{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:AJGE5F4WIAFU5AAHCX6W5WH6EP","short_pith_number":"pith:AJGE5F4W","schema_version":"1.0","canonical_sha256":"024c4e9796400b4e800715fd6ed8fe23f2205b00e211b965e8053e4628c9cf53","source":{"kind":"arxiv","id":"2207.06329","version":1},"attestation_state":"computed","paper":{"title":"GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter Response","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["hep-ex"],"primary_cat":"physics.data-an","authors_text":"Alexander Rogachev, Fedor Ratnikov","submitted_at":"2022-07-13T16:35:30Z","abstract_excerpt":"High energy physics experiments essentially rely on simulated data for physics analyses. However, running detailed simulation models requires a tremendous amount of computation resources. New approaches to speed up detector simulation are therefore needed. The generation of calorimeter responses is often the most expensive component of the simulation chain for HEP experiments. It was shown that deep learning techniques, especially Generative Adversarial Networks, may be used to reproduce the calorimeter response. However, those applications are challenging, as the generated responses need eval"},"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":"2207.06329","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.data-an","submitted_at":"2022-07-13T16:35:30Z","cross_cats_sorted":["hep-ex"],"title_canon_sha256":"c3604549530193ee0155cb31a7cda5091d02cead5c4d309f5e29e6f440bd423e","abstract_canon_sha256":"3545abf4730d486540768190a68fca5d8c185e21a13fa1276bd28fde873652d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:46:18.066020Z","signature_b64":"YaM4T0q1R4pcVQHP72J7JOJI1RR7hS5x5CS/0v11iyhiDqrVNT05jZxq+IfzkO/FgFeeJw8hUEWXBX2f9lsNBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"024c4e9796400b4e800715fd6ed8fe23f2205b00e211b965e8053e4628c9cf53","last_reissued_at":"2026-07-05T05:46:18.065596Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:46:18.065596Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter Response","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["hep-ex"],"primary_cat":"physics.data-an","authors_text":"Alexander Rogachev, Fedor Ratnikov","submitted_at":"2022-07-13T16:35:30Z","abstract_excerpt":"High energy physics experiments essentially rely on simulated data for physics analyses. However, running detailed simulation models requires a tremendous amount of computation resources. New approaches to speed up detector simulation are therefore needed. The generation of calorimeter responses is often the most expensive component of the simulation chain for HEP experiments. It was shown that deep learning techniques, especially Generative Adversarial Networks, may be used to reproduce the calorimeter response. However, those applications are challenging, as the generated responses need eval"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.06329","kind":"arxiv","version":1},"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/2207.06329/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2207.06329","created_at":"2026-07-05T05:46:18.065660+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.06329v1","created_at":"2026-07-05T05:46:18.065660+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.06329","created_at":"2026-07-05T05:46:18.065660+00:00"},{"alias_kind":"pith_short_12","alias_value":"AJGE5F4WIAFU","created_at":"2026-07-05T05:46:18.065660+00:00"},{"alias_kind":"pith_short_16","alias_value":"AJGE5F4WIAFU5AAH","created_at":"2026-07-05T05:46:18.065660+00:00"},{"alias_kind":"pith_short_8","alias_value":"AJGE5F4W","created_at":"2026-07-05T05:46:18.065660+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.07565","citing_title":"Ensemble Distributionally Robust Bayesian Optimisation with Continuous Context","ref_index":125,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AJGE5F4WIAFU5AAHCX6W5WH6EP","json":"https://pith.science/pith/AJGE5F4WIAFU5AAHCX6W5WH6EP.json","graph_json":"https://pith.science/api/pith-number/AJGE5F4WIAFU5AAHCX6W5WH6EP/graph.json","events_json":"https://pith.science/api/pith-number/AJGE5F4WIAFU5AAHCX6W5WH6EP/events.json","paper":"https://pith.science/paper/AJGE5F4W"},"agent_actions":{"view_html":"https://pith.science/pith/AJGE5F4WIAFU5AAHCX6W5WH6EP","download_json":"https://pith.science/pith/AJGE5F4WIAFU5AAHCX6W5WH6EP.json","view_paper":"https://pith.science/paper/AJGE5F4W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.06329&json=true","fetch_graph":"https://pith.science/api/pith-number/AJGE5F4WIAFU5AAHCX6W5WH6EP/graph.json","fetch_events":"https://pith.science/api/pith-number/AJGE5F4WIAFU5AAHCX6W5WH6EP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AJGE5F4WIAFU5AAHCX6W5WH6EP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AJGE5F4WIAFU5AAHCX6W5WH6EP/action/storage_attestation","attest_author":"https://pith.science/pith/AJGE5F4WIAFU5AAHCX6W5WH6EP/action/author_attestation","sign_citation":"https://pith.science/pith/AJGE5F4WIAFU5AAHCX6W5WH6EP/action/citation_signature","submit_replication":"https://pith.science/pith/AJGE5F4WIAFU5AAHCX6W5WH6EP/action/replication_record"}},"created_at":"2026-07-05T05:46:18.065660+00:00","updated_at":"2026-07-05T05:46:18.065660+00:00"}