AMAT

In direct exoplanet detection, existing algorithms use techniques based on a low-rank approximation to separate the rotating planet signal from the quasi-static speckles. We present a novel approach that iteratively finds the planet’s flux and the low-rank approximation of quasi-static signals, strengthening the existing models.

CONTENTS:

• README: this file
• amat.py: the main code for AMAT algorithm
• l1lracd.py: the functions for calculating l1 norm LRA
• util.py: the utilized functions for our proposed algorithm
• test_AMAT.ipynb: test of L1 and L2 norm for exoplanet detection as a detection map comparison.

CITE:

Please cite "An Alternating Minimization Algorithm with Trajectory for Direct Exoplanet Detection". https://doi.org/10.14428/esann/2023.ES2023-137.

Please also provide a link to this webpage in your paper (https://github.com/hazandaglayan/amat)

Dependencies:

You need to install VIP_HCI, numpy, and joblib.