Galaxies, Human Eyes, and Artificial Neural Networks

@article{Lahav1995GalaxiesHE,
  title={Galaxies, Human Eyes, and Artificial Neural Networks},
  author={O. Lahav and A. Naim and R. Buta and H. Corwin and G. de Vaucouleurs and A. Dressler and J. Huchra and S. van den Bergh and S. Raychaudhury and L. Sodr� and M. Storrie-Lombardi},
  journal={Science},
  year={1995},
  volume={267},
  pages={859 - 862}
}
  • O. Lahav, A. Naim, +8 authors M. Storrie-Lombardi
  • Published 1995
  • Computer Science, Physics, Medicine
  • Science
    • The quantitative morphological classification of galaxies is important for understanding the origin of type frequency and correlations with environment. However, galaxy morphological classification is still mainly done visually by dedicated individuals, in the spirit of Hubble's original scheme and its modifications. The rapid increase in data on galaxy images at low and high redshift calls for a re-examination of the classification schemes and for automatic methods. Here are shown results from… CONTINUE READING
    View on AAAS
    cds.cern.ch
    82 Citations
    Highly Influencial Citations
    5
    Background Citations
    26
    Methods Citations
    23
    Results Citations
    4

    Topics from this paper

    Explore Further: Topics Discussed in This Paper

    82 Citations
    Citation Type

    Citation Type

    Galaxy Zoo: reproducing galaxy morphologies via machine learning★
    • 136
    • PDF
    Rotation-invariant convolutional neural networks for galaxy morphology prediction
    • 375
    • PDF
    Galaxy types in the Sloan Digital Sky survey using supervised artificial neural networks
    • 110
    • Highly Influenced
    • PDF
    Galaxy Zoo 1: data release of morphological classifications for nearly 900 000 galaxies
    • 442
    • PDF
    Objective Classification of Galaxy Spectra using the Information Bottleneck Method
    • 68
    • PDF
    Neural networks and the classification of active galactic nucleus spectra
    • 4
    • PDF
    Galaxy morphology prediction using capsule networks
    • 8
    • PDF
    An automatic taxonomy of galaxy morphology using unsupervised machine learning
    • 32
    • PDF
    Soft clustering analysis of galaxy morphologies: A worked example with SDSS
    • 7
    • PDF
    Galaxy morphological classification in deep-wide surveys via unsupervised machine learning
    • 8
    • PDF