Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Observational cosmology with Artificial Neural Networks   In cosmology, the analysis of observational evidence is very important to
test theoretical models of the Universe. Artificial neural networks are
powerful and versatile computational tools for data modelling and are recently
being considered in the analysis of cosmological data. The main goal of this
paper is to provide an introduction to artificial neural networks and to
describe some applications to cosmology. We present an overview on the
fundamentals of neural networks and their technical details. Throughout three
examples, we show their capabilities in modelling cosmological data, saving
computational time in numerical tasks, and classifying stellar objects.
Artificial neural networks offer interesting qualities that make them a viable
alternative method for data analysis in cosmological research.

26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin