The ADACS TAC has selected three projects for support this semester:
An automated data reduction pipeline for AAO Data Central – CI: Simon O’Toole
Galaxy and black hole co evolution survey using active machine learning – CI: Julie Banfield
In addition to a significant improvement in understanding of the physics of co-evolution, we expect that the same software suite can be re-used for other scientific questions. This means that the developed software will be a key part of future astronomical surveys.
GPU acceleration of gravitational-wave signal models – CI: Rory Smith
Searching for gravitational waves using parameter estimation tools would allow LIGO to search a much larger volume of the observable universe. Additionally, we expect to be able to detect the “stochastic background” of gravitational waves using only a day of data, compared to around 40 months of data with current techniques. Detection of this background would be a big astronomical prize, allowing the study of the whole population of binary black holes and neutron stars, as well as having cosmological implications.
The project will produce a GPU parallelized implementation of a commonly used gravitational- wave signal model known as “IMRPhenomP”. This implementation would allow us to benchmark expected performance improvements to current parameter estimation tools. Looking further, the implementation would serve as a corner stone of a fully GPU-implemented Monte Carlo algorithm, the core algorithm used in parameter estimation.
Congratulations to all successful applicants; thanks and commiserations to all who applied but weren’t selected.