{"paper":{"title":"Deep Learning Based Event Reconstruction for Cyclotron Radiation Emission Spectroscopy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["nucl-ex"],"primary_cat":"physics.ins-det","authors_text":"A. Ashtari Esfahani, A. B. Telles, A. Lindman, A. Marsteller, A. M. Jones, A. Ziegler, B. A. VanDevender, B. Monreal, C. Claessens, C. Matth\\'e, E. C. Morrison, E. Novitski, E. Zayas, F. Thomas, J. A. Formaggio, J. A. Nikkel, J. Hartse, J. I. Pe\\~na, J. K. Gaison, J. Stachurska, K. Kazkaz, K. M. Heeger, L. A. Thorne, L. de Viveiros, L. Gladstone, L. Salda\\~na, L. Tvrznikova, M. C. Carmona-Benitez, M. Fertl, M. Grando, M. Guigue, M. Li, M. Schram, M. Thomas, N. Buzinsky, N. S. Oblath, P. L. Slocum, P. T. Surukuchi, R. Cervantes, R. G. H. Robertson, R. Mohiuddin, R. Mueller, R. Reimann, S. B\\\"oser, T. E. Weiss, T. Th\\\"ummler, T. Wendler, W. Pettus, W. Van De Pontseele, X. Huyan, Y.-H. Sun","submitted_at":"2024-01-05T15:55:27Z","abstract_excerpt":"The objective of the Cyclotron Radiation Emission Spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time-frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.13256","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/2402.13256/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"}