AI1talk#293

Leveraging Transfer Learning for Astronomical Image Analysis

Cosmic Insights from Big Data: How Machine Learning is Decoding the Universe

作者

  • Stefano CavuotiINAF - Astronomical Observatory of Capodimonte Napoli
  • Demetra De CiccoUniversity of Napoli “Federico II”
  • Lars DoorenbosAIMI, ARTORG Center, University of Bern
  • Gianluca SasanelliUniversity of Napoli “Federico II”
  • Olena Torbaniuk
  • Massimo BresciaUniversity of Napoli “Federico II”
  • Giuseppe LongoUniversity of Napoli “Federico II”
  • Pablo Márquez-NeilaAIMI, ARTORG Center, University of Bern
  • Maurizio PaolilloUniversity of Napoli “Federico II”
  • Raphael SznitmanAIMI, ARTORG Center, University of Bern
  • Crescenzo TortoraINAF - Astronomical Observatory of Capodimonte Napoli

This presentation explores some applications of transfer learning in astronomical image analysis, focusing on the usage of a pretrained network (EfficientNet) as a feature extractor. We discuss methods for identifying active galactic nuclei, extracting physical parameters, and detecting anomalies in time series data. Additionally, we present some potential future applications, demonstrating the versatility of this approach, even without a training phase.

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