Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Bayesian Framework for Cosmic String Searches in CMB Maps   There exists various proposals to detect cosmic strings from Cosmic Microwave
Background (CMB) or 21 cm temperature maps. Current proposals do not aim to
find the location of strings on sky maps, all of these approaches can be
thought of as a statistic on a sky map. We propose a Bayesian interpretation of
cosmic string detection and within that framework, we derive a connection
between estimates of cosmic string locations and cosmic string tension $G\mu$.
We use this Bayesian framework to develop a machine learning framework for
detecting strings from sky maps and outline how to implement this framework
with neural networks. The neural network we trained was able to detect and
locate cosmic strings on noiseless CMB temperature map down to a string tension
of $G\mu=5 \times10^{-9}$ and when analyzing a CMB temperature map that does
not contain strings, the neural network gives a 0.95 probability that
$G\mu\leq2.3\times10^{-9}$.

31
The categories of this paper are: ao-sci
1
The categories of this paper are: cs
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph