An Alternating Minimization Algorithm with Trajectory for Direct Exoplanet Detection

Daglayan Sevim, Hazan;Vary, Simon;Absil, Pierre-Antoine
(2023) ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning — Location: Bruges (Belgium) and online (4.October.2023)

Files

ES2023-137.pdf
  • Open Access
  • Adobe PDF
  • 2.2 MB
Download

Details

Authors
Abstract
Effective image post-processing algorithms are vital for the successful direct imaging of exoplanets. Existing algorithms use techniques based on a low-rank approximation to separate the rotating planet signal from the quasi-static speckles. In this paper, we present a novel approach that iteratively finds the planet’s flux and the low-rank approximation of quasi-static signals, strengthening the existing model based on lowrank approximations. We implement the algorithm with two different norms and test it on data, showing improvement over classical low-rank approaches. Our results highlight the benefits of iterative refinement of low-rank approximation to enhance planet detection.
Affiliations

Citations

  • APA
  • Chicago
  • FWB

Daglayan Sevim, H., Vary, S., & Absil, P.-A. (2023). An Alternating Minimization Algorithm with Trajectory for Direct Exoplanet Detection. ESANN 2023 proceedings. Published. ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium) and online. https://doi.org/10.14428/esann/2023.es2023-137