arXiv subjects

Leonid Pogorelyuk

Publications and source records attributed to Leonid Pogorelyuk.

21 records · Page 2Linked to original sources

arXiv2019

Reduced Order Estimation of the Speckle Electric Field History for Space-Based Coronagraphs

In high-contrast space-based coronagraphs, one of the main limiting factors for imaging the dimmest exoplanets is the time varying nature of the residual starlight (speckles). Modern methods try to differentiate between the intensities of starlight and other sources, but none incorporate models of space-based systems which can take into account actuations of the deformable mirrors. Instead, we propose formulating the estimation problem in terms of the electric field while allowing for dithering of the deformable mirrors. Our reduced-order approach is similar to intensity-based PCA (e.g. KLIP) although, under certain assumptions, it requires a considerably lower number of modes of the electric field. We illustrate this by a FALCO simulation of the WFIRST hybrid Lyot coronagraph.

astro-ph.IM↗

arXiv2019

Dark Hole Maintenance and A Posteriori Intensity Estimation in the Presence of Speckle Drift in a High-Contrast Space Coronagraph

Direct exoplanet imaging via coronagraphy requires maintenance of high contrast in a dark hole for lengthy integration periods. Wavefront errors that change slowly over that time accumulate and cause systematic errors in the star's Point Spread Function (PSF) which limit the achievable signal-to-noise ratio of the planet. In this paper we show that estimating the speckle drift can be achieved via intensity measurements in the dark hole together with dithering of the deformable mirrors to increase phase diversity. A scheme based on an Extended Kalman Filter and Electric Field Conjugation is proposed for maintaining the dark hole during the integration phase. For the post-processing phase, an a posteriori approach is proposed to estimate the realization of the PSF drift process and the intensity of the planet light incoherent with the speckles.

astro-ph.IM↗

arXiv2018

Clustering of Series via Dynamic Mode Decomposition and the Matrix Pencil Method

In this paper, a new algorithm for extracting features from sequences of multidimensional observations is presented. The independently developed Dynamic Mode Decomposition and Matrix Pencil methods provide a least-squares model-based approach for estimating complex frequencies present in signals as well as their corresponding amplitudes. Unlike other feature extraction methods such as Fourier Transform or Autoregression which have to be computed for each sequence individually, the least-squares approach considers the whole dataset at once. It invokes order reduction methods to extract a small number of features best describing all given data, and indicate which frequencies correspond to which sequences. As an illustrative example, the new method is applied to regions of different grain orientation in a Transmission Electron Microscopy image.

math.NA↗