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ShapeNet: Shape Constraint for Galaxy Image Deconvolution
F. Nammour, U. Akhaury, J. N. Girard +4
Deep Learning (DL) has shown remarkable results in solving inverse problems in various domains. In particular, the Tikhonet approach is very powerful to deconvolve optical astronom…
Deep Learning for space-variant deconvolution in galaxy surveys
Florent Sureau, Alexis Lechat, Jean-Luc Starck
Deconvolution of large survey images with millions of galaxies requires to develop a new generation of methods which can take into account a space variant Point Spread Function (PS…
Representation learning for automated spectroscopic redshift estimation
Joana Frontera-Pons, Florent Sureau, Bruno Moraes +2
Determining the radial positions of galaxies up to a high accuracy depends on the correct identification of salient features in their spectra. Classical techniques for spectroscopi…
Learning sparse representations on the sphere
Florent Sureau, Felix Voigtlaender, Malte Wust +2
Many representation systems on the sphere have been proposed in the past, such as spherical harmonics, wavelets, or curvelets. Each of these data representations is designed to ext…
Unsupervised feature-learning for galaxy SEDs with denoising autoencoders
Joana Frontera-Pons, Florent Sureau, Jerome Bobin +1
With the increasing number of deep multi-wavelength galaxy surveys, the spectral energy distribution (SED) of galaxies has become an invaluable tool for studying the formation of t…
Planck 2013 results. IX. HFI spectral response
Planck Collaboration, P. A. R. Ade, N. Aghanim +217
The Planck High Frequency Instrument (HFI) spectral response was determined through a series of ground based tests conducted with the HFI focal plane in a cryogenic environment pri…
Planck 2013 results. V. LFI calibration
Planck Collaboration, N. Aghanim, C. Armitage-Caplan +217
We discuss the methods employed to photometrically calibrate the data acquired by the Low Frequency Instrument on Planck. Our calibration is based on a combination of the Orbital D…