diff --git a/paper.md b/paper.md index a6fe62e8..8e5d3ba4 100755 --- a/paper.md +++ b/paper.md @@ -25,17 +25,22 @@ authors: - name: Arthur Vigan orcid: 0000-0002-5902-7828 affiliation: 5 + - name: Iain Hammond + orcid: 0000-0003-1502-4315 + affiliation: 6 affiliations: - - name: Space sciences, Technologies & Astrophysics Research Institute, Université de Liège, Belgium - index: 1 - - name: TO BE FILLED - index: 2 - - name: Rheinische Friedrich-Wilhelms-Universität Bonn, Germany - index: 3 - - name: Institute of Astronomy, KU Leuven, Belgium - index: 4 - - name: Aix Marseille Univ, CNRS, CNES, LAM, Marseille, France - index: 5 + - name: Space sciences, Technologies & Astrophysics Research Institute, Université de Liège, Belgium + index: 1 + - name: TO BE FILLED + index: 2 + - name: Rheinische Friedrich-Wilhelms-Universität Bonn, Germany + index: 3 + - name: Institute of Astronomy, KU Leuven, Belgium + index: 4 + - name: Aix Marseille Univ, CNRS, CNES, LAM, Marseille, France + index: 5 + - name: School of Physics and Astronomy, Monash University, Vic 3800, Australia + index: 6 date: 4 May 2022 bibliography: paper.bib --- @@ -44,8 +49,8 @@ bibliography: paper.bib Direct imaging of exoplanets and circumstellar disks at optical and infrared wavelengths requires reaching high contrasts at short angular separations. This -can only be achieved through the synergy of different techniques, such as -adaptive optics, coronagraphy, and a relevant combination of observing strategy +can only be achieved through the synergy of advanced instrumentation, such as +adaptive optics and coronagraphy, with a relevant combination of observing strategy and post-processing algorithms to model and subtract residual starlight. In this context, ``VIP`` is a Python package providing the tools to reduce, post-process and analyze high-contrast imaging datasets, enabling the detection @@ -55,22 +60,21 @@ stellar environments. # Statement of need ``VIP`` stands for Vortex Image Processing. It is a collaborative project -which started at the University of Liège, and aiming to integrate open-source, +which started at the University of Liège, aiming to integrate open-source, efficient, easy-to-use and well-documented implementations of state-of-the-art algorithms used in the context of high-contrast imaging. The package follows a modular architecture, such that its routines cover a wide diversity of tasks, including: * image pre-processing, such as sky subtraction, bad pixel correction, bad -frames removal, or image alignment and star centering (`preproc` module); +frame removal, or image alignment and star centering (`preproc` module); -* modeling and subtracting the stellar PSF using state-of-the-art algorithms -leveraging observing strategies such as angular differential imaging (ADI), +* modeling and subtracting the stellar point spread function (PSF) using state-of-the-art algorithms that leverage observing strategies such as angular differential imaging (ADI), spectral differential imaging (SDI) or reference star differential imaging [@Racine:1999; @Sparks:2002; @Marois:2006], which induce diversity between speckle and authentic astrophysical signals (`psfsub` module); -* characterizing either point sources or extended circumstellar signals through +* characterizing point sources or extended circumstellar signals through forward modeling (`fm` module); * detecting and characterizing point sources through inverse approaches @@ -81,7 +85,7 @@ detecting point sources, and estimating their significance (`metrics` module). The features implemented in ``VIP`` as of 2017 are described in @Gomez:2017. Since then, the package has been widely used by the high-contrast imaging -community, for the discovery of low-mass companions +community for the discovery of low-mass companions [@Milli:2017; @Hirsch:2019; @Ubeira:2020], their characterization [@Wertz:2017; @Delorme:2017; @Christiaens:2018; @Christiaens:2019], the study of planet formation [@Ruane:2017; @Reggiani:2018; @Mauco:2020; @Toci:2020], @@ -89,7 +93,7 @@ the study of high-mass star formation [@Rainot:2020; @Rainot:2022], or the development of new high-contrast imaging algorithms [@Gomez:2018; @Dahlqvist:2020; @Pairet:2021; @Dahlqvist:2021]. Given the rapid expansion of ``VIP``, we summarize here all novelties that were brought -to the package over the past 5 years. +to the package over the past five years. The rest of this manuscript summarizes all major changes since v0.7.0 [@Gomez:2017], that are included in the latest release of ``VIP`` (v1.3.0). At @@ -97,7 +101,7 @@ a structural level, ``VIP`` underwent a major change since version v1.1.0, which aimed to migrate towards a more streamlined and easy-to-use architecture. The package now revolves around five major modules (`fm`, `invprob`, `metrics`, `preproc` and `psfsub`, as described above) complemented by four additional -modules containing different kinds of utility functions (`config`, `fits`, +modules containing various utility functions (`config`, `fits`, `stats` and `var`). New `Dataset` and `Frame` classes have also been implemented, enabling an object-oriented approach for processing high-contrast imaging datasets and analyzing final images, respectively. Similarly, a @@ -111,8 +115,8 @@ Some of the major changes in each module of ``VIP`` are summarized below: models and extended signals in ADI cubes, in order to forward-model the effect of ADI post-processing [@Milli:2012; @Christiaens:2019]; - the log-likelihood expression used in the negative fake companion (NEGFC) - technique was updated, as well as the default convergence criterion for the - NEGFC-MCMC method - it is now based on auto-correlation [@Christiaens:2021]; + technique was updated, and the default convergence criterion for the + NEGFC-MCMC method is now based on auto-correlation [@Christiaens:2021]; - the NEGFC methods are now fully compatible with integral field spectrograph (IFS) input datacubes. @@ -131,7 +135,7 @@ Some of the major changes in each module of ``VIP`` are summarized below: of either isolated bad pixels or clumps of bad pixels, leveraging on iterative sigma filtering (`cube_fix_badpix_clump`), the circular symmetry of the PSF (`cube_fix_badpix_annuli`), or the radial expansion of the PSF - with wavelength (`cube_fix_badpix_ifs`), and (ii) the correction of bad + with changing wavelength (`cube_fix_badpix_ifs`), and (ii) the correction of bad pixels based on either median replacement (default) or Gaussian kernel interpolation (`cube_fix_badpix_with_kernel`); - a new algorithm was added for the recentering of coronagraphic image cubes @@ -147,7 +151,7 @@ Some of the major changes in each module of ``VIP`` are summarized below: [@Lafreniere:2007] was added; - an annular version of the non-negative matrix factorization algorithm is now available [@Lee:1999; @Gomez:2017]; - - besides median-ADI, the `medsub` routine now also supports median-SDI. + - the `medsub` routine now also supports median-SDI. We refer the interested reader to release descriptions and GitHub [announcements](https://github.com/vortex-exoplanet/VIP/discussions/categories/announcements) @@ -167,17 +171,16 @@ defined as the top-right pixel among the four central pixels of the image - a change motivated by the new default FT-based methods for image operations. The center convention is unchanged for odd-size images (central pixel). -Finally, a total of nine jupyter notebook tutorials covering most of the -available features in VIP were implemented. These tutorials illustrate (i) how -to load and post-process an ADI dataset (quick-start tutorial); (ii) how to -pre-process ADI and IFS datasets; (iii) how to model and subtract the stellar -halo with ADI-based algorithms; (iv) how to calculate metrics such as the S/N -ratio [@Mawet:2014], STIM maps [@Pairet:2019] and contrast curves; (v) how to -find the radial separation, azimuth and flux of a point source; (vi) how to -create and forward model scattered-light disk models; (vii) how to post-process -IFS data and infer the exact astro- and photometry of a given point source; -(viii) how to use FT-based and interpolation-based methods for different image -operations, and assess their respective performance; and (ix) how to use the +Finally, a total of eight jupyter notebook tutorials covering most of the +available features in VIP were implemented. These tutorials illustrate how to (i) +load and post-process an ADI dataset (quick-start tutorial); (ii) pre-process ADI +and IFS datasets; (iii) model and subtract the stellar halo with ADI-based +algorithms; (iv) calculate metrics such as the S/N ratio [@Mawet:2014], STIM maps +[@Pairet:2019] and contrast curves; (v) find the radial separation, azimuth and +flux of a point source; (vi) create and forward model scattered-light disk models; +(vii) post-process IFS data and infer the exact astro- and photometry of a given point +source; (viii) use FT-based and interpolation-based methods for different image +operations, and assess their respective performance; and (ix) use the new object-oriented framework for ``VIP``.