Astrometric and photometric accuracies in high contrast imaging: The SPHERE speckle calibration tool (SpeCal)
R. Galicher, A. Boccaletti, D. Mesa, P. Delorme, R. Gratton, M., Langlois, A.-M. Lagrange, A.-L. Maire, H. Le Coroller, G. Chauvin, B. Biller,, F. Cantalloube, M. Janson, E. Lagadec, N. Meunier, A. Vigan, J. Hagelberg, M., Bonnefoy, A. Zurlo, S. Rocha, D. Maurel, M. Jaquet

TL;DR
This paper introduces SpeCal, a software tool developed for the SPHERE instrument at VLT, which improves the detection and characterization of faint exoplanets and sources by calibrating speckle noise and ensuring accurate astrometry and photometry.
Contribution
The paper presents SpeCal, a dedicated, robust software for processing high contrast imaging data, ensuring consistent, accurate measurements across various observing strategies and conditions.
Findings
SpeCal effectively minimizes stellar halo, enhancing faint source detection.
Astrometry and photometry extracted by SpeCal are accurate and unbiased.
SpeCal is widely used in SPHERE data analysis and comparison of algorithms.
Abstract
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. 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. 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
