Astrometric and photometric accuracies in high contrast imaging: The SPHERE speckle calibration tool (SpeCal)

Abstract

Context. The consortium of the Spectro-Polarimetric High-contrast Exoplanet REsearch installed at the Very Large Telescope (SPHERE/VLT) has been operating its guaranteed observation time (260 nights over five years) since February 2015. The main part of this time (200 nights) is dedicated to the detection and characterization of young and giant exoplanets on wide orbits. Aims. The large amount of data must be uniformly processed so that accurate and homogeneous measurements of photometry and astrometry can be obtained for any source in the field. Methods. To complement the European Southern Observatory pipeline, the SPHERE consortium developed a dedicated piece of software to process the data. First, the software corrects for instrumental artifacts. Then, it uses the speckle calibration tool (SpeCal) to minimize the stellar light halo that prevents us from detecting faint sources like exoplanets or circumstellar disks. SpeCal is meant to extract the astrometry and photometry of detected point-like sources (exoplanets, brown dwarfs, or background sources). SpeCal was intensively tested to ensure the consistency of all reduced images (cADI, Loci, TLoci, PCA, and others) for any SPHERE observing strategy (ADI, SDI, ASDI as well as the accuracy of the astrometry and photometry of detected point-like sources. Results. SpeCal is robust, user friendly, and efficient at detecting and characterizing point-like sources in high contrast images. It is used to process all SPHERE data systematically, and its outputs have been used for most of the SPHERE consortium papers to date. SpeCal is also a useful framework to compare different algorithms using various sets of data (different observing modes and conditions). Finally, our tests show that the extracted astrometry and photometry are accurate and not biased.

HD 117214 debris disk: scattered-light images and constraints on the presence of planets

“…To subtract the stellar light, we used the SpeCal pipeline (Galicher et al 2018), which provides several algorithms based on angular differential imaging (ADI; Marois et al 2006) such as classical ADI (cADI), Principal Component Analysis (PCA; Soummer et al 2012;Amara & Quanz 2012), and Template Locally Optimized Combination of Images (TLOCI; Marois et al 2014), which were used to process both the IRDIS and IFS datasets. Figure 1 shows the cADI images of the K1 band data (left panel) and spectrally combined IFS data (right panel).…”

SPHERE view of the jet and the envelope of RY Tauri

“…All IFS and IRDIS data sets were reduced with the SPHERE Data Reduction and Handling (DRH) pipeline (Pavlov et al 2008) in the SPHERE Data Center (DC 1 , Delorme et al 2017). The fully reduced IFS images are combined to four-dimensional data cubes (two spatial, one spectral, and one time dimension) and further processed with the SHINE Specal pipeline (Galicher et al 2018). The latter performs angular and spectral differential imaging (ADI+SDI) in the flavors of classical ADI (cADI, Marois et al 2006), Template Locally Optimized Combination of Images (TLOCI, Marois et al 2014), and Principal Component Analysis (PCA, Soummer et al 2012;Amara et al 2015).…”

The B-Star Exoplanet Abundance Study: a co-moving 16–25 MJup companion to the young binary system HIP 79098

“…We reduced the SPHERE data with the SpeCal pipeline (Galicher et al 2018) within the SPHERE Data Center (Delorme et al 2017) framework. The field rotation during the observation was <1 deg, so angular differential imaging (ADI) cannot be efficiently used.…”