MaxiMask: Identifying Contaminants in Astronomical Images using Convolutional Neural Networks
Language
en
Communication dans un congrès
This item was published in
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, 2018-10-11, Maryland. 2019
English Abstract
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, ...Read more >
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.Read less <
Origin
Hal imported