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title tags authors affiliations date bibliography
VIP: A Python package for high-contrast imaging
Python
astronomy
exoplanets
high-contrast
direct imaging
name orcid affiliation
Valentin Christiaens
0000-0002-0101-8814
1
name orcid affiliation
Carlos Gomez Gonzalez
TO BE FILLED
2
name orcid affiliation
friends
TO BE FILLED
3
name orcid affiliation
Arthur Vigan
0000-0002-5902-7828
4
name index
Space sciences, Technologies & Astrophysics Research (STAR) Institute, Université de Liège, Belgium
1
name index
TO BE FILLED
2
name index
More top-notch institutes
3
name index
Aix Marseille Univ, CNRS, CNES, LAM, Marseille, France
4
4 May 2022
paper.bib

Summary

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 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 and characterization of directly imaged exoplanets, circumstellar disks, and stellar environments*

Statement of need

VIP stands for Vortex Image Processing. It is a collaborative project which started within the Vortex group at the University of Liège, and 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);

  • modeling and subtracting the stellar PSF using state-of-the-art algorithms leveraging angular differential imaging (ADI), reference star differential imaging (RDI), or spectral differential imaging (SDI) observing strategies [@Racine:1999; @Sparks:2002; @Marois:2006] for diversity between speckle and authentic circumstellar astrophysical signals (psfsub module);

  • characterizing either point sources or extended circumstellar signals through forward modeling (fm module);

  • detecting and characterizing point sources through inverse approaches (invprob module);

  • assessing the achieved contrast in PSF-subtracted images, automatically 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 [@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], 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]. Nonetheless, given the steady expansion of VIP, a new publication is in order to summarize all novelties that were brought to the package over the past 5 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 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, 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 HCIPostProcAlgo class and different subclasses inheriting from it have been defined to facilitate an object-oriented use of VIP routines.

Some of the major changes in each module of VIP are summarized below:

  • fm:

    • new routines were added to create parametrizable scattered-light disk 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];
    • the NEGFC methods are now fully compatible with integral field spectrograph (IFS) input datacubes.

  • invprob:

    • a Python implementation of the ANDROMEDA algorithm [@Cantalloube:2015] is now available as part of VIP ;
    • the FMMF algorithm [@Pueyo:2016] is now also available in the invprob module.

  • metrics:

    • calculation of standardized trajectory maps (STIM) is now available [@Pairet:2019];

  • preproc:

    • the module now boasts (not sure "boast" is the right verb here) several new algorithms for (i) the identification 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 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 based on the cross-correlation of the speckle pattern, after appropriate filtering and log-scaling of pixel intensities Gary: which paper should I cite?

  • psfsub:

    • all principal component analysis (PCA) based routines [@Amara:2012; @Soummer:2012] have been re-written for improved efficiency, and are now also compatible with 4D IFS+ADI input cubes to apply SDI-based PSF modeling and subtraction algorithms;
    • an implementation of the Locally Optimal Combination of Images [@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 support median-SDI.

We refer the interested reader to release descriptions and announcements in the Discussion section of the GitHub for a more complete list of all changes, including improvements not mentioned in the above summary.

Two major convention updates are also to be noted in VIP. All image operations (rotation, scaling, resampling and sub-pixel shifts) are now performed using Fourier-Transform (FT) based methods by default. These have been implemented as low-level routines in the preproc module. FT-based methods significantly outperform interpolation-based methods in terms of flux conservation [@Larkin:1997]. However, given the order of magnitude slower computation of FT-based image rotations, the option to use interpolation-based methods is still available in all relevant VIP functions. The second change of convention concerns the assumed center for even-size images, which is now 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 map [@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 new object-oriented framework for VIP.

Acknowledgements

An up-to-date list of contributors to VIP is available at VIP_contributors. VC acknowledges financial support from the Belgian F.R.S.-FNRS.

References