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Communication Dans Un Congrès Année : 2019

MaxiMask: Identifying Contaminants in Astronomical Images using Convolutional Neural Networks

Maxime Paillassa
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Emmanuel Bertin
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H. Bouy

Résumé

We present MaxiMask, a contaminant detector for ground-based astronomical images based on convolutional neural networks (CNNs). Once trained, Maxi-Mask is able to detect cosmic rays, hot pixels, bad pixels, saturated pixels, diffraction spikes, nebulous features, persistence effects, satellite trails and residual fringe patterns in ground based images, encompassing a broad range of ambient conditions, PSF sampling, detectors, optics and stellar density. Individual image pixels can be flagged through semantic segmentation, based on high-resolution probability maps generated by MaxiMask for each contaminant, except for the tracking error probability which is assigned by another dedicated CNN. Training and testing data have been gathered from a large dataset of simulated and real data originating from various modern CCD and near-IR cameras.
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Dates et versions

hal-02338648 , version 1 (30-10-2019)

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Maxime Paillassa, Emmanuel Bertin, H. Bouy. MaxiMask: Identifying Contaminants in Astronomical Images using Convolutional Neural Networks. Astronomical Data Analysis Software and Systems XXVIII. ASP Conference Series, Vol. 521, proceedings of a conference held (11-15 October 2018) at The Hotel at the University of Maryland, College Park, Maryland, USA. Edited by Peter J. Teuben, Marc W. Pound, Brian A. Thomas, and Elizabeth M.Warner. San Francisco: Astronomical Society of the Pacific, 2019, p.99, Oct 2018, Maryland, United States. ⟨hal-02338648⟩
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