21,246 research outputs found
Application of Neural Networks for Energy Reconstruction
The possibility to use Neural Networks for reconstruction of the energy
deposited in the calorimetry system of the CMS detector is investigated. It is
shown that using feed - forward neural network, good linearity, Gaussian energy
distribution and good energy resolution can be achieved. Significant
improvement of the energy resolution and linearity is reached in comparison
with other weighting methods for energy reconstruction.Comment: 18 pages, 13 figures, LATEX, submitted to: Nuclear Instruments &
Methods
Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment
We present a simulation-based study using deep convolutional neural networks
(DCNNs) to identify neutrino interaction vertices in the MINERvA passive
targets region, and illustrate the application of domain adversarial neural
networks (DANNs) in this context. DANNs are designed to be trained in one
domain (simulated data) but tested in a second domain (physics data) and
utilize unlabeled data from the second domain so that during training only
features which are unable to discriminate between the domains are promoted.
MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at
Fermilab. -dependent cross sections are an important part of the physics
program, and these measurements require vertex finding in complicated events.
To illustrate the impact of the DANN we used a modified set of simulation in
place of physics data during the training of the DANN and then used the label
of the modified simulation during the evaluation of the DANN. We find that deep
learning based methods offer significant advantages over our prior track-based
reconstruction for the task of vertex finding, and that DANNs are able to
improve the performance of deep networks by leveraging available unlabeled data
and by mitigating network performance degradation rooted in biases in the
physics models used for training.Comment: 41 page
Review of Face Detection Systems Based Artificial Neural Networks Algorithms
Face detection is one of the most relevant applications of image processing
and biometric systems. Artificial neural networks (ANN) have been used in the
field of image processing and pattern recognition. There is lack of literature
surveys which give overview about the studies and researches related to the
using of ANN in face detection. Therefore, this research includes a general
review of face detection studies and systems which based on different ANN
approaches and algorithms. The strengths and limitations of these literature
studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa
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