Grant Count
3
Highest Funding Involved
$935,735.00
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No related researchers have been discovered for organisation Flatiron Institute.
Start Date: 11-2021
End Date: 11-2024
Amount: $405,000.00
Summary: Wobbling stars reveal their hidden companions. This project aims to measure the wobble in the position of distant stars that is caused by massive objects, using telescopes in space. This project expects to generate new knowledge on how binary stars, exoplanets, and stellar mass black holes are formed. Expected outcomes of this project include tight constraints on binary star models, new discoveries of neutron stars and black holes that are a few times more massive than the Sun, and samples of st .... Wobbling stars reveal their hidden companions. This project aims to measure the wobble in the position of distant stars that is caused by massive objects, using telescopes in space. This project expects to generate new knowledge on how binary stars, exoplanets, and stellar mass black holes are formed. Expected outcomes of this project include tight constraints on binary star models, new discoveries of neutron stars and black holes that are a few times more massive than the Sun, and samples of stars that do, and do not, host exoplanets. This should provide significant benefits including a catalogue of companion properties for billions of sources, new understanding of how stars die, as well as the first control sample of stars without planets to help understand how and why planets form. Read more Read less
Funder: Australian Research Council
View Funded ActivityStart Date: 04-2020
End Date: 03-2024
Amount: $935,735.00
Summary: Shining gravitational waves on binary astrophysics. This project aims to take advantage of the growing data set of gravitational-wave observations, which ushered in a new field of gravitational-wave astronomy, to answer fundamental questions in astrophysics. This project will combine state-of-the art theoretical modelling with innovative machine learning techniques in order to explore how the Universe makes merging black holes and neutron stars, and what they tell us about the lives and deaths .... Shining gravitational waves on binary astrophysics. This project aims to take advantage of the growing data set of gravitational-wave observations, which ushered in a new field of gravitational-wave astronomy, to answer fundamental questions in astrophysics. This project will combine state-of-the art theoretical modelling with innovative machine learning techniques in order to explore how the Universe makes merging black holes and neutron stars, and what they tell us about the lives and deaths of the most elusive but incredibly important massive stars. This will strengthen Australia's role in the emerging field of gravitational-wave astronomy and provide broad benefits through transferrable machine learning techniques, collaboration building, and big data training. Read more Read less
Funder: Australian Research Council
View Funded ActivityStart Date: 06-2020
End Date: 08-2023
Amount: $405,000.00
Summary: Quantum-Inspired Machine Learning. This project aims to develop new machine learning techniques based around the close correspondence between neural networks used in deep learning, and tensor networks used in quantum physics. Tensor networks are a form of information compression that is useful in machine learning to construct a compact representation of a large data set in a way that is more amenable to understanding the internal structure than a deep neural network. Expected outcomes of this pr .... Quantum-Inspired Machine Learning. This project aims to develop new machine learning techniques based around the close correspondence between neural networks used in deep learning, and tensor networks used in quantum physics. Tensor networks are a form of information compression that is useful in machine learning to construct a compact representation of a large data set in a way that is more amenable to understanding the internal structure than a deep neural network. Expected outcomes of this project include more resilient algorithms for machine learning, and new ways to represent quantum states that will impact fundamental physics. The resulting benefits include enhanced capacity for cross-discipline collaboration, and improved methods for future industrial applications. Read more Read less
Funder: Australian Research Council
View Funded Activity