Zenodo repository: The repository containing the 8 challenge datasets and the 1 training dataset can be found here.

Instruments

Datasets from two high-contrast spectro-imagers of the latest generation are used:

• VLT/SPHERE-IFS, Beuzit et al., 2019
• Gemini-S/GPI, Macintosh et al., 2008

There are 4 datasets from SPHERE-IFS and 4 datasets from GPI.
The training dataset is from SPHERE-IFS.

The 8 target stars contain no known companion candidate. In order to mitigate the impact of a potential astrophysical signal (planet or disk), we performed the injection using the opposite parallactic angles. This trick preserves the temporal correlation of the starlight residuals, but any pre-existing circumstellar signal is no longer co-aligned after derotation. The opposite parallactic angles are the ones used for the injections and provided with each downloadable dataset.

The datasets are taken under various observing conditions, from very favorable to bad, with bright or faint targets (see details below).

The data have been pre-reduced by the official IFS (SPHERE-DC) and GPI pipelines (GPIES Data Cruncher). Our team homogenized the data (centering, cropping etc.) and injected between 2 and 3 synthetic planetary signals in the coronagraphic image cubes (see details below).

Provided data content

The 8 data set of the data challenge contain the following files (in .fits format):

• image_cube_instID.fits: Spectral temporal coronagraphic image cube (4D array);
• parallactic_angles_instID.fits: Vector of parallactic angles and airmass variation (2D vector array);
• wavelength_vect_instID.fits: Vector of the central wavelength of each channel of the spectro-imager (1D vector array);
• psf_cube_instID.fits: Spectral non-coronagraphic image cube of the star (3D array);
• first_guess_astrometry_instID.fits: First guess position of the injected signals (between 2 and 3 vectors);
  where inst is either sphere or gpi (lower case) and ID the index of the data set between 0 and 4.

The images (coronagraphic and non-coronagraphics) are withing a frame with an odd number of pixels, centered on the central pixel. If Npix is the dimension of the frame, it means that the center is located at (Npix-1)/2.

The first guess gives an estimation of the location of the injected planetary signal within a radius of 5 pixels (in case of low SNR). The values are given as the distance (in pixels) from the star (the center of the frame) in cartesian coordinates (x,y).

The airmass is given as an information, it is not mandatory to take it into account within the algorithm used.

More information (telescope, instrument, coronagraph type, effective telescope diameter, total field rotation, spectral resolving power, central wavelength, exposure time DIT, number of exposures NDIT, pixel scale, seeing etc.) are written in the header, when available.

Observing conditions

The table below summarizes the main information about the data set and the observing conditions:

Injection procedure

In each data set we observed from 2 to 3 synthetic exoplanet (point source) signals. Within the SPHERE-IFS images we injected 11 exoplanet signals, and within the GPI data 10 exoplanet signals. In total there are 21 exoplanet signals to be characterized.

As shown in the injection procedure tutorial, based on VIP pipeline procedures, the exoplanet signal injection relies on the following steps:

• Choosing the subpixel position of the injected planet in the 1st frame of the image cube;
• Choosing the mean contrast accross wavelengths of the injected planet mean_contrast_planet;
• Choosing a template spectrum shape for the injected planet spectrum_planet (e.g. blackbody, atmospheric model, best fit etc.);
• Fitting the stellar spectrum measured from the non-coronagraphic image fitted_stellar_spectrum (for the contrast normalization);
• Grabbing a stellar spectrum SED model model_stellar_spectrum (to estimate the transmission of the atmosphere and the instrument);
• Computing the airmass factor to be applied to the planetary signal through time airmass_factor;
• Injecting the planet signal, using the inject_fcp_ifs function of VIP.
  The injection function takes as an input: the multispectral image cube in which the injection is made, the normalized non-coronagraphic PSF used as a planetary signal model, the parallactic angle variation, the position, spectrum and mean contrast of the injection, the fitted stellar spectrum, the model SED stellar spectrum, the instrument spectral resolving power and the airmass variation through time.

The injected signal at a given wavelength (lambda) and position then writes: Injected_planet_lambda = PSF_lambda * spectrum_planet * mean_contrast_planet * transmission_AtmInstr * airmass_factor, where transmission_AtmInstr = fitted_stellar_spectrum / model_stellar_spectrum .

No other effect is taken into account for the injection: no other flux temporal variation (intrinsic nor instrumental), no smearing at large separation due to the exposure time, no temporal binning, no off-centering of the star behind the coronagraph during the exposure, no diffraction effect due to the coronagraph at close separation etc.

Training data set: On the Zenodo repository containing the data, you will find a data set annoted sphere0. This is the training data set (empty of exoplanetary signals). On the Github repository containing the toolkit, you will find a tutorial showing our planet injection procedure within this example SPHERE-IFS data set. This tutorial makes use of 2 planet spectra (in folder /planet_spectra/) to be injected and uses a given stellar spectra (in folder /stellar_spectra/) to compute a mean contrast. This tutorial contains a part to visualise the input data and one to process the data for a quick-look using a full frame ASDI PCA. Feel free to use this training set to refine your algorithm use.

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Data and pre-reduction team:

• J.-B. Ruffio (Caltech, USA), on behalf of GPIES collaboration: Gemini-S/GPI data
• P. Delorme (IPAG, France), on behalf of the SPHERE Data Center team: VLT/SPHERE-IFS data

Injection of planetary signals, homogenization and test team:

• Faustine Cantalloube (LAM, France)
• Carles Cantero (ULiège, Belgium)
• Valentin Christiaens (ULiège, Belgium)