Exploring the Deep Universe by Computational Analysis of Data from Observations

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The formation and evolution of massive galaxies is reasonably well understood in the context of the successful standard lambda-CDMformalism. Such simulations of cosmic evolution, however, lead to serious challenges in the regime of the very faint galaxies,including the problems referred to as missing satellites, too big to fail, and planes of satellite galaxies. With the massive amounts ofexcellent data being produced by astronomical surveys, and with new missions scheduled to produce more data of even betterquality, we have a unique chance to solve these problems. To do this, we require innovative developments in information technology.In EDUCADO (Exploring the Deep Universe by Computational Analysis of Data from Observations), an intensive collaboration at theintersection of astronomy and computer science, we bring together experts from different disciplines and sectors. We will train 10Doctoral Candidates in the development of a variety of high-quality methods, needed to address the formation of the fainteststructures. We will reliably and reproducibly detect unprecedented numbers of the faintest observable galaxies from new large-areasurveys. We will study the morphology, populations, and distribution of large samples of various classes of dwarf galaxies andcompare dwarf galaxy populations and properties across different environments. We will confront the results with cosmologicalmodels of galaxy formation and evolution. Finally, we will perform detailed, principled, and robust simulations and observations ofthe Milky Way and the Local Group to compare with dwarf galaxies in other environments. EDUCADO will deliver a comprehensiveinterdisciplinary, intersectoral, and international training programme including a secondment at one of our 11 associated partners foreach DC. We will provide a fresh and sustainable way of training PhD scientists with interdisciplinary and intersectoral data scienceexpertise, a requisite for future European competitiveness.