Research Area: Astronomical Imagery and Data

We apply modern computer vision to astronomical surveys and cluster science, including transfer learning on SDSS multi-band images, self-supervised feature learning for cluster richness, and detection of inverse Compton emission. This thread demonstrates cross-domain use of representation learning methods developed in the lab.

All Publications

  1. Thumbnail for Semisupervised Learning for Detecting Inverse Compton Emission in Galaxy Clusters
    Lin S-C, Su Y, Gastaldello F, Jacobs N. 2024. Semisupervised Learning for Detecting Inverse Compton Emission in Galaxy Clusters. Astrophysical Journal 977:176. DOI: 10.3847/1538-4357/ad8888.
  2. Thumbnail for Estimating Cluster Masses from SDSS Multi-band Images with Transfer Learning
    Lin S-C, Su Y, Liang G, Zhang Y, Jacobs N, Zhang Y. 2022. Estimating Cluster Masses from SDSS Multi-band Images with Transfer Learning. Monthly Notices of the Royal Astronomical Society (MNRAS) 512:3885–3894. DOI: 10.1093/mnras/stac725.
  3. Thumbnail for Multi-Branch Attention Networks for Classifying Galaxy Clusters
    Zhang Y, Liang G, Su Y, Jacobs N. 2021. Multi-Branch Attention Networks for Classifying Galaxy Clusters. In: International Conference on Pattern Recognition (ICPR 2020). DOI: 10.1109/ICPR48806.2021.9412498.
  4. Thumbnail for Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate
    Liang G, Su Y, Lin S-C, Zhang Y, Zhang Y, Jacobs N. 2020. Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate. In: Workshop on Machine Learning and the Physical Sciences at the 34th Conference on Neural Information Processing Systems.
  5. Thumbnail for A deep learning view of the census of galaxy clusters in IllustrisTNG
    Su Y, Zhang Y, Liang G, ZuHone J, Barnes D, Jacobs N, Ntampaka M, Forman W, Nulsen P, Kraft R, Jones C. 2020. A deep learning view of the census of galaxy clusters in IllustrisTNG. Monthly Notices of the Royal Astronomical Society (MNRAS). DOI: 10.1093/mnras/staa2690.