64 research outputs found
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks
Experiments in particle physics produce enormous quantities of data that must
be analyzed and interpreted by teams of physicists. This analysis is often
exploratory, where scientists are unable to enumerate the possible types of
signal prior to performing the experiment. Thus, tools for summarizing,
clustering, visualizing and classifying high-dimensional data are essential. In
this work, we show that meaningful physical content can be revealed by
transforming the raw data into a learned high-level representation using deep
neural networks, with measurements taken at the Daya Bay Neutrino Experiment as
a case study. We further show how convolutional deep neural networks can
provide an effective classification filter with greater than 97% accuracy
across different classes of physics events, significantly better than other
machine learning approaches
Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector
We provide a fast approach incorporating the usage of deep learning for
evaluating the effects of photon sensors in an antineutrino detector on the
event reconstruction performance therein. This work is an attempt to harness
the power of deep learning for detector designing and upgrade planning. Using
the Daya Bay detector as a benchmark case and the vertex reconstruction
performance as the objective for the deep neural network, we find that the
photomultiplier tubes (PMTs) have different relative importance to the vertex
reconstruction. More importantly, the vertex position resolutions for the Daya
Bay detector follow approximately a multi-exponential relationship with respect
to the number of PMTs and hence, the coverage. This could also assist in
deciding on the merits of installing additional PMTs for future detector plans.
The approach could easily be used with other objectives in place of vertex
reconstruction
Machine learning at the energy and intensity frontiers of particle physics
Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics
Neutrino Characterisation using Convolutional Neural Networks in CHIPS water Cherenkov detectors
This work presents a novel approach to water Cherenkov neutrino detector
event reconstruction and classification. Three forms of a Convolutional Neural
Network have been trained to reject cosmic muon events, classify beam events,
and estimate neutrino energies, using only a slightly modified version of the
raw detector event as input. When evaluated on a realistic selection of
simulated CHIPS-5kton prototype detector events, this new approach
significantly increases performance over the standard likelihood-based
reconstruction and simple neural network classification.Comment: 45 pages, 22 figures, 5 tables, to be submitted to Nuclear
Instruments and Methods in Physics Research - section
Neutrino characterisation using convolutional neural networks in CHIPS water Cherenkov detectors
This work presents a novel approach to water Cherenkov neutrino detector event reconstruction and classification. Three forms of a Convolutional Neural Network have been trained to reject cosmic muon events, classify beam events, and estimate neutrino energies, using only a slightly modified version of the raw detector event as input. When evaluated on a realistic selection of simulated CHIPS-5kton prototype detector events, this new approach significantly increases performance over the standard likelihood-based reconstruction and simple neural network classification
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Pulse shape discrimination and exploration of scintillation signals using convolutional neural networks
Abstract: We demonstrate the use of a convolutional neural network to perform neutron-gamma pulse shape discrimination, where the only inputs to the network are the raw digitised silicon photomultiplier signals from a dual scintillator detector element made of 6Li F:ZnS(Ag) scintillator and PVT plastic. A realistic labelled dataset was created to train the network by exposing the detector to an AmBe source, and a data-driven method utilising a separate photomultiplier tube was used to assign labels to the recorded signals. This approach is compared to the charge integration and continuous wavelet transform methods and a simpler artificial neural net. It is found to provide superior levels of discrimination, achieving an area under the curve of 0.996 ± 0.003. We find that the neural network is capable of extracting interpretable features directly from the raw data. In addition, by visualising the high-dimensional representations of the network with the t-SNE algorithm, we discover that not only is this method robust to minor mislabeling of the training dataset but that it is possible to identify an underlying substructure within the signals that goes beyond the original labelling. This technique could be utilised to explore and cluster complex, raw detector data in a novel way that may reveal more insights than standard analysis methods
Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber
We present several studies of convolutional neural networks applied to data
coming from the MicroBooNE detector, a liquid argon time projection chamber
(LArTPC). The algorithms studied include the classification of single particle
images, the localization of single particle and neutrino interactions in an
image, and the detection of a simulated neutrino event overlaid with cosmic ray
backgrounds taken from real detector data. These studies demonstrate the
potential of convolutional neural networks for particle identification or event
detection on simulated neutrino interactions. We also address technical issues
that arise when applying this technique to data from a large LArTPC at or near
ground level
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