CADET is a machine learning pipeline trained for identification of surface brightness depressions (so-called X-ray cavities) on noisy Chandra images of early-type galaxies and galaxy clusters. The pipeline consists of a convolutional neural network trained for producing pixel-wise cavity predictions and a DBSCAN clustering algorithm, which decomposes the predictions into individual cavities. The pipeline is further described in Plšek et al. 2023.

The CADET pipeline was originally developed as a part of my diploma thesis and was further described in Plšek et al. 2023. If you use the CADET pipeline in your research, please cite the following paper:

@misc{plšek2023cavity,
      title={CAvity DEtection Tool (CADET): Pipeline for automatic detection of X-ray cavities in hot galactic and cluster atmospheres}, 
      author={Tomáš Plšek and Norbert Werner and Martin Topinka and Aurora Simionescu},
      year={2023},
      eprint={2304.05457},
      archivePrefix={arXiv},
      primaryClass={astro-ph.HE}
}