64,934 research outputs found
Neural network parametrization of spectral functions from hadronic tau decays and determination of QCD vacuum condensates
The spectral function is determined from ALEPH and OPAL data
on hadronic tau decays using a neural network parametrization trained to retain
the full experimental information on errors, their correlations and chiral sum
rules: the DMO sum rule, the first and second Weinberg sum rules and the
electromagnetic mass splitting of the pion sum rule. Nonperturbative QCD vacuum
condensates can then be determined from finite energy sum rules. Our method
minimizes all sources of theoretical uncertainty and bias producing an estimate
of the condensates which is independent of the specific finite energy sum rule
used. The results for the central values of the condensates and are
both negative.Comment: 29 pages, 18 ps figure
A geometrical analysis of global stability in trained feedback networks
Recurrent neural networks have been extensively studied in the context of
neuroscience and machine learning due to their ability to implement complex
computations. While substantial progress in designing effective learning
algorithms has been achieved in the last years, a full understanding of trained
recurrent networks is still lacking. Specifically, the mechanisms that allow
computations to emerge from the underlying recurrent dynamics are largely
unknown. Here we focus on a simple, yet underexplored computational setup: a
feedback architecture trained to associate a stationary output to a stationary
input. As a starting point, we derive an approximate analytical description of
global dynamics in trained networks which assumes uncorrelated connectivity
weights in the feedback and in the random bulk. The resulting mean-field theory
suggests that the task admits several classes of solutions, which imply
different stability properties. Different classes are characterized in terms of
the geometrical arrangement of the readout with respect to the input vectors,
defined in the high-dimensional space spanned by the network population. We
find that such approximate theoretical approach can be used to understand how
standard training techniques implement the input-output task in finite-size
feedback networks. In particular, our simplified description captures the local
and the global stability properties of the target solution, and thus predicts
training performance
Deep Neural Networks for Energy and Position Reconstruction in EXO-200
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.Comment: Accepted version. 33 pages, 28 figure
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