Machine-learning methods for weak lensing analysis of the ESA Euclid sky survey

A clear picture has emerged from the last three decades of research: our Universe is expanding at an accelerated rate. The cause of this expansion remains elusive, but in essence acts as a repulsive force. This so-called dark energy represents about 69% of the energy content in the Universe. A further 26% of the energy is contained in dark matter, a form of matter that is invisible electromagnetically. Understanding the nature of these two major components of the Universe is at the top of the list of unsolved problems. To unveil answers, ambitious experiments are devised to survey an ever larger and deeper fraction of the sky. One such project is the European Space Agency (ESA) telescope Euclid, which will probe dark matter and infer desperately needed information about dark energy. Because light bundles follow null geodesics, their trajectories are affected by the mass distribution along the line of sight, which includes dark matter. This is gravitational lensing. In the vast majority of cases, deformations of the source objects are weak, and profiles are slightly sheared. The nature of the dark components can be fathomed by measuring the shear over a large fraction of the sky. The shear can be recovered by a statistical analysis of a large number of objects. In this thesis, we take on the development of the necessary tools to measure the shear. Shear measurement techniques have been developed and improved for more than two decades. Their performance, however, do not meet the unprecedented requirements imposed by future surveys. Requirements trickle down from the targeted determination of the cosmological parameters. We aim at preparing novel and innovative methods. These methods are tested against the Euclid requirements. Contributions can be classified into two major themes. A key step in the processing of weak gravitational lensing data is the correction of image deformations generated by the instrument itself. This point spread function (PSF) correction is the first theme. The second is the shear measurement itself, and in particular, producing accurate measurements. We explore machine-learning methods, and notably artificial neural networks. These methods are, for the most part, data-driven. Schemes must first be trained against a representative sample of data. Crafting optimal training sets and choosing the method parameters can be crucial for the performance. We dedicate an important fraction of this dissertation to describing simulations behind the datasets and motivating our parameter choices. We propose schemes to build a clean selection of stars and model the PSF to the Euclid requirements in the first part of this thesis. Shear measurements are notoriously biased because of their small size and their low intensity. We introduce an approach that produces unbiased estimates of shear. This is achieved by processing data from any shape measurement technique with artificial neural networks, and predicting corrected estimates of the shape of the galaxies, or directly the shear. We demonstrate that simple networks with simple trainings are sufficient to reach the Euclid requirements on shear measurements

The CHEOPS mission

The CHaracterising ExOPlanet Satellite (CHEOPS) was selected in 2012, as the first small mission in the ESA Science Programme and successfully launched in December 2019. CHEOPS is a partnership between ESA and Switzerland with important contributions by ten additional ESA Member States. CHEOPS is the first mission dedicated to search for transits of exoplanets using ultrahigh precision photometry on bright stars already known to host planets. As a follow-up mission, CHEOPS is mainly dedicated to improving, whenever possible, existing radii measurements or provide first accurate measurements for a subset of those planets for which the mass has already been estimated from ground-based spectroscopic surveys and to following phase curves. CHEOPS will provide prime targets for future spectroscopic atmospheric characterisation. Requirements on the photometric precision and stability have been derived for stars with magnitudes ranging from 6 to 12 in the V band. In particular, CHEOPS shall be able to detect Earth-size planets transiting G5 dwarf stars in the magnitude range between 6 and 9 by achieving a photometric precision of 20 ppm in 6 hours of integration. For K stars in the magnitude range between 9 and 12, CHEOPS shall be able to detect transiting Neptune-size planets achieving a photometric precision of 85 ppm in 3 hours of integration. This is achieved by using a single, frame-transfer, back-illuminated CCD detector at the focal plane assembly of a 33.5 cm diameter telescope. The 280 kg spacecraft has a pointing accuracy of about 1 arcsec rms and orbits on a sun-synchronous dusk-dawn orbit at 700 km altitude. The nominal mission lifetime is 3.5 years. During this period, 20% of the observing time is available to the community through a yearly call and a discretionary time programme managed by ESA.Comment: Submitted to Experimental Astronom