Exploring the Deep Universe by Computational Analysis of Data from Observations
Lead Research Organisation:
University of Birmingham
Department Name: School of Computer Science
Abstract
The formation and evolution of massive galaxies is reasonably well understood in the context of the successful standard lambda-CDM
formalism. 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 of
excellent data being produced by astronomical surveys, and with new missions scheduled to produce more data of even better
quality, 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 the
intersection of astronomy and computer science, we bring together experts from different disciplines and sectors. We will train 10
Doctoral Candidates in the development of a variety of high-quality methods, needed to address the formation of the faintest
structures. We will reliably and reproducibly detect unprecedented numbers of the faintest observable galaxies from new large-area
surveys. We will study the morphology, populations, and distribution of large samples of various classes of dwarf galaxies and
compare dwarf galaxy populations and properties across different environments. We will confront the results with cosmological
models of galaxy formation and evolution. Finally, we will perform detailed, principled, and robust simulations and observations of
the Milky Way and the Local Group to compare with dwarf galaxies in other environments. EDUCADO will deliver a comprehensive
interdisciplinary, intersectoral, and international training programme including a secondment at one of our 11 associated partners for
each DC. We will provide a fresh and sustainable way of training PhD scientists with interdisciplinary and intersectoral data science
expertise, a requisite for future European competitiveness.
formalism. 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 of
excellent data being produced by astronomical surveys, and with new missions scheduled to produce more data of even better
quality, 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 the
intersection of astronomy and computer science, we bring together experts from different disciplines and sectors. We will train 10
Doctoral Candidates in the development of a variety of high-quality methods, needed to address the formation of the faintest
structures. We will reliably and reproducibly detect unprecedented numbers of the faintest observable galaxies from new large-area
surveys. We will study the morphology, populations, and distribution of large samples of various classes of dwarf galaxies and
compare dwarf galaxy populations and properties across different environments. We will confront the results with cosmological
models of galaxy formation and evolution. Finally, we will perform detailed, principled, and robust simulations and observations of
the Milky Way and the Local Group to compare with dwarf galaxies in other environments. EDUCADO will deliver a comprehensive
interdisciplinary, intersectoral, and international training programme including a secondment at one of our 11 associated partners for
each DC. We will provide a fresh and sustainable way of training PhD scientists with interdisciplinary and intersectoral data science
expertise, a requisite for future European competitiveness.
Organisations
People |
ORCID iD |
| Peter Tino (Principal Investigator) |