Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using artificial neural networks to improve photometric modeling in
  airless bodies   Relevant information about physical properties of the surface of airless
bodies such as porosity, particle size, or roughness can be inferred knowing
the dependence of the brightness with illumination and observing geometry.
Additionally, this knowledge is necessary to standardize or photometrically
correct data acquired under different illumination conditions. In this work we
develop a robust, automatic, and efficient photometric modeling methodology
which is tested and validated using Bennu images acquired by the camera MapCam
from the OSIRIS-REx spacecraft. It consists of a supervised machine learning
algorithm through an artificial neural network. Our system provides a more
precise modeling for all color filters than the previous procedures which are
already published, offering an improvement over this classic approach of up to
14.30%, as well as a considerable reduction in computing time.

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The categories of this paper are: econ
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The categories of this paper are: q-bio
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The categories of this paper are: eess
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The categories of this paper are: stat
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn