PAILLASSA, M.; BERTIN, E.; BOUY, H.

doi:10.1051/0004-6361/201936345

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PAILLASSA, M.
Laboratoire d'Astrophysique de Bordeaux [Pessac] [LAB]

BERTIN, E.
Institut d'Astrophysique de Paris [IAP]

BOUY, H.
Laboratoire d'Astrophysique de Bordeaux [Pessac] [LAB]

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Ce document a été publié dans

Astronomy and Astrophysics - A&A. 2020, vol. 634, p. A48

EDP Sciences

Résumé en anglais

In this work, we propose two convolutional neural network classifiers for detecting contaminants in astronomical images. Once trained, our classifiers are able to identify various contaminants, such as cosmic rays, hot and bad pixels, persistence effects, satellite or plane trails, residual fringe patterns, nebulous features, saturated pixels, diffraction spikes, and tracking errors in images. They encompass a broad range of ambient conditions, such as seeing, image sampling, detector type, optics, and stellar density. The first classifier, MaxiMask, performs semantic segmentation and generates bad pixel maps for each contaminant, based on the probability that each pixel belongs to a given contaminant class. The second classifier, MaxiTrack, classifies entire images and mosaics, by computing the probability for the focal plane to be affected by tracking errors. We gathered training and testing data from real data originating from various modern charged-coupled devices and near-infrared cameras, that are augmented with image simulations. We quantified the performance of both classifiers and show that MaxiMask achieves state-of-the-art performance for the identification of cosmic ray hits. Thanks to a built-in Bayesian update mechanism, both classifiers can be tuned to meet specific science goals in various observational contexts.< Réduire

Mots clés en anglais

surveys

methods: data analysis

techniques: image processing

DOI

http://dx.doi.org/10.1051/0004-6361/201936345

Project ANR

Initiative d'excellence de l'Université de Bordeaux - ANR-10-IDEX-0003
FUTURE - ANR-16-IDEX-0003

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Importé de hal

Unités de recherche

Laboratoire d'Astrophysique de Bordeaux (LAB) - UMR 5804