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Found 4 papers, 4 papers with code
Astronomical image time series classification using CONVolutional attENTION (ConvEntion)
In this work, we solved various problems the datasets tend to suffer from and we present new results for classifications using astronomical image time series with an increase in accuracy of 13%, compared to state-of-the-art approaches that use image time series, and a 12% increase, compared to approaches that use light curves.
Galaxy Image Translation with Semi-supervised Noise-reconstructed Generative Adversarial Networks
Image-to-image translation with Deep Learning neural networks, particularly with Generative Adversarial Networks (GANs), is one of the most powerful methods for simulating astronomical images.
A CNN adapted to time series for the classification of Supernovae
The first one is adapted to time series and thus to the treatment of supernovae light-curves.
Photometric redshifts from SDSS images using a Convolutional Neural Network
We also find that the CNN redshifts are unbiased with respect to galaxy inclination, and that $\sigma_{MAD}$ decreases with the signal-to-noise ratio (SNR), achieving values below 0. 007 for SNR >100, as in the deep stacked region of Stripe 82.
Instrumentation and Methods for Astrophysics
