Georges Meylan

Biographie

Après une thèse en astrophysique, en 1985, à l'Observatoire astronomique de l'Université de Genève, Georges Meylan séjourne en tant que postdoc à l'Université de Californie à Berkeley, USA, et au siège de l'Observatoire Européen Austral (ESO) à Munich, Allemagne. Il occupe ensuite des positions d'astronome senior à l'ESO à Munich et au Space Telescope Science Institute (STScI) à Baltimore, USA. Depuis août 2004, il occupe la chaire d'astrophysique de l'EPFL.

Sa recherche porte sur la cosmologie observationnelle, incluant le phénomène de lentilles gravitationnelles, les quasars et leurs galaxies hôtes, la formation et l'évolution des galaxies tout au long de l'évolution de l'Univers, la dynamique stellaire et l'étude des populations stellaires proches et lointaines.

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https://people.epfl.ch/georges.meylan?lang=fr

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Cosmological constraints from key probes of the Euclid imaging survey rely critically on the accurate determination of the true redshift distributions, n(z); of tomographic redshift bins. We determine whether the mean redshift, < z >, of ten Euclid tomographic redshift bins can be calibrated to the Euclid target uncertainties of sigma(< z >) < 0:002 (1 + z) via cross-correlation, with spectroscopic samples akin to those from the Baryon Oscillation Spectroscopic Survey (BOSS), Dark Energy Spectroscopic Instrument (DESI), and Euclid's NISP spectroscopic survey. We construct mock Euclid and spectroscopic galaxy samples from the Flagship simulation and measure small-scale clustering redshifts up to redshift z < 1 :8 with an algorithm that performs well on current galaxy survey data. The clustering measurements are then fitted to two n(z) models: one is the true n(z) with a free mean; the other a Gaussian process modified to be restricted to non-negative values. We show that < z > is measured in each tomographic redshift bin to an accuracy of order 0.01 or better. By measuring the clustering redshifts on subsets of the full Flagship area, we construct scaling relations that allow us to extrapolate the method performance to larger sky areas than are currently available in the mock. For the full expected Euclid, BOSS, and DESI overlap region of approximately 6000 deg(2), the uncertainties attainable by clustering redshifts exceeds the Euclid requirement by at least a factor of three for both n(z) models considered, although systematic biases limit the accuracy. Clustering redshifts are an extremely effective method for redshift calibration for Euclid if the sources of systematic biases can be determined and removed, or calibrated out with sufficiently realistic simulations. We outline possible future work, in particular an extension to higher redshifts with quasar reference samples.

EDP SCIENCES S A2023

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EAS 2022 takes positive steps forward for sustainable astronomy

Georges Meylan

2022

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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%.

EDP SCIENCES S A2022

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