EXO-200 Public Data Release for AI/ML Applications
S. Al Kharusi, G. Anton, I. Badhrees, P. S. Barbeau, V. Belov, T. Bhatta, M. Breidenbach, T. Brunner, G. F. Cao, W. R. Cen, C. Chambers, B. Cleveland, M. Coon, A. Craycraft, T. Daniels, L. Darroch, S. J. Daugherty, J. Davis, S. Delaquis, A. Der Mesrobian-Kabakian, R. DeVoe, A. Dolgolenko, M. J. Dolinski, J. Echevers, B. Eckert, W. Fairbank, D. Fairbank, J. Farine, S. Feyzbakhsh, P. Fierlinger, Y. S. Fu, D. Fudenberg, P. Gautam, R. Gornea, G. Gratta, C. Hall, E. V. Hansen, J. Hoessl, P. Hufschmidt, M. Hughes, A. Iverson, A. Jamil, C. Jessiman, M. J. Jewell, A. Johnson, A. Karelin, L. J. Kaufman, T. Koffas, R. Krücken, A. Kuchenkov, K. S. Kumar, Y. Lan, A. Larson, B. G. Lenardo, D. S. Leonard, G. S. Li, S. Li, Z. Li, C. Licciardi, Y. H. Lin, R. MacLellan, T. McElroy, T. Michel, B. Mong, D. C. Moore, K. Murray, O. Njoya, O. Nusair, A. Odian, I. Ostrovskiy, H. Peltz Smalley, A. Perna, A. Piepke, A. Pocar, F. Retière, A. L. Robinson, P. C. Rowson, S. Schmidt, D. Sinclair, K. Skarpaas, A. K. Soma, V. Stekhanov, M. Tarka, S. Thibado, J. Todd, T. Tolba, T. I. Totev, R. Tsang, B. Veenstra, V. Veeraraghavan, P. Vogel, J. -L. Vuilleumier, M. Wagenpfeil, J. Watkins, M. Weber, L. J. Wen, U. Wichoski, G. Wrede, S. X. Wu, Q. Xia, H. Xu, D. R. Yahne, L. Yang, Y. -R. Yen, O. Ya. Zeldovich, T. Ziegler
Abstract
We present a public release of a subset of calibration data from the EXO-200 experiment, a liquid xenon time projection chamber designed to search for neutrinoless double-beta decay in 136Xe. The released dataset consists of approximately one million events collected during 228Th calibration campaigns performed near the end of Phase-II operations between February 26 and March 2, 2018. For each event, the dataset includes raw detector waveforms from the U-wire, V-wire, and avalanche photodiode (APD) readout channels together with reconstructed event quantities including charge energy, light energy, rotated energy, and charge-cluster information. The dataset is distributed in HDF5 format and is intended to support data preservation, educational activities, and the development of modern machine-learning techniques for rare-event physics. This paper describes the detector, dataset contents, file structure, and public access mechanism.
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