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Personne# Jean-Luc Starck

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Hakim Atek, Gianluca Castignani, Frédéric Courbin, Marcello Farina, Maurizio Martinelli, Richard Massey, Georges Meylan, Austin Chandler Peel, Jean-Luc Starck, Yi Wang

Context. The Euclid mission is expected to discover thousands of z > 6 galaxies in three deep fields, which together will cover a similar to 50 deg(2) area. However, the limited number of Euclid bands (four) and the low availability of ancillary data could make the identification of z > 6 galaxies challenging. Aims. In this work we assess the degree of contamination by intermediate-redshift galaxies (z = 1-5.8) expected for z > 6 galaxies within the Euclid Deep Survey. Methods. This study is based on similar to 176 000 real galaxies at z = 1-8 in a similar to 0.7 deg(2) area selected from the UltraVISTA ultra-deep survey and similar to 96 000 mock galaxies with 25.3 6 recovery of 91% (88%) for bright (faint) galaxies. For the UltraVISTA-like bright sample, the percentage of z = 1-5.8 contaminants amongst apparent z > 6 galaxies as observed with Euclid alone is 18%, which is reduced to 4% (13%) by including ultra-deep Rubin (Spitzer) photometry. Conversely, for the faint mock sample, the contamination fraction with Euclid alone is considerably higher at 39%, and minimised to 7% when including ultra-deep Rubin data. For UltraVISTA-like bright galaxies, we find that Euclid (I-E - Y-E) > 2:8 and (Y-E - J(E)) < 1.4 colour criteria can separate contaminants from true z > 6 galaxies, although these are applicable to only 54% of the contaminants as many have unconstrained (I-E - Y-E) colours. In the best scenario, these cuts reduce the contamination fraction to 1% whilst preserving 81% of the fiducial z > 6 sample. For the faint mock sample, colour cuts are infeasible; we find instead that a 5 sigma detection threshold requirement in at least one of the Euclid near-infrared bands reduces the contamination fraction to 25%.

Jean-Paul Richard Kneib, Huanyuan Shan, Jean-Luc Starck

Aims. With the next generation of large surveys poised to join the ranks of observational cosmology in the near future, it is important to explore their potential synergies and to maximize their scientific outcomes. In this study, we aim to investigate the complementarity of two upcoming space missions: Euclid and the China Space Station Telescope (CSST), both of which will be focused on weak gravitational lensing for cosmology. In particular, we analyze the photometric redshift (photo-z) measurements by combining NUV, 2006;gy bands from CSST with the VIS, Y,2006;J,2006;H bands from Euclid, and other optical bands from the ground-based Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) and Dark Energy Survey. We also consider the advantages of combining the two space observational data in simplifying image deblending. For Euclid, weak lensing measurements use the broad optical wavelength range of 550-900 nm, for which chromatic point-spread function (PSF) effects are significant. For this purpose, the CSST narrow-band data in the optical can provide valuable information for Euclid to obtain more accurate PSF measurements and to calibrate the color and color-gradient biases for galaxy shear measurements. Methods. We created image simulations, using the Hubble Deep UV data as the input catalog, for different surveys and quantified the photo-z performance using the EAZY template fitting code. For the blending analyses, we employed high-resolution HST-ACS CANDELS F606W and F814W data to synthesize mock simulated data for Euclid, CSST, and an LSST-like survey. We analyzed the blending fraction for different cases as well as the blending effects on galaxy photometric measurements. Furthermore, we demonstrated that CSST can provide a large enough number of high signal-to-noise ratio multi-band galaxy images to calibrate the color-gradient biases for Euclid. Results. The sky coverage of Euclid lies entirely within the CSST footprint. The combination of Euclid with the CSST data can thus be done more uniformly than with the various ground-based data that are part of the Euclid survey. Our studies show that by combining Euclid and CSST, we can reach a photo-z precision of sigma(NMAD)0.04 and an outlier fraction of eta 2.4% at the nominal depth of the Euclid Wide Survey (VIS24.5 AB mag). For CSST, including the Euclid Y,& 2006;J,& 2006;H bands reduces the overall photo-z outlier fraction from similar to 8.5% to 2.4%. For z & 2004;>& 2004;1, the improvements are even more significant. Because of the similarly high resolutions, the data combination of Euclid and CSST can be relatively straightforward for photometry measurements. On the other hand, to include ground-based data, sophisticated deblending utilizing priors from high-resolution space observations are required. The multi-band data from CSST are very helpful in controlling the chromatic PSF effect for Euclid VIS shear measurements. The color-gradient bias for Euclid galaxies with different bulge-to-total flux ratio at different redshifts can be well calibrated to the level of 0.1% using galaxies from the CSST deep survey.

Utsav Akhaury, Frédéric Courbin, Pascale Jablonka, Jean-Luc Starck

With the onset of large-scale astronomical surveys capturing millions of images, there is an increasing need to develop fast and accurate deconvolution algorithms that generalize well to different images. A powerful and accessible deconvolution method would allow for the reconstruction of a cleaner estimation of the sky. The deconvolved images would be helpful to perform photometric measurements to help make progress in the fields of galaxy formation and evolution. We propose a new deconvolution method based on the Learnlet transform. Eventually, we investigate and compare the performance of different Unet architectures and Learnlet for image deconvolution in the astrophysical domain by following a two-step approach: a Tikhonov deconvolution with a closed-form solution, followed by post-processing with a neural network. To generate our training dataset, we extract HST cutouts from the CANDELS survey in the F606W filter (V-band) and corrupt these images to simulate their blurred-noisy versions. Our numerical results based on these simulations show a detailed comparison between the considered methods for different noise levels.