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Majorana Demonstrator Data Release for AI/ML Applications

Authors: Majorana Collaboration;

Majorana Demonstrator Data Release for AI/ML Applications

Abstract

If you used this dataset in your work, the collaboration kindly requests that you cite the two following manuscripts: ==================================================================================== @article{PhysRevLett.130.062501, title = {Final Result of the Majorana Demonstrator's Search for Neutrinoless Double-$\ensuremath{\beta}$ Decay in $^{76}\mathrm{Ge}$}, author = {Arnquist, I. J. and Avignone, F. T. and Barabash, A. S. and Barton, C. J. and Barton, P. J. and Bhimani, K. H. and Blalock, E. and Bos, B. and Busch, M. and Buuck, M. and Caldwell, T. S. and Chan, Y-D. and Christofferson, C. D. and Chu, P.-H. and Clark, M. L. and Cuesta, C. and Detwiler, J. A. and Efremenko, Yu. and Ejiri, H. and Elliott, S. R. and Giovanetti, G. K. and Green, M. P. and Gruszko, J. and Guinn, I. S. and Guiseppe, V. E. and Haufe, C. R. and Henning, R. and Hervas Aguilar, D. and Hoppe, E. W. and Hostiuc, A. and Kidd, M. F. and Kim, I. and Kouzes, R. T. and Lannen V., T. E. and Li, A. and Lopez, A. M. and L\'opez-Casta\~no, J. M. and Martin, E. L. and Martin, R. D. and Massarczyk, R. and Meijer, S. J. and Mertens, S. and Oli, T. K. and Othman, G. and Paudel, L. S. and Pettus, W. and Poon, A. W. P. and Radford, D. C. and Reine, A. L. and Rielage, K. and Ruof, N. W. and Schaper, D. C. and Tedeschi, D. and Varner, R. L. and Vasilyev, S. and Wilkerson, J. F. and Wiseman, C. and Xu, W. and Yu, C.-H. and Zhu, B. X.}, collaboration = {Majorana Collaboration}, journal = {Phys. Rev. Lett.}, volume = {130}, issue = {6}, pages = {062501}, numpages = {8}, year = {2023}, month = {Feb}, publisher = {American Physical Society}, doi = {10.1103/PhysRevLett.130.062501}, url = {https://link.aps.org/doi/10.1103/PhysRevLett.130.062501} } ==================================================================================== @misc{arnquist2023majorana, title={Majorana Demonstrator Data Release for AI/ML Applications}, author={I. J. Arnquist and F. T. Avignone III au2 and A. S. Barabash and C. J. Barton and K. H. Bhimani and E. Blalock and B. Bos and M. Busch and M. Buuck and T. S. Caldwell and Y. -D. Chan and C. D. Christofferson and P. -H. Chu and M. L. Clark and C. Cuesta and J. A. Detwiler and Yu. Efremenko and H. Ejiri and S. R. Elliott and N. Fuad and G. K. Giovanetti and M. P. Green and J. Gruszko and I. S. Guinn and V. E. Guiseppe and C. R. Haufe and R. Henning and D. Hervas Aguilar and E. W. Hoppe and A. Hostiuc and M. F. Kidd and I. Kim and R. T. Kouzes and T. E. Lannen V au2 and A. Li and J. M. Lopez-Castano and R. D. Martin and R. Massarczyk and S. J. Meijer and S. Mertens and T. K. Oli and L. S. Paudel and W. Pettus and A. W. P. Poon and B. Quenallata and D. C. Radford and A. L. Reine and K. Rielage and N. W. Ruof and D. C. Schaper and S. J. Schleich and D. Tedeschi and R. L. Varner and S. Vasilyev and S. L. Watkins and J. F. Wilkerson and C. Wiseman and W. Xu and C. -H. Yu and B. X. Zhu}, year={2023}, eprint={2308.10856}, archivePrefix={arXiv}, primaryClass={cs.LG} } ====================================================================================

The enclosed data release consists of a subset of the 228Th calibration data from the Majorana Demonstrator experiment. Each Majorana event is accompanied by raw Germanium detector waveforms, pulse shape discrimina- tion cuts, and calibrated final energies, all shared in an HDF5 file format along with relevant metadata. This release is specifically designed to support the training and testing of Artificial Intelligence and Machine Learning (AI/ML) algorithms upon our data. Please read the following ArXiV posting before using this dataset: https://arxiv.org/abs/2308.10856. Please direct questions about the material provided within this release to liaobo77@ucsd.edu (A. Li).

Keywords

Pulse Shape Discrimination, Neutrino Experiment, Energy Reconstruction, Time Series, High Purity Germanium Detector, Supervised Learning

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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