Approximate nonnegative matrix factorization algorithm for the analysis of angular differential imaging data

C. Arcidiacono; V. Simoncini

doi:10.1117/12.2311681

11 July 2018 Approximate nonnegative matrix factorization algorithm for the analysis of angular differential imaging data

C. Arcidiacono, V. Simoncini

Author Affiliations +

Proceedings Volume 10703, Adaptive Optics Systems VI; 1070331 (2018) https://doi.org/10.1117/12.2311681
Event: SPIE Astronomical Telescopes + Instrumentation, 2018, Austin, Texas, United States

Abstract

The angular differential imaging (ADI) is used to improve contrast in high resolution astronomical imaging. An example is the direct imaging of exoplanet in camera fed by Extreme Adaptive Optics. The subtraction of the main dazzling object to observe the faint companion was improved using Principal Component Analysis (PCA). It factorizes the positive astronomical frames into positive and negative components. On the contrary, the Nonnegative Matrix Factorization (NMF) uses only positive components, mimicking the actual composition of the long exposure images.

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C. Arcidiacono and V. Simoncini "Approximate nonnegative matrix factorization algorithm for the analysis of angular differential imaging data", Proc. SPIE 10703, Adaptive Optics Systems VI, 1070331 (11 July 2018); https://doi.org/10.1117/12.2311681

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