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High-contrast imaging provided by a coronagraph is critical for the direction imaging of the Earth-like planet orbiting its bright parent star. A major limitation for such direct imaging is the speckle noise that is induced from the…

Instrumentation and Methods for Astrophysics · Physics 2015-10-14 JiangPei Dou , DeQing Ren , YongTian Zhu , Xi Zhang

The direct detection and characterization of exoplanets will be a major scientific driver over the next decade, involving the development of very large telescopes and requires high-contrast imaging close to the optical axis. Some complex…

Instrumentation and Methods for Astrophysics · Physics 2017-05-16 M. Beaulieu , L. Abe , P. Martinez , P. Baudoz , C. Gouvret , F. Vakili

High-dynamic range imaging from space in the visible, aiming in particular at the detection of terrestrial exoplanets, necessitates not only the use of a coronagraph, but also of adaptive optics to correct optical defects in real time.…

Astrophysics · Physics 2008-11-26 Pascal J. Bordé , Wesley A. Traub

In a previous paper, we discussed an original solution to improve the performances of coronagraphs by adding, in the optical scheme, an adaptive hologram removing most of the residual speckle starlight. In our simulations, the detection…

Instrumentation and Methods for Astrophysics · Physics 2011-02-17 Davide Ricci , Hervé Le Coroller , Antoine Labeyrie , Pierre Piron

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…

Instrumentation and Methods for Astrophysics · Physics 2019-09-04 Leonid Pogorelyuk , N. Jeremy Kasdin , Clarence W. Rowley

We present a novel axial ptychographic coherent diffractive imaging (AP-CDI) technique designed to overcome the critical throughput bottleneck of conventional methods. By replacing the 2D raster scan with a simple 1D axial scan, our…

Optics · Physics 2025-11-24 Qijun You , Lingshuo Meng , Fangrui Quan , Wei Cao

ERIS is a diffraction limited thermal infrared imager and spectrograph for the Very Large Telescope UT4. One of the science cases for ERIS is the detection and characterization of circumstellar structures and exoplanets around bright stars…

In the last years, the Observatory of Padova (Istituto Nazionale di Astrofisica - Osservatorio Astronomico di Padova) and the University of Padova have been involved massively in projects dedicated to the exoplanets search, both ground, and…

Instrumentation and Methods for Astrophysics · Physics 2020-12-15 Gabriele Umbriaco

High-contrast imaging and spectroscopy provide unique constraints for exoplanet formation models as well as for planetary atmosphere models. Instrumentation techniques in this field have greatly improved over the last two decades, with the…

Instrumentation and Methods for Astrophysics · Physics 2017-12-20 Johan Mazoyer , Laurent Pueyo , Mamadou N'Diaye , Kevin Fogarty , Neil Zimmerman , Rémi Soummer , Stuart Shaklan , Colin Norman

This Point spread function (PSF) plays a crucial role in many computational imaging applications, such as shape from focus/defocus, depth estimation, and fluorescence microscopy. However, the mathematical model of the defocus process is…

Computer Vision and Pattern Recognition · Computer Science 2022-09-20 Renzhi He , Yan Zhuang , Boya Fu , Fei Liu

We further examine a theory of phase contrast imaging (PCI) of cold atomic gases, first introduced by us in Phys. Rev. Lett. {\bf 112}, 233602 (2014). We model the PCI measurement by directly calculating the entangled state between the…

Atomic Physics · Physics 2018-08-14 Ebubechukwu O. Ilo-Okeke , Tim Byrnes

Computational fluid dynamics (CFD) based simulation of coronary blood flow provides valuable hemodynamic markers, such as pressure gradients, for diagnosing coronary artery disease (CAD). However, CFD is computationally expensive,…

Computer Vision and Pattern Recognition · Computer Science 2025-12-12 Rene Lisasi , Michele Esposito , Chen Zhao

Sparse principal component analysis (sparse PCA) is a widely used technique for dimensionality reduction in multivariate analysis, addressing two key limitations of standard PCA. First, sparse PCA can be implemented in high-dimensional low…

Methodology · Statistics 2025-10-07 Jan O. Bauer

We demonstrate the use of a variant of Principal Component Analysis (PCA) for discrimination problems in astronomy. This variant of PCA is shown to provide the best linear discrimination between data classes. As a test case, we present the…

Astrophysics · Physics 2009-10-30 Rodrigo Ibata , Michael Irwin

A high contrast coronagraph is expected to provide one of the promising ways to directly observe extra-solar planets. We present the newest results of our laboratory experiment investigating "rigid" coronagraph with a binary shaped…

Instrumentation and Methods for Astrophysics · Physics 2011-08-17 K. Enya , L. Abe , S. Tanaka , T. Nakagawa , K. Haze , T. Sato , T. Wakayama

The in situ measurement of the particle size distribution (PSD) of a suspension of particles presents huge challenges. Various effects from the process could introduce noise to the data from which the PSD is estimated. This in turn could…

Computed tomography is widely used to examine internal structures in a non-destructive manner. To obtain high-quality reconstructions, one typically has to acquire a densely sampled trajectory to avoid angular undersampling. However, many…

Image and Video Processing · Electrical Eng. & Systems 2020-12-10 Haoyu Wei , Florian Schiffers , Tobias Würfl , Daming Shen , Daniel Kim , Aggelos K. Katsaggelos , Oliver Cossairt

Context. Proxima Cen b is the prime target for the search of life around a nearby exoplanet by characterizing its atmosphere in reflected light. Due to the very high star/companion contrast (<1E-6), High Dispersion Coronagraphy is the most…

Instrumentation and Methods for Astrophysics · Physics 2026-01-14 N. Blind , N. Restori , B . Chazelas , C. Lovis , J. Kühn , C. Mordasini

Singular value decomposition (SVD) based principal component analysis (PCA) breaks down in the high-dimensional and limited sample size regime below a certain critical eigen-SNR that depends on the dimensionality of the system and the…

Statistics Theory · Mathematics 2019-12-17 Arvind Prasadan , Raj Rao Nadakuditi , Debashis Paul

Principal components analysis (PCA) is a classical method for the reduction of dimensionality of data in the form of n observations (or cases) of a vector with p variables. For a simple model of factor analysis type, it is proved that…

Statistics Theory · Mathematics 2009-01-29 Iain M Johnstone , Arthur Yu Lu
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