Deep neural networks for energy and position reconstruction in EXO-200
文献类型：期刊论文
| 作者 | Anton, G; Albert, JB; Dilling, J; Dolgolenko, A; Dolinski, MJ; Fairbank, W; Farine, J; Feyzbakhsh, S; Fierlinger, P; Fudenberg, D |
| 刊名 | JOURNAL OF INSTRUMENTATION
 |
| 出版日期 | 2018 |
| 卷号 | 13页码:P08023 |
| 关键词 | Analysis and statistical methods Double-beta decay detectors Pattern recognition cluster finding calibration and fitting methods Time projection chambers |
| ISSN号 | 1748-0221 |
| DOI | 10.1088/1748-0221/13/08/P08023 |
| 文献子类 | Article |
| 英文摘要 | We apply deep neural networks (DNN) to data from the EXO-200 experiment. In the studied cases, the DNN is able to reconstruct the relevant parameters - total energy and position - directly from raw digitized waveforms, with minimal exceptions. For the first time, the developed algorithms are evaluated on real detector calibration data. The accuracy of reconstruction either reaches or exceeds what was achieved by the conventional approaches developed by EXO-200 over the course of the experiment. Most existing DNN approaches to event reconstruction and classification in particle physics are trained on Monte Carlo simulated events. Such algorithms are inherently limited by the accuracy of the simulation. We describe a unique approach that, in an experiment such as EXO-200, allows to successfully perform certain reconstruction and analysis tasks by training the network on waveforms from experimental data, either reducing or eliminating the reliance on the Monte Carlo. |
| WOS研究方向 | Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000443201700003 |
| 源URL | [http://ir.ihep.ac.cn/handle/311005/286252]  |
| 专题 | 高能物理研究所_实验物理中心 高能物理研究所_粒子天体物理中心 |
| 作者单位 | 中国科学院高能物理研究所 |
| 推荐引用方式 GB/T 7714 | Anton, G,Albert, JB,Dilling, J,et al. Deep neural networks for energy and position reconstruction in EXO-200[J]. JOURNAL OF INSTRUMENTATION,2018,13:P08023. |
| APA | Anton, G.,Albert, JB.,Dilling, J.,Dolgolenko, A.,Dolinski, MJ.,...&Coon, M.(2018).Deep neural networks for energy and position reconstruction in EXO-200.JOURNAL OF INSTRUMENTATION,13,P08023. |
| MLA | Anton, G,et al."Deep neural networks for energy and position reconstruction in EXO-200".JOURNAL OF INSTRUMENTATION 13(2018):P08023. |
入库方式： OAI收割
来源：高能物理研究所
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