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A feasibility study of multi-electrode high-purity germanium detector for Ge-76 neutrinoless double beta decay searching
Instrumentation and Detectors · Submitted 11 Nov 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This study demonstrated that it is feasible to realize single- and double-electron discrimination on multi-electrode HPGe detectors using an FC neural network and LGB model.
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The abstract, sentence by sentence
Experiments to search for neutrinoless double-beta (0{\nu}\b{eta}\b{eta}) decay of 76Ge using a high-purity germanium (HPGe) detector rely heavily on background suppression technologies to enhance their sensitivities. In this work, we proposed a pulse-shape analysis method based on a neural network (NN) and a light gradient boosting machine (lightGBM; LGB) to discriminate single-electron (background) and double-electrons (0{\nu}\b{eta}\b{eta} signal) events in a multi-electrode HPGe detector. In this paper, we describe a multi-electrode HPGe detector system, a data-processing system, and pulse-shape simulation procedures. We built a fully connected (FC) neural network and an LGB model to classify the single- and double-electron events. The FC network is trained with simulated single- and double-electron-induced pulses and tested in an independent dataset generated by the pulse-shape simulation. The discrimination efficiency of the FC neural network in the test set for the 0{\nu}\b{eta}\b{eta} double-electron events signal was 77.4%, the precision was 57.7%, and the training time was 430 min. The discrimination efficiency of LGB model was 73.1%, the precision was 64.0%, and the training time was 1.5 min. This study demonstrated that it is feasible to realize single- and double-electron discrimination on multi-electrode HPGe detectors using an FC neural network and LGB model. These results can be used as a reference for future 76Ge 0{\nu}\b{eta}\b{eta} experiments.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Experiments to search for neutrinoless double-beta (0{\nu}\b{eta}\b{eta}) decay of 76Ge using a high-purity germanium (HPGe) detector rely heavily on background suppression technologies to enhance their sensitivities. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | In this work, we proposed a pulse-shape analysis method based on a neural network (NN) and a light gradient boosting machine (lightGBM; LGB) to discriminate single-electron (background) and double-electrons (0{\nu}\b{eta}\b{eta} signal) events in a multi-electrode HPGe detector. | contribution 0.81 | 0.87 | 0.06 | asserted 0.72 |
| s2 | In this paper, we describe a multi-electrode HPGe detector system, a data-processing system, and pulse-shape simulation procedures. | method 0.64 | 0.84 | 0.02 | asserted 0.93 |
| s3 | We built a fully connected (FC) neural network and an LGB model to classify the single- and double-electron events. | method 0.95 | 0.96 | 0.03 | asserted 1.00 |
| s4 | The FC network is trained with simulated single- and double-electron-induced pulses and tested in an independent dataset generated by the pulse-shape simulation. | method 1.00 | 0.94 | 0.02 | asserted 1.00 |
| s5 | The discrimination efficiency of the FC neural network in the test set for the 0{\nu}\b{eta}\b{eta} double-electron events signal was 77.4%, the precision was 57.7%, and the training time was 430 min. | result 1.00 | 0.97 | 0.05 | asserted 1.00 |
| s6 | The discrimination efficiency of LGB model was 73.1%, the precision was 64.0%, and the training time was 1.5 min. | result 1.00 | 0.96 | 0.04 | asserted 1.00 |
| s7 | This study demonstrated that it is feasible to realize single- and double-electron discrimination on multi-electrode HPGe detectors using an FC neural network and LGB model. | interpretation 0.89 | 0.92 | 0.93 | asserted 0.99 |
| s8 | These results can be used as a reference for future 76Ge 0{\nu}\b{eta}\b{eta} experiments. | future work 0.78 | 0.24 | 0.06 | hedged possibility 0.58 |
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The paper
Original abstract
Experiments to search for neutrinoless double-beta (0{\nu}\b{eta}\b{eta}) decay of 76Ge using a high-purity germanium (HPGe) detector rely heavily on background suppression technologies to enhance their sensitivities. In this work, we proposed a pulse-shape analysis method based on a neural network (NN) and a light gradient boosting machine (lightGBM; LGB) to discriminate single-electron (background) and double-electrons (0{\nu}\b{eta}\b{eta} signal) events in a multi-electrode HPGe detector. In this paper, we describe a multi-electrode HPGe detector system, a data-processing system, and pulse-shape simulation procedures. We built a fully connected (FC) neural network and an LGB model to classify the single- and double-electron events. The FC network is trained with simulated single- and double-electron-induced pulses and tested in an independent dataset generated by the pulse-shape simulation. The discrimination efficiency of the FC neural network in the test set for the 0{\nu}\b{eta}\b{eta} double-electron events signal was 77.4%, the precision was 57.7%, and the training time was 430 min. The discrimination efficiency of LGB model was 73.1%, the precision was 64.0%, and the training time was 1.5 min. This study demonstrated that it is feasible to realize single- and double-electron discrimination on multi-electrode HPGe detectors using an FC neural network and LGB model. These results can be used as a reference for future 76Ge 0{\nu}\b{eta}\b{eta} experiments.
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