Findings from Tsinghua University in the Area of Machine Learning Described (Mac hine Learning-based Discrimination of Bulk and Surface Events of Germanium Detec tors for Light Dark Matter Detection)
Findings from Tsinghua University in the Area of Machine Learning Described (Mac hine Learning-based Discrimination of Bulk and Surface Events of Germanium Detec tors for Light Dark Matter Detection)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – A new study on Machine Learning is now available. According to news originating from Beijing, People’s Republic of China, by News Rx correspondents, research stated, “Surface events that exhibit incomplete char ge collection are an essential background source in the light dark matter detect ion experiments with p -type point -contact germanium detectors. We propose a ma chine learningbased algorithm to identify bulk and surface events according to t heir pulse shape features.” Financial supporters for this research include National Key Research and De-velo pment Program of China, National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Tsinghua University , “We construct the training and test set with part of the -source calibration d ata and use the rising edge of the waveform as the model input. This method is v erified with the test set and another part of the -source calibration data. Resu lts show that this method performs well on both datasets, and presents robustnes s against the bulk events’ proportion and the dataset size. Compared with the pr evious approach, the uncertainty is reduced by 16% near the energy threshold on the physics data of CDEX-1B.”
Key words
Beijing/People’s Republic of China/Asi a/Cyborgs/Dark Matter/Emerging Technologies/Machine Learning/Physics/Tsing hua University
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