paper.bib

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@article{Hagelberg:2016,
	abstract = {We present GRAPHIC, a new angular differential imaging reduction pipeline where all geometric image operations are based on Fourier transforms. To achieve this goal the entire pipeline is parallelized making it possible to reduce large amounts of observation data without the need to bin the data. The specific rotation and shift algorithms based on Fourier transforms are described and performance comparison with conventional interpolation algorithm is given. Tests using fake companions injected in real science frames demonstrate the significant gain obtained by using geometric operations based on Fourier transforms compared to conventional interpolation. This also translates in a better point spread function and speckle subtraction with respect to conventional reduction pipelines, achieving detection limits comparable to current best performing pipelines. Flux conservation of the companions is also demonstrated. This pipeline is currently able to reduce science data produced by Very Large Telescope (VLT)/NACO, Gemini/NICI, VLT/SPHERE, and Subaru/SCExAO.},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {https://ui.adsabs.harvard.edu/abs/2016MNRAS.455.2178H},
	archiveprefix = {arXiv},
	author = {{Hagelberg}, J. and {S{\'e}gransan}, D. and {Udry}, S. and {Wildi}, F.},
	date-added = {2022-11-26 11:24:18 +0100},
	date-modified = {2022-11-26 11:24:23 +0100},
	doi = {10.1093/mnras/stv2398},
	eprint = {1510.04331},
	journal = {MNRAS},
	keywords = {methods: data analysis, techniques: high angular resolution, techniques: image processing, planets and satellites: detection, infrared: planetary systems, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Earth and Planetary Astrophysics},
	month = jan,
	number = {2},
	pages = {2178-2186},
	primaryclass = {astro-ph.IM},
	title = {{The Geneva Reduction and Analysis Pipeline for High-contrast Imaging of planetary Companions}},
	volume = {455},
	year = 2016,
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	bdsk-url-1 = {https://doi.org/10.1093/mnras/stv2398},
	bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2016MNRAS.455.2178H}}

@misc{pyklip:2015,
	abstract = {pyKLIP subtracts out the stellar PSF to search for directly-imaged exoplanets and disks using a Python implementation of the Karhunen-Lo{\`e}ve Image Projection (KLIP) algorithm. pyKLIP supports ADI, SDI, and ADI+SDI to model the stellar PSF and offers a large array of PSF subtraction parameters to optimize the reduction. pyKLIP relies on a minimal amount of dependencies (numpy, scipy, and astropy) and parallelizes the KLIP algorithm to speed up the reduction. pyKLIP supports GPI and P1640 data and can interface with other data sources with the addition of new modules. It also can inject simulated planets and disks as well as automatically search for point sources in PSF-subtracted data.},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {https://ui.adsabs.harvard.edu/abs/2015ascl.soft06001W},
	archiveprefix = {ascl},
	author = {{Wang}, Jason J. and {Ruffio}, Jean-Baptise and {De Rosa}, Robert J. and {Aguilar}, Jonathan and {Wolff}, Schuyler G. and {Pueyo}, Laurent},
	date-added = {2022-06-08 18:04:30 +0200},
	date-modified = {2022-06-08 18:04:37 +0200},
	eid = {ascl:1506.001},
	eprint = {1506.001},
	howpublished = {Astrophysics Source Code Library, record ascl:1506.001},
	keywords = {Software},
	month = jun,
	pages = {ascl:1506.001},
	title = {{pyKLIP: PSF Subtraction for Exoplanets and Disks}},
	year = 2015,
	bdsk-url-1 = {https://ui.adsabs.harvard.edu/abs/2015ascl.soft06001W}}

@inproceedings{Aach:2001,
	abstract = {Today's solid state flat panel radiography detectors provide images which contain artifacts caused by lines, columns and clusters of inactive pixels. If not too large, such defects can be filled by interpolation algorithms which usually work in the spatial domain. This paper describes an alternative spectral domain approach to defect interpolation. The acquired radiograph is modeled as the undistorted image multiplied by a known binary defect window. The window effect is then removed by deconvolving the window spectrum from the spectrum of the observed, distorted radiograph. The basic ingredient of our interpolation algorithm is an earlier approach to block transform coding of arbitrarily shaped image segments, that extrapolates the segment internal intensities over a block into which the segment is embedded. For defect interpolation, the arbitrarily shaped segment is formed by a local image region with defects, thus turning extrapolation into defect interpolation. Our algorithm reconstructs both oriented structures and noise- like information in a natural-looking manner, even for large defects. Moreover, our concept can also be applied to non- binary defect windows, e.g. for gain correction.},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {https://ui.adsabs.harvard.edu/abs/2001SPIE.4322..824A},
	author = {{Aach}, Til and {Metzler}, Volker H.},
	booktitle = {Medical Imaging 2001: Image Processing},
	date-added = {2022-06-07 18:16:09 +0200},
	date-modified = {2022-06-07 18:16:14 +0200},
	doi = {10.1117/12.431161},
	editor = {{Sonka}, Milan and {Hanson}, Kenneth M.},
	month = jul,
	pages = {824-835},
	series = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series},
	title = {{Defect interpolation in digital radiography: how object-oriented transform coding helps}},
	volume = {4322},
	year = 2001,
	bdsk-url-1 = {https://doi.org/10.1117/12.431161},
	bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2001SPIE.4322..824A}}

@article{pynpoint:2015,
	abstract = {We announce the public release of PynPoint, a Python package that we have developed for analysing exoplanet data taken with the angular differential imaging observing technique. In particular, PynPoint is designed to model the point spread function of the central star and to subtract its flux contribution to reveal nearby faint companion planets. The current version of the package does this correction by using a principal component analysis method to build a basis set for modelling the point spread function of the observations. We demonstrate the performance of the package by reanalysing publicly available data on the exoplanet β Pictoris b, which consists of close to 24,000 individual image frames. We show that PynPoint is able to analyse this typical data in roughly 1.5 min on a Mac Pro, when the number of images is reduced by co-adding in sets of 5. The main computational work, the calculation of the Singular-Value-Decomposition, parallelises well as a result of a reliance on the SciPy and NumPy packages. For this calculation the peak memory load is 6 GB, which can be run comfortably on most workstations. A simpler calculation, by co-adding over 50, takes 3 s with a peak memory usage of 600 MB. This can be performed easily on a laptop. In developing the package we have modularised the code so that we will be able to extend functionality in future releases, through the inclusion of more modules, without it affecting the users application programming interface. We distribute the PynPoint package under GPLv3 licence through the central PyPI server, and the documentation is available online (http://pynpoint.ethz.ch).},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {https://ui.adsabs.harvard.edu/abs/2015A&C....10..107A},
	archiveprefix = {arXiv},
	author = {{Amara}, A. and {Quanz}, S.~P. and {Akeret}, J.},
	date-added = {2022-06-03 17:47:00 +0200},
	date-modified = {2022-06-03 17:47:14 +0200},
	doi = {10.1016/j.ascom.2015.01.003},
	eprint = {1405.3284},
	journal = {Astronomy and Computing},
	keywords = {Methods: data analysis, Techniques: image processing, Planets and satellites: detection, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
	month = apr,
	pages = {107-115},
	primaryclass = {astro-ph.EP},
	title = {{PynPoint code for exoplanet imaging}},
	volume = {10},
	year = 2015,
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	bdsk-url-1 = {https://doi.org/10.1016/j.ascom.2015.01.003},
	bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2015A&C....10..107A},
	bdsk-url-3 = {https://ui.adsabs.harvard.edu/link_gateway/2015A&C....10..107A/EPRINT_HTML}}

@article{pynpoint:2019,
	abstract = {Context. The direct detection and characterization of planetary and substellar companions at small angular separations is a rapidly advancing field. Dedicated high-contrast imaging instruments deliver unprecedented sensitivity, enabling detailed insights into the atmospheres of young low-mass companions. In addition, improvements in data reduction and point spread function (PSF)-subtraction algorithms are equally relevant for maximizing the scientific yield, both from new and archival data sets. <BR /> Aims: We aim at developing a generic and modular data-reduction pipeline for processing and analysis of high-contrast imaging data obtained with pupil-stabilized observations. The package should be scalable and robust for future implementations and particularly suitable for the 3-5 μm wavelength range where typically thousands of frames have to be processed and an accurate subtraction of the thermal background emission is critical. <BR /> Methods: PynPoint is written in Python 2.7 and applies various image-processing techniques, as well as statistical tools for analyzing the data, building on open-source Python packages. The current version of PynPoint has evolved from an earlier version that was developed as a PSF-subtraction tool based on principal component analysis (PCA). <BR /> Results: The architecture of PynPoint has been redesigned with the core functionalities decoupled from the pipeline modules. Modules have been implemented for dedicated processing and analysis steps, including background subtraction, frame registration, PSF subtraction, photometric and astrometric measurements, and estimation of detection limits. The pipeline package enables end-to-end data reduction of pupil-stabilized data and supports classical dithering and coronagraphic data sets. As an example, we processed archival VLT/NACO L' and M' data of β Pic b and reassessed the brightness and position of the planet with a Markov chain Monte Carlo analysis; we also provide a derivation of the photometric error budget. <P />Based on observations collected at the European Southern Observatory, Chile, ESO No. 60.A-9800(J), 084.C-0739(A), and 090.C-0653(D).PynPoint is available at <A href="https://github.com/PynPoint/PynPoint">https://github.com/PynPoint/PynPoint</A> under the GNU General Public License v3.},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {https://ui.adsabs.harvard.edu/abs/2019A&A...621A..59S},
	archiveprefix = {arXiv},
	author = {{Stolker}, T. and {Bonse}, M.~J. and {Quanz}, S.~P. and {Amara}, A. and {Cugno}, G. and {Bohn}, A.~J. and {Boehle}, A.},
	date-added = {2022-06-03 17:46:31 +0200},
	date-modified = {2022-06-03 17:46:41 +0200},
	doi = {10.1051/0004-6361/201834136},
	eid = {A59},
	eprint = {1811.03336},
	journal = {Astronomy and Astrophysics},
	keywords = {methods: data analysis, techniques: high angular resolution, techniques: image processing, planets and satellites: detection, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
	month = jan,
	pages = {A59},
	primaryclass = {astro-ph.EP},
	title = {{PynPoint: a modular pipeline architecture for processing and analysis of high-contrast imaging data}},
	volume = {621},
	year = 2019,
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	bdsk-url-1 = {https://doi.org/10.1051/0004-6361/201834136},
	bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2019A&A...621A..59S},
	bdsk-url-3 = {https://ui.adsabs.harvard.edu/link_gateway/2019A&A...621A..59S/EPRINT_HTML}}

@article{Gomez:2016,
	abstract = {Context. Data processing constitutes a critical component of high-contrast exoplanet imaging. Its role is almost as important as the choice of a coronagraph or a wavefront control system, and it is intertwined with the chosen observing strategy. Among the data processing techniques for angular differential imaging (ADI), the most recent is the family of principal component analysis (PCA) based algorithms. It is a widely used statistical tool developed during the first half of the past century. PCA serves, in this case, as a subspace projection technique for constructing a reference point spread function (PSF) that can be subtracted from the science data for boosting the detectability of potential companions present in the data. Unfortunately, when building this reference PSF from the science data itself, PCA comes with certain limitations such as the sensitivity of the lower dimensional orthogonal subspace to non-Gaussian noise. <BR /> Aims: Inspired by recent advances in machine learning algorithms such as robust PCA, we aim to propose a localized subspace projection technique that surpasses current PCA-based post-processing algorithms in terms of the detectability of companions at near real-time speed, a quality that will be useful for future direct imaging surveys. <BR /> Methods: We used randomized low-rank approximation methods recently proposed in the machine learning literature, coupled with entry-wise thresholding to decompose an ADI image sequence locally into low-rank, sparse, and Gaussian noise components (LLSG). This local three-term decomposition separates the starlight and the associated speckle noise from the planetary signal, which mostly remains in the sparse term. We tested the performance of our new algorithm on a long ADI sequence obtained on β Pictoris with VLT/NACO. <BR /> Results: Compared to a standard PCA approach, LLSG decomposition reaches a higher signal-to-noise ratio and has an overall better performance in the receiver operating characteristic space. This three-term decomposition brings a detectability boost compared to the full-frame standard PCA approach, especially in the small inner working angle region where complex speckle noise prevents PCA from discerning true companions from noise.},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {https://ui.adsabs.harvard.edu/abs/2016A&A...589A..54G},
	archiveprefix = {arXiv},
	author = {{Gomez Gonzalez}, C.~A. and {Absil}, O. and {Absil}, P. -A. and {Van Droogenbroeck}, M. and {Mawet}, D. and {Surdej}, J.},
	date-added = {2022-06-03 17:32:26 +0200},
	date-modified = {2022-06-08 18:05:34 +0200},
	doi = {10.1051/0004-6361/201527387},
	eid = {A54},
	eprint = {1602.08381},
	journal = {Astronomy and Astrophysics},
	keywords = {methods: data analysis, techniques: high angular resolution, techniques: image processing, planetary systems, planets and satellites: detection, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Earth and Planetary Astrophysics},
	month = may,
	pages = {A54},
	primaryclass = {astro-ph.IM},
	title = {{Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences. The LLSG algorithm}},
	volume = {589},
	year = 2016,
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@article{Milli:2019,
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {https://ui.adsabs.harvard.edu/abs/2019A&A...626A..54M},
	archiveprefix = {arXiv},
	author = {{Milli}, J. and {Engler}, N. and {Schmid}, H.~M. and {Olofsson}, J. and {M{\'e}nard}, F. and {Kral}, Q. and {Boccaletti}, A. and {Th{\'e}bault}, P. and {Choquet}, E. and {Mouillet}, D. and {Lagrange}, A. -M. and {Augereau}, J. -C. and {Pinte}, C. and {Chauvin}, G. and {Dominik}, C. and {Perrot}, C. and {Zurlo}, A. and {Henning}, T. and {Beuzit}, J. -L. and {Avenhaus}, H. and {Bazzon}, A. and {Moulin}, T. and {Llored}, M. and {Moeller-Nilsson}, O. and {Roelfsema}, R. and {Pragt}, J.},
	date-added = {2022-05-03 22:16:59 +0200},
	date-modified = {2022-05-03 23:04:09 +0200},
	doi = {10.1051/0004-6361/201935363},
	eid = {A54},
	eprint = {1905.03603},
	journal = {Astronomy and Astrophysics},
	keywords = {planet-disk interactions, techniques: high angular resolution, techniques: polarimetric, scattering, stars: individual: HR 4796A, planetary systems, Astrophysics - Earth and Planetary Astrophysics},
	month = jun,
	pages = {A54},
	primaryclass = {astro-ph.EP},
	title = {{Optical polarised phase function of the HR 4796A dust ring}},
	volume = {626},
	year = 2019,
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	bdsk-url-1 = {https://doi.org/10.1051/0004-6361/201935363},
	bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2019A&A...626A..54M},
	bdsk-url-3 = {https://ui.adsabs.harvard.edu/link_gateway/2019A&A...626A..54M/EPRINT_HTML}}

@article{Milli:2017b,
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {http://cdsads.u-strasbg.fr/abs/2017A%26A...599A.108M},
	archiveprefix = {arXiv},
	author = {{Milli}, J. and {Vigan}, A. and {Mouillet}, D. and {Lagrange}, A.-M. and {Augereau}, J.-C. and {Pinte}, C. and {Mawet}, D. and {Schmid}, H.~M. and {Boccaletti}, A. and {Matr{\`a}}, L. and {Kral}, Q. and {Ertel}, S. and {Chauvin}, G. and {Bazzon}, A. and {M{\'e}nard}, F. and {Beuzit}, J.-L. and {Thalmann}, C. and {Dominik}, C. and {Feldt}, M. and {Henning}, T. and {Min}, M. and {Girard}, J.~H. and {Galicher}, R. and {Bonnefoy}, M. and {Fusco}, T. and {de Boer}, J. and {Janson}, M. and {Maire}, A.-L. and {Mesa}, D. and {Schlieder}, J.~E. and {SPHERE Consortium}},
	date-added = {2022-05-03 22:14:02 +0200},
	date-modified = {2022-05-03 23:07:03 +0200},
	doi = {10.1051/0004-6361/201527838},
	eid = {A108},
	eprint = {1701.00750},
	journal = {Astronomy and Astrophysics},
	keywords = {instrumentation: high angular resolution, planet-disk interactions, planets and satellites: detection, scattering, planetary systems},
	month = mar,
	pages = {A108},
	primaryclass = {astro-ph.EP},
	title = {{Near-infrared scattered light properties of the HR 4796 A dust ring.}},
	volume = {599},
	year = {2017},
	bdsk-url-1 = {http://dx.doi.org/10.1051/0004-6361/201527838}}

@article{Ruffio:2017,
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {http://cdsads.u-strasbg.fr/abs/2017ApJ...842...14R},
	archiveprefix = {arXiv},
	author = {{Ruffio}, J.-B. and {Macintosh}, B. and {Wang}, J.~J. and {Pueyo}, L. and {Nielsen}, E.~L. and {De Rosa}, R.~J. and {Czekala}, I. and {Marley}, M.~S. and {Arriaga}, P. and {Bailey}, V.~P. and {Barman}, T. and {Bulger}, J. and {Chilcote}, J. and {Cotten}, T. and {Doyon}, R. and {Duch{\^e}ne}, G. and {Fitzgerald}, M.~P. and {Follette}, K.~B. and {Gerard}, B.~L. and {Goodsell}, S.~J. and {Graham}, J.~R. and {Greenbaum}, A.~Z. and {Hibon}, P. and {Hung}, L.-W. and {Ingraham}, P. and {Kalas}, P. and {Konopacky}, Q. and {Larkin}, J.~E. and {Maire}, J. and {Marchis}, F. and {Marois}, C. and {Metchev}, S. and {Millar-Blanchaer}, M.~A. and {Morzinski}, K.~M. and {Oppenheimer}, R. and {Palmer}, D. and {Patience}, J. and {Perrin}, M. and {Poyneer}, L. and {Rajan}, A. and {Rameau}, J. and {Rantakyr{\"o}}, F.~T. and {Savransky}, D. and {Schneider}, A.~C. and {Sivaramakrishnan}, A. and {Song}, I. and {Soummer}, R. and {Thomas}, S. and {Wallace}, J.~K. and {Ward-Duong}, K. and {Wiktorowicz}, S. and {Wolff}, S.},
	date-added = {2022-04-30 20:03:31 +0200},
	date-modified = {2022-04-30 20:03:37 +0200},
	doi = {10.3847/1538-4357/aa72dd},
	eid = {14},
	eprint = {1705.05477},
	journal = {Astrophysical Journal},
	keywords = {instrumentation: adaptive optics, methods: statistical, planetary systems, surveys, techniques: high angular resolution, techniques: image processing},
	month = jun,
	pages = {14},
	primaryclass = {astro-ph.EP},
	title = {{Improving and Assessing Planet Sensitivity of the GPI Exoplanet Survey with a Forward Model Matched Filter}},
	volume = {842},
	year = {2017},
	bdsk-url-1 = {http://dx.doi.org/10.3847/1538-4357/aa72dd}}

@article{JensenClem:2018,
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {http://cdsads.u-strasbg.fr/abs/2018AJ....155...19J},
	archiveprefix = {arXiv},
	author = {{Jensen-Clem}, R. and {Mawet}, D. and {Gomez Gonzalez}, C.~A. and {Absil}, O. and {Belikov}, R. and {Currie}, T. and {Kenworthy}, M.~A. and {Marois}, C. and {Mazoyer}, J. and {Ruane}, G. and {Tanner}, A. and {Cantalloube}, F.},
	date-added = {2022-04-30 10:51:40 +0200},
	date-modified = {2022-04-30 10:51:45 +0200},
	doi = {10.3847/1538-3881/aa97e4},
	eid = {19},
	eprint = {1711.01215},
	journal = {Astronomical Journal},
	keywords = {methods: statistical, techniques: high angular resolution},
	month = jan,
	pages = {19},
	primaryclass = {astro-ph.IM},
	title = {{A New Standard for Assessing the Performance of High Contrast Imaging Systems}},
	volume = {155},
	year = {2018},
	bdsk-url-1 = {http://dx.doi.org/10.3847/1538-3881/aa97e4}}

@article{Ruane:2019,
	abstract = {Reference star differential imaging (RDI) is a powerful strategy for high-contrast imaging. Using example observations taken with the vortex coronagraph mode of Keck/NIRC2 in L‧ band, we demonstrate that RDI provides improved sensitivity to point sources at small angular separations compared to angular differential imaging (ADI). Applying RDI to images of the low-mass stellar companions HIP 79124 C (192 mas separation, ΔL‧ = 4.01) and HIP 78233 B (141 mas separation, ΔL‧ = 4.78), the latter a first imaging detection, increases the significance of their detections by up to a factor of 5 with respect to ADI. We compare methods for reference frame selection and find that pre-selection of frames improves detection significance of point sources by up to a factor of 3. In addition, we use observations of the circumstellar disks around MWC 758 and 2MASS J16042165-2130284 to show that RDI allows for accurate mapping of scattered light distributions without self-subtraction artifacts.},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {https://ui.adsabs.harvard.edu/abs/2019AJ....157..118R},
	archiveprefix = {arXiv},
	author = {{Ruane} and {Ngo}, Henry and {Mawet}, Dimitri and {Absil}, Olivier and {Choquet}, {\'E}lodie and {Cook}, Therese and {Gomez Gonzalez}, Carlos and {Huby}, Elsa and {Matthews}, Keith and {Meshkat}, Tiffany and {Reggiani}, Maddalena and {Serabyn}, Eugene and {Wallack}, Nicole and {Xuan}, W. Jerry},
	date-added = {2022-04-28 18:01:42 +0200},
	date-modified = {2022-04-30 10:45:31 +0200},
	doi = {10.3847/1538-3881/aafee2},
	eid = {118},
	eprint = {1901.04090},
	journal = {Astronomical Journal},
	keywords = {planets and satellites: detection, protoplanetary disks, stars: imaging, stars: individual: MWC 758, 2MASS J16042165‑2130284, techniques: high angular resolution, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Physics - Optics},
	month = mar,
	number = {3},
	pages = {118},
	primaryclass = {astro-ph.IM},
	title = {{Reference Star Differential Imaging of Close-in Companions and Circumstellar Disks with the NIRC2 Vortex Coronagraph at the W. M. Keck Observatory}},
	volume = {157},
	year = 2019,
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	bdsk-url-1 = {https://doi.org/10.3847/1538-3881/aafee2},
	bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2019AJ....157..118R},
	bdsk-url-3 = {https://ui.adsabs.harvard.edu/link_gateway/2019AJ....157..118R/EPRINT_HTML}}

@article{Dahlqvist:2021,
	abstract = {Context. Most of the high-contrast imaging (HCI) data-processing techniques used over the last 15 years have relied on the angular differential imaging (ADI) observing strategy, along with subtraction of a reference point spread function (PSF) to generate exoplanet detection maps. Recently, a new algorithm called regime switching model (RSM) map has been proposed to take advantage of these numerous PSF-subtraction techniques; RSM uses several of these techniques to generate a single probability map. Selection of the optimal parameters for these PSF-subtraction techniques as well as for the RSM map is not straightforward, is time consuming, and can be biased by assumptions made as to the underlying data set. <BR /> Aims: We propose a novel optimisation procedure that can be applied to each of the PSF-subtraction techniques alone, or to the entire RSM framework. <BR /> Methods: The optimisation procedure consists of three main steps: (i) definition of the optimal set of parameters for the PSF-subtraction techniques using the contrast as performance metric, (ii) optimisation of the RSM algorithm, and (iii) selection of the optimal set of PSF-subtraction techniques and ADI sequences used to generate the final RSM probability map. <BR /> Results: The optimisation procedure is applied to the data sets of the exoplanet imaging data challenge, which provides tools to compare the performance of HCI data-processing techniques. The data sets consist of ADI sequences obtained with three state-of-the-art HCI instruments: SPHERE, NIRC2, and LMIRCam. The results of our analysis demonstrate the interest of the proposed optimisation procedure, with better performance metrics compared to the earlier version of RSM, as well as to other HCI data-processing techniques.},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {https://ui.adsabs.harvard.edu/abs/2021A&A...656A..54D},
	archiveprefix = {arXiv},
	author = {{Dahlqvist}, C. -H. and {Cantalloube}, F. and {Absil}, O.},
	date-added = {2022-04-27 16:08:33 +0200},
	date-modified = {2022-04-27 16:08:38 +0200},
	doi = {10.1051/0004-6361/202141446},
	eid = {A54},
	eprint = {2109.14318},
	journal = {Astronomy and Astrophysics},
	keywords = {methods: data analysis, techniques: image processing, techniques: high angular resolution, planets and satellites: detection, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Earth and Planetary Astrophysics},
	month = dec,
	pages = {A54},
	primaryclass = {astro-ph.IM},
	title = {{Auto-RSM: An automated parameter-selection algorithm for the RSM map exoplanet detection algorithm}},
	volume = {656},
	year = 2021,
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	bdsk-url-1 = {https://doi.org/10.1051/0004-6361/202141446},
	bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2021A&A...656A..54D},
	bdsk-url-3 = {https://ui.adsabs.harvard.edu/link_gateway/2021A&A...656A..54D/EPRINT_HTML}}

@article{Pueyo:2016,
	abstract = {A new class of high-contrast image analysis algorithms that empirically fit and subtract systematic noise has lead to recent discoveries of faint exoplanet/substellar companions and scattered light images of circumstellar disks. These methods are extremely efficient at enhancing the detectability of a faint astrophysical signal, but they generally create systematic biases in their observed properties. This paper provides a general solution for this outstanding problem. We present an analytical derivation of a linear expansion that captures the impact of astrophysical over-subtraction or self-subtraction in current image analysis techniques. We examine the general case for which the reference images of the astrophysical scene move azimuthally and/or radially across the field of view as a result of the observation strategy. Our new method is based on perturbing the covariance matrix underlying any least-squares speckles problem, and propagating this perturbation through the data analysis algorithm. Most of the work in this paper is presented in the Principal Component Analysis framework, but it can be easily generalized to methods relying on the linear combination of images (instead of eigenmodes). Based on this linear expansion, which is obtained in the most general case, we then demonstrate practical applications of this new algorithm. We first consider the spectral extraction of faint point sources in IFS data and illustrate, using public Gemini Planet Imager commissioning data, that our novel perturbation-based Forward Modeling, which we named Karhunen Loeve Image Processing (KLIP-FM), can indeed alleviate algorithmic biases. We then apply KLIP-FM to the detection of point sources and show how it decreases the rate of false negatives while keeping the rate of false positives unchanged when compared to classical KLIP. This can potentially have important consequences on the design of follow-up strategies of ongoing direct imaging surveys.},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {https://ui.adsabs.harvard.edu/abs/2016ApJ...824..117P},
	archiveprefix = {arXiv},
	author = {{Pueyo}, Laurent},
	date-added = {2022-04-27 16:08:23 +0200},
	date-modified = {2022-04-27 16:08:27 +0200},
	doi = {10.3847/0004-637X/824/2/117},
	eid = {117},
	eprint = {1604.06097},
	journal = {Astrophysical Journal},
	keywords = {planetary systems, techniques: image processing, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Earth and Planetary Astrophysics},
	month = jun,
	number = {2},
	pages = {117},
	primaryclass = {astro-ph.IM},
	title = {{Detection and Characterization of Exoplanets using Projections on Karhunen Loeve Eigenimages: Forward Modeling}},
	volume = {824},
	year = 2016,
	bdsk-file-1 = {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},
	bdsk-url-1 = {https://doi.org/10.3847/0004-637X/824/2/117},
	bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2016ApJ...824..117P},
	bdsk-url-3 = {https://ui.adsabs.harvard.edu/link_gateway/2016ApJ...824..117P/EPRINT_HTML}}

@article{Pairet:2021,
	archiveprefix = {arXiv},
	author = {{Pairet}, Beno{\^\i}t and {Cantalloube}, Faustine and {Jacques}, Laurent},
	doi = {10.1093/mnras/stab607},
	eprint = {2008.05170},
	journal = {MNRAS},
	keywords = {techniques: high angular resolution, techniques: image processing, planet-disc interactions, protoplanetary discs, circumstellar matter, Astrophysics - Instrumentation and Methods for Astrophysics},
	month = may,
	number = {3},
	pages = {3724-3742},
	primaryclass = {astro-ph.IM},
	title = {{MAYONNAISE: a morphological components analysis pipeline for circumstellar discs and exoplanets imaging in the near-infrared}},
	url = {https://ui.adsabs.harvard.edu/abs/2021MNRAS.503.3724P},
	volume = {503},
	year = 2021,
	bdsk-url-1 = {https://ui.adsabs.harvard.edu/abs/2021MNRAS.503.3724P},
	bdsk-url-2 = {https://doi.org/10.1093/mnras/stab607}}

@article{Dahlqvist:2020,
	archiveprefix = {arXiv},
	author = {{Dahlqvist}, C. -H. and {Cantalloube}, F. and {Absil}, O.},
	doi = {10.1051/0004-6361/201936421},
	eid = {A95},
	eprint = {1912.05412},
	journal = {Astronomy and Astrophysics},
	keywords = {techniques: image processing, techniques: high angular resolution, methods: statistical, methods: data analysis, planetary systems, planets and satellites: detection, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Earth and Planetary Astrophysics},
	month = jan,
	pages = {A95},
	primaryclass = {astro-ph.IM},
	title = {{Regime-switching model detection map for direct exoplanet detection in ADI sequences}},
	url = {https://ui.adsabs.harvard.edu/abs/2020A&A...633A..95D},
	volume = {633},
	year = 2020,
	bdsk-url-1 = {https://ui.adsabs.harvard.edu/abs/2020A&A...633A..95D},
	bdsk-url-2 = {https://doi.org/10.1051/0004-6361/201936421}}

@article{Gomez:2018,
	archiveprefix = {arXiv},
	author = {{Gomez Gonzalez}, C.~A. and {Absil}, O. and {Van Droogenbroeck}, M.},
	doi = {10.1051/0004-6361/201731961},
	eid = {A71},
	eprint = {1712.02841},
	journal = {Astronomy and Astrophysics},
	keywords = {methods: data analysis, techniques: high angular resolution, techniques: imaging spectroscopy, planetary systems, planets and satellites: detection, Astrophysics - Instrumentation and Methods for Astrophysics},
	month = {Jun},
	pages = {A71},
	primaryclass = {astro-ph.IM},
	title = {{Supervised detection of exoplanets in high-contrast imaging sequences}},
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@article{Amara:2012,
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@article{Flasseur:2018,
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {https://ui.adsabs.harvard.edu/abs/2018A&A...618A.138F},
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