a6b5030a0f6a66ba0718923b92dd360aafbe6b82bdab56f"},"schema_version":"1.0"},"canonical_sha256":"3a0eed60c1557998cc633879b5569d248548071cff3ff94ba63e92b7549e8841","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:01:39.338390Z","signature_b64":"qAE70biRQV4VjmlG1Dn7R/7EbK5X6Psep1PZ1PUeO8bda6YScO6djxT6afunsdWiLvrcBKwNBQFEhizqltajCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a0eed60c1557998cc633879b5569d248548071cff3ff94ba63e92b7549e8841","last_reissued_at":"2026-07-05T05:01:39.337952Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:01:39.337952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.07953","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":"2026-07-05T05:01:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"amW5zq+SzWRV8GgT5k9lwlsWaGWbI7tldb7SUSJJLaweK565i3yCUq5/U/QNiwRQZxwkk0Ty0jGkvV9GuxXQDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T05:32:13.512532Z"},"content_sha256":"f59d00d04c51f64827bd8eb22f94e13a4945f6a19b8867c1b549506b45c241a1","schema_version":"1.0","event_id":"sha256:f59d00d04c51f64827bd8eb22f94e13a4945f6a19b8867c1b549506b45c241a1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:HIHO2YGBKV4ZRTDDHB43KVU5ES","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Application of multilayer perceptron with data augmentation in nuclear physics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["nucl-th"],"primary_cat":"cs.LG","authors_text":"Derya Soydaner, Esra Y\\\"uksel, H\\\"useyin Bahtiyar","submitted_at":"2022-05-16T19:29:37Z","abstract_excerpt":"Neural networks have become popular in many fields of science since they serve as promising, reliable and powerful tools. In this work, we study the effect of data augmentation on the predictive power of neural network models for nuclear physics data. We present two different data augmentation techniques, and we conduct a detailed analysis in terms of different depths, optimizers, activation functions and random seed values to show the success and robustness of the model. Using the experimental uncertainties for data augmentation for the first time, the size of the training data set is artific"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.07953","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/2205.07953/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":"2026-07-05T05:01:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FiLYt/deBc9j/BxvqlkjE2y6jpREb2A8pfmvR3ORcspixZbkuOPJiSL2HzXUjflB1yk78Q6qPDBTN4iAnZzkAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T05:32:13.513117Z"},"content_sha256":"b19ce2068adf2c9dc3e120428fba653145049ed73529f10f315791ceb181f0da","schema_version":"1.0","event_id":"sha256:b19ce2068adf2c9dc3e120428fba653145049ed73529f10f315791ceb181f0da"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HIHO2YGBKV4ZRTDDHB43KVU5ES/bundle.json","state_url":"https://pith.science/pith/HIHO2YGBKV4ZRTDDHB43KVU5ES/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HIHO2YGBKV4ZRTDDHB43KVU5ES/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":"2026-08-06T05:32:13Z","links":{"resolver":"https://pith.science/pith/HIHO2YGBKV4ZRTDDHB43KVU5ES","bundle":"https://pith.science/pith/HIHO2YGBKV4ZRTDDHB43KVU5ES/bundle.json","state":"https://pith.science/pith/HIHO2YGBKV4ZRTDDHB43KVU5ES/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HIHO2YGBKV4ZRTDDHB43KVU5ES/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:HIHO2YGBKV4ZRTDDHB43KVU5ES","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":"3a21f61862dbbcad1a6b5030a0f6a66ba0718923b92dd360aafbe6b82bdab56f","cross_cats_sorted":["nucl-th"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-16T19:29:37Z","title_canon_sha256":"70bd2c1d509765fd7dd00bf443663e1ab3917e283eab4484446a20c876009d19"},"schema_version":"1.0","source":{"id":"2205.07953","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.07953","created_at":"2026-07-05T05:01:39Z"},{"alias_kind":"arxiv_version","alias_value":"2205.07953v2","created_at":"2026-07-05T05:01:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.07953","created_at":"2026-07-05T05:01:39Z"},{"alias_kind":"pith_short_12","alias_value":"HIHO2YGBKV4Z","created_at":"2026-07-05T05:01:39Z"},{"alias_kind":"pith_short_16","alias_value":"HIHO2YGBKV4ZRTDD","created_at":"2026-07-05T05:01:39Z"},{"alias_kind":"pith_short_8","alias_value":"HIHO2YGB","created_at":"2026-07-05T05:01:39Z"}],"graph_snapshots":[{"event_id":"sha256:b19ce2068adf2c9dc3e120428fba653145049ed73529f10f315791ceb181f0da","target":"graph","created_at":"2026-07-05T05:01:39Z","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/2205.07953/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neural networks have become popular in many fields of science since they serve as promising, reliable and powerful tools. In this work, we study the effect of data augmentation on the predictive power of neural network models for nuclear physics data. We present two different data augmentation techniques, and we conduct a detailed analysis in terms of different depths, optimizers, activation functions and random seed values to show the success and robustness of the model. Using the experimental uncertainties for data augmentation for the first time, the size of the training data set is artific","authors_text":"Derya Soydaner, Esra Y\\\"uksel, H\\\"useyin Bahtiyar","cross_cats":["nucl-th"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-16T19:29:37Z","title":"Application of multilayer perceptron with data augmentation in nuclear physics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.07953","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:f59d00d04c51f64827bd8eb22f94e13a4945f6a19b8867c1b549506b45c241a1","target":"record","created_at":"2026-07-05T05:01:39Z","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":"3a21f61862dbbcad1a6b5030a0f6a66ba0718923b92dd360aafbe6b82bdab56f","cross_cats_sorted":["nucl-th"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-16T19:29:37Z","title_canon_sha256":"70bd2c1d509765fd7dd00bf443663e1ab3917e283eab4484446a20c876009d19"},"schema_version":"1.0","source":{"id":"2205.07953","kind":"arxiv","version":2}},"canonical_sha256":"3a0eed60c1557998cc633879b5569d248548071cff3ff94ba63e92b7549e8841","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3a0eed60c1557998cc633879b5569d248548071cff3ff94ba63e92b7549e8841","first_computed_at":"2026-07-05T05:01:39.337952Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:01:39.337952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qAE70biRQV4VjmlG1Dn7R/7EbK5X6Psep1PZ1PUeO8bda6YScO6djxT6afunsdWiLvrcBKwNBQFEhizqltajCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:01:39.338390Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.07953","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f59d00d04c51f64827bd8eb22f94e13a4945f6a19b8867c1b549506b45c241a1","sha256:b19ce2068adf2c9dc3e120428fba653145049ed73529f10f315791ceb181f0da"],"state_sha256":"02a8b6ca80a4776485e524825a980cf55913c565fa68ca32dfe0228cf3cca319"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uQie0ql5gO8atfTJMyFTLWrZyK+KcD29PhCNB4ERWhdJUIQZ23/xKufGLmszkKcOC30i9UPgJWorZy0DkquVBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T05:32:13.518784Z","bundle_sha256":"09a91041c613b2bf791200b8efb4cf3cd5ffa4cc08619c0582e334e17d712f4e"}}