{"paper":{"title":"Particle Identification at VAMOS++ with Machine Learning Techniques","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["nucl-ex"],"primary_cat":"physics.ins-det","authors_text":"A. Lemasson, A. N. Andreyev, A. Navin, A. Utepov, C. Fourgeres, C. Kim, D. Ackermann, D. Ramos, D. Treasa, F. Didierjean, F. Recchia, G. de Angelis, G. de France, G. Fremont, G. M. Gu, G. Mukherjee, H. Miyatake, I. Tsekhanovich, J. C. Thomas, J. Dudouet, J. Goupil, J. Ha, J. Park, K. Chae, K. I. Hahn, K. Rezynkina, M. J. Kim, M. Mukai, M. Rejmund, M. Rosenbusch, P. John, P. Marini, P. Schury, R. Banik, R. M. P\\'erez Vidal, S. Bae, S. Bhattacharya, S. Bhattacharyya, S. Choi, S. Jeong, S. Kim, T. Niwase, W. Korten, Y. Cho, Y. Hirayama, Y. H. Kim, Y. Son, Y. X. Watanabe","submitted_at":"2023-11-13T06:32:32Z","abstract_excerpt":"Multi-nucleon transfer reaction between 136Xe beam and 198Pt target was performed using the VAMOS++ spectrometer at GANIL to study the structure of n-rich nuclei around N=126. Unambiguous charge state identification was obtained by combining two supervised machine learning methods, deep neural network (DNN) and positional correction using a gradient-boosting decision tree (GBDT). The new method reduced the complexity of the kinetic energy calibration and outperformed the conventional method, improving the charge state resolution by 8%"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.07103","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/2311.07103/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"}