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Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11908)


Further advances in exoplanet detection and characterisation require sampling a diverse population of extrasolar planets. One technique to detect these distant worlds is through the direct detection of their thermal emission. The so-called direct imaging technique, is suitable for observing young planets far from their star. These are very low signal-to-noise-ratio (SNR) measurements and limited ground truth hinders the use of supervised learning approaches. In this paper, we combine deep generative and discriminative models to bypass the issues arising when directly training on real data. We use a Generative Adversarial Network to obtain a suitable dataset for training Convolutional Neural Network classifiers to detect and locate planets across a wide range of SNRs. Tested on artificial data, our detectors exhibit good predictive performance and robustness across SNRs. To demonstrate the limits of the detectors, we provide maps of the precision and recall of the model per pixel of the input image. On real data, the models can re-confirm bright source detections.


  • Exoplanet detection
  • Direct imaging
  • Computer vision
  • Generative Adversarial Networks
  • Convolutional Neural Networks

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  • DOI: 10.1007/978-3-030-46133-1_20
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  1. 1.

    Paris Observatory Exoplanet Catalogue:

  2. 2.

    The term flux refers to the rate of incoming photons.

  3. 3.

    There are no confirmed planet detections on NICMOS filter F110W yet. These bright sources are almost certainly background stars. However, detecting these showcases the potential for any bright source –including planets– to be detected.

  4. 4.

    The original dataset is publicly available at the HST LAPLACE STScI archive

  5. 5.

    The Field of View (FOV) of the camera, is 19.2\({^\prime }{^\prime }\) \(\times \,\)19.2\({{^\prime }{^\prime }}\) corresponding to images of size 256 \(\times \) 256 pixels and the coronagraph is a circular disk with a radius of 4 pixels.

  6. 6.

    Bias calibration removes unwanted saturated pixels that arise during long exposures. Dark calibration corrects for thermal emissions coming from the detector. Flat calibration corrects for differences in sensitivity across the CCD detector.

  7. 7.

    Large bright spots found outside the speckle pattern.

  8. 8.

    Data and code are available at

  9. 9.

    The term refers to artifacts caused by the uneven overlap of the deconvolutions of a CNN when the kernel (filter) size is not divisible by the number of strides.

  10. 10.

  11. 11.

    We opted not to use a GAN for augmenting the positive examples (i) to fully control the SNR of the injected planets, for evaluation purposes and (ii) because the randomly positioned faint planet signal in positive examples would be easily masked by the most prevalent features of the images, i.e. those comprising the speckle pattern.

  12. 12.

    The PSF is the response of the telescope optics to incoming light, i.e. it defines the light distribution of a point-source, e.g. a planet, on the detector plane.

  13. 13.

    The window’s top-left corner is \((x-1, y-1)\) and bottom-right is \((x+2, y+2)\).

  14. 14.


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This project has received funding from the European Research Council (ERC) under the EU Horizon 2020 research & innovation programme (grant No 758892, ExoAI) and under the EU Seventh Framework Programme (FP7/2007-2013)/ ERC grant No 617119 (ExoLights). Furthermore, we acknowledge funding by the Science & Technology Funding Council (STFC) grants: ST/K502406/1, ST/P000282/1, ST/P002153/1 and ST/S002634/1. We are grateful for the support of the NVIDIA Corporation through the NVIDIA GPU Grant program.

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Yip, K.H. et al. (2020). Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11908. Springer, Cham.

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