A machine learning-based methodology for pulse classification in dual-phase xenon time projection chambers

Abstract

Machine learning techniques are now well established in experimental particle physics, allowing detector data to be analysed in new and unique ways. The identification of signals in particle observatories is an essential data processing task that can potentially be improved using such methods. This paper aims at exploring the benefits that a dedicated machine learning approach might provide to the classification of signals in dual-phase noble gas time projection chambers. A full methodology is presented, from exploratory data analysis using Gaussian mixture models and feature importance ranking to the construction of dedicated predictive models based on standard implementations of neural networks and random forests, validated using unlabelled simulated data from the LZ experiment as a proxy to real data. The global classification accuracy of the predictive models developed in this work is estimated to be >99.0%, which is an improvement over conventional algorithms tested with the same data. The results from the clustering analysis were also used to identify anomalies in the data caused by miscalculated signal properties, showing that this methodology can also be used for data monitoring.

arXiv comment17 pages, 9 figures

References in corpus (38)

• Scikit-learn: Machine Learning in Python
• Machine learning and the physical sciences
• Dark Matter Search Results from a One Tonne$\times$Year Exposure of XENON1T
• First results from the LUX dark matter experiment at the Sanford Underground Research Facility
• Results from a search for dark matter in the complete LUX exposure
• Low-Mass Dark Matter Search with the DarkSide-50 Experiment
• DARWIN: towards the ultimate dark matter detector
• Classification without labels: Learning from mixed samples in high energy physics
• Projected WIMP sensitivity of the LUX-ZEPLIN (LZ) dark matter experiment
• The XENON1T Dark Matter Experiment
• Liquid noble gas detectors for low energy particle physics
• Projected WIMP Sensitivity of the XENONnT Dark Matter Experiment
• The LUX-ZEPLIN (LZ) Experiment
• Results of Dark Matter Search using the Full PandaX-II Exposure
• A deep learning approach to cosmological dark energy models
• The ZEPLIN-III dark matter detector: instrument design, manufacture and commissioning
• Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
• Constraints on low-mass WIMPs from the EDELWEISS-III dark matter search
• Improved WIMP-search reach of the CDMS II germanium data
• Investigation of background electron emission in the LUX detector
• Gaseous and dual-phase time projection chambers for imaging rare processes
• Two distinct components of the delayed single electron background signals in liquid xenon emission detectors
• Identifying WIMP dark matter from particle and astroparticle data
• In-situ characterization of the Hamamatsu R5912-HQE photomultiplier tubes used in the DEAP-3600 experiment
• Discrimination of electronic recoils from nuclear recoils in two-phase xenon time projection chambers
• Calibration, event reconstruction, data analysis and limits calculation for the LUX dark matter experiment
• Deep Learning the Morphology of Dark Matter Substructure
• Response of photomultiplier tubes to xenon scintillation light
• Simulations of Events for the LUX-ZEPLIN (LZ) Dark Matter Experiment
• Deep Neural Networks for Energy and Position Reconstruction in EXO-200
• Deep learning based pulse shape discrimination for germanium detectors
• Convolutional Neural Networks for Direct Detection of Dark Matter
• Improving sensitivity to low-mass dark matter in LUX using a novel electrode background mitigation technique
• Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
• LHC signals of a heavy doublet Higgs as dark matter portal: cut-based approach and improvement with gradient boosting and neural networks
• Using Machine Learning to disentangle LHC signatures of Dark Matter candidates
• Developing a Bubble Chamber Particle Discriminator Using Semi-Supervised Learning
• The Impact of Photon Flight Path on S1 Pulse Shape Analysis in Liquid Xenon Two-phase Detectors

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