Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Convolutional Neural Network For Cosmic String Detection in CMB
  Temperature Maps   We present in detail the convolutional neural network used in our previous
work to detect cosmic strings in cosmic microwave background (CMB) temperature
anisotropy maps. By training this neural network on numerically generated CMB
temperature maps, with and without cosmic strings, the network can produce
prediction maps that locate the position of the cosmic strings and provide a
probabilistic estimate of the value of the string tension $G\mu$. Supplying
noiseless simulations of CMB maps with arcmin resolution to the network
resulted in the accurate determination both of string locations and string
tension for sky maps having strings with string tension as low as
$G\mu=5\times10^{-9}$. The code is publicly available online. Though we trained
the network with a long straight string toy model, we show the network performs
well with realistic Nambu-Goto simulations.

15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
1
The categories of this paper are: cs
23
The categories of this paper are: solv-int