{"paper":{"title":"A Neural Network approach to reconstructing SuperKEKB beam parameters from beamstrahlung","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["hep-ex"],"primary_cat":"physics.acc-ph","authors_text":"B.O. El Bashir, D. Liventsev, D. Ricalde-Herrmann, D. Rodriguez Perez, E. De La Cruz-Burelo, G. Bonvicini, G. Tejeda-Mu\\~noz, H. Farhat, I. Dom\\'inguez, J. Flanagan, K. Kanazawa, K. Kumara, M. Tobiyama I. Heredia de la Cruz, N.A. Althubiti, P.L.M. Podesta-Lerma, R.Ayad, R. Gillard, S. Di Carlo, S. Izaguirre Gamez","submitted_at":"2022-06-23T13:55:25Z","abstract_excerpt":"This work shows how it is possible to reconstruct SuperKEKB's beam parameters using a Neural Network with beamstrahlung signal from the Large Angle Beamstrahlung Monitor (LABM) as input. We describe the device, the model, and discuss the results."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.11709","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/2206.11709/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"}