Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Topological defects and confinement with machine learning: the case of
  monopoles in compact electrodynamics   We investigate the advantages of machine learning techniques to recognize the
dynamics of topological objects in quantum field theories. We consider the
compact U(1) gauge theory in three spacetime dimensions as the simplest example
of a theory that exhibits confinement and mass gap phenomena generated by
monopoles. We train a neural network with a generated set of monopole
configurations to distinguish between confinement and deconfinement phases,
from which it is possible to determine the deconfinement transition point and
to predict several observables. The model uses a supervised learning approach
and treats the monopole configurations as three-dimensional images (holograms).
We show that the model can determine the transition temperature with accuracy,
which depends on the criteria implemented in the algorithm. More importantly,
we train the neural network with configurations from a single lattice size
before making predictions for configurations from other lattice sizes, from
which a reliable estimation of the critical temperatures are obtained.

31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin