Convolutional neural networks for transient candidate vetting in large-scale surveys

@inproceedings{Gieseke2017ConvolutionalNN,
  title={Convolutional neural networks for transient candidate vetting in large-scale surveys},
  author={Fabian Gieseke and Steven Bloemen and Cas van den Bogaard and Tom Heskes and Jonas Kindler and Richard Scalzo and Val'erio A. R. M. Ribeiro and Jan van Roestel and Paul J. De Groot and Fang Yuan and Anais Moller and Brad Tucker},
  year={2017}
}
  • Fabian Gieseke, Steven Bloemen, +9 authors Brad Tucker
  • Published 2017
  • Physics
  • Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make intensive use of machine learning algorithms to classify the detected objects and to filter out the most interesting candidates. A number of upcoming surveys will produce up to three orders of magnitude more data, which renders high-precision classification… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    1
    Twitter Mention

    Citations

    Publications citing this paper.
    SHOWING 1-3 OF 3 CITATIONS

    References

    Publications referenced by this paper.
    SHOWING 1-5 OF 5 REFERENCES

    The Elements of Statistical Learning

    VIEW 7 EXCERPTS
    HIGHLY INFLUENTIAL

    nolearn: scikit-learn compatible neural network library

    • D Nouri
    • 2014
    VIEW 1 EXCERPT
    HIGHLY INFLUENTIAL

    nolearn: scikit-learn compatible neural network

    • S. Press Nissanke, M. Kasliwal, A. Georgieva
    • Nouri D.,
    • 2013
    VIEW 1 EXCERPT

    Machine Learning: A Probabilistic Perspec

    • W. Hubbard, L D.Jackel
    • Neural Computation,
    • 1989