{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:C2VB3GNEV4WEKTBM5HIKNEMKUJ","short_pith_number":"pith:C2VB3GNE","canonical_record":{"source":{"id":"1701.05927","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-01-20T21:09:06Z","cross_cats_sorted":["hep-ex","physics.data-an"],"title_canon_sha256":"8ae85f379dda9ec429421eabcf01b6ef146d7a785560e4824270fe20e1ca0355","abstract_canon_sha256":"fc734cdd650806059411a10b47f64578293336538de0e75f562f431490d62d11"},"schema_version":"1.0"},"canonical_sha256":"16aa1d99a4af2c454c2ce9d0a6918aa2663aafa400addfe7593aac0a7c074ced","source":{"kind":"arxiv","id":"1701.05927","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.05927","created_at":"2026-05-18T00:31:22Z"},{"alias_kind":"arxiv_version","alias_value":"1701.05927v2","created_at":"2026-05-18T00:31:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.05927","created_at":"2026-05-18T00:31:22Z"},{"alias_kind":"pith_short_12","alias_value":"C2VB3GNEV4WE","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"C2VB3GNEV4WEKTBM","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"C2VB3GNE","created_at":"2026-05-18T12:31:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:C2VB3GNEV4WEKTBM5HIKNEMKUJ","target":"record","payload":{"canonical_record":{"source":{"id":"1701.05927","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-01-20T21:09:06Z","cross_cats_sorted":["hep-ex","physics.data-an"],"title_canon_sha256":"8ae85f379dda9ec429421eabcf01b6ef146d7a785560e4824270fe20e1ca0355","abstract_canon_sha256":"fc734cdd650806059411a10b47f64578293336538de0e75f562f431490d62d11"},"schema_version":"1.0"},"canonical_sha256":"16aa1d99a4af2c454c2ce9d0a6918aa2663aafa400addfe7593aac0a7c074ced","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:22.162485Z","signature_b64":"9hLy79Y2bbcQyvHaqg9DmgwgP793w/v/wZW153WHTayGbvGUTEQvfxDt/RxpjmiRJlrpDG5neigaOfhZcKEfAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"16aa1d99a4af2c454c2ce9d0a6918aa2663aafa400addfe7593aac0a7c074ced","last_reissued_at":"2026-05-18T00:31:22.162000Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:22.162000Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1701.05927","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":"2026-05-18T00:31:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hxtIGr5hca9XYFPK0MN/mMCKJ5JkrJGYKQAn418ga49bpBYk8y5Tej3yOOuoDTCPnfYnenMnWsnC3t7BMGhxBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T08:46:12.504301Z"},"content_sha256":"c489d04155a14e3f283cf9e99a3bea0ae4de1cabc811f1da3d82741b1badb0e5","schema_version":"1.0","event_id":"sha256:c489d04155a14e3f283cf9e99a3bea0ae4de1cabc811f1da3d82741b1badb0e5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:C2VB3GNEV4WEKTBM5HIKNEMKUJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ex","physics.data-an"],"primary_cat":"stat.ML","authors_text":"Benjamin Nachman, Luke de Oliveira, Michela Paganini","submitted_at":"2017-01-20T21:09:06Z","abstract_excerpt":"We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images -- 2D representations of energy depositions from particles interacting with a calorimeter. We propose a simple architecture, the Location-Aware Generative Adversarial Network, that learns to produce realistic radiation patterns from simulated high energy particle collisions. The pixel intensities of GAN-generated images faithfully span over many ord"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.05927","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:31:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tq6WmIILqSSOxrYrP4pAx7gEs/aNIV4ivEw2lDK3qCkumA8OB/IPc80i546mGtEQnjcYwNU9jfQdVHhRxoPcCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T08:46:12.504985Z"},"content_sha256":"f9806ee6bb8b572f8e76105948f3a0f2da5329ccd1cd91d692ae61972f460601","schema_version":"1.0","event_id":"sha256:f9806ee6bb8b572f8e76105948f3a0f2da5329ccd1cd91d692ae61972f460601"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C2VB3GNEV4WEKTBM5HIKNEMKUJ/bundle.json","state_url":"https://pith.science/pith/C2VB3GNEV4WEKTBM5HIKNEMKUJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C2VB3GNEV4WEKTBM5HIKNEMKUJ/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":"2026-06-06T08:46:12Z","links":{"resolver":"https://pith.science/pith/C2VB3GNEV4WEKTBM5HIKNEMKUJ","bundle":"https://pith.science/pith/C2VB3GNEV4WEKTBM5HIKNEMKUJ/bundle.json","state":"https://pith.science/pith/C2VB3GNEV4WEKTBM5HIKNEMKUJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C2VB3GNEV4WEKTBM5HIKNEMKUJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:C2VB3GNEV4WEKTBM5HIKNEMKUJ","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":"fc734cdd650806059411a10b47f64578293336538de0e75f562f431490d62d11","cross_cats_sorted":["hep-ex","physics.data-an"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-01-20T21:09:06Z","title_canon_sha256":"8ae85f379dda9ec429421eabcf01b6ef146d7a785560e4824270fe20e1ca0355"},"schema_version":"1.0","source":{"id":"1701.05927","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.05927","created_at":"2026-05-18T00:31:22Z"},{"alias_kind":"arxiv_version","alias_value":"1701.05927v2","created_at":"2026-05-18T00:31:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.05927","created_at":"2026-05-18T00:31:22Z"},{"alias_kind":"pith_short_12","alias_value":"C2VB3GNEV4WE","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"C2VB3GNEV4WEKTBM","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"C2VB3GNE","created_at":"2026-05-18T12:31:08Z"}],"graph_snapshots":[{"event_id":"sha256:f9806ee6bb8b572f8e76105948f3a0f2da5329ccd1cd91d692ae61972f460601","target":"graph","created_at":"2026-05-18T00:31:22Z","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"},"paper":{"abstract_excerpt":"We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images -- 2D representations of energy depositions from particles interacting with a calorimeter. We propose a simple architecture, the Location-Aware Generative Adversarial Network, that learns to produce realistic radiation patterns from simulated high energy particle collisions. The pixel intensities of GAN-generated images faithfully span over many ord","authors_text":"Benjamin Nachman, Luke de Oliveira, Michela Paganini","cross_cats":["hep-ex","physics.data-an"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-01-20T21:09:06Z","title":"Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.05927","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:c489d04155a14e3f283cf9e99a3bea0ae4de1cabc811f1da3d82741b1badb0e5","target":"record","created_at":"2026-05-18T00:31:22Z","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":"fc734cdd650806059411a10b47f64578293336538de0e75f562f431490d62d11","cross_cats_sorted":["hep-ex","physics.data-an"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-01-20T21:09:06Z","title_canon_sha256":"8ae85f379dda9ec429421eabcf01b6ef146d7a785560e4824270fe20e1ca0355"},"schema_version":"1.0","source":{"id":"1701.05927","kind":"arxiv","version":2}},"canonical_sha256":"16aa1d99a4af2c454c2ce9d0a6918aa2663aafa400addfe7593aac0a7c074ced","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"16aa1d99a4af2c454c2ce9d0a6918aa2663aafa400addfe7593aac0a7c074ced","first_computed_at":"2026-05-18T00:31:22.162000Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:22.162000Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9hLy79Y2bbcQyvHaqg9DmgwgP793w/v/wZW153WHTayGbvGUTEQvfxDt/RxpjmiRJlrpDG5neigaOfhZcKEfAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:22.162485Z","signed_message":"canonical_sha256_bytes"},"source_id":"1701.05927","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c489d04155a14e3f283cf9e99a3bea0ae4de1cabc811f1da3d82741b1badb0e5","sha256:f9806ee6bb8b572f8e76105948f3a0f2da5329ccd1cd91d692ae61972f460601"],"state_sha256":"82fee2044d78c9823f96e4dbfd1e4b6ab49c0b7b0c347aac325f7252ac0c8ec4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gsnr1AokN2jugjGlvPfipuKEWmpBUqWAQjlL4JlMF01VubNr2x0nIswHxUmdMLQGlLKkuvPAmE14uOFJ391aDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T08:46:12.507966Z","bundle_sha256":"62850ec9a000134f16660f3c6d95c2472afc2297cb523b8b7d0f46f347973031"}}