Foundation Models for Radio Astronomy
Jul 28, 2023
Speakers
Anna Scaife
Speaker · 0 followers
About
The Square Kilometre Array (SKA) will be the world's largest radio telescope, producing data volumes approaching exa-scale within a few years of operation. Extracting scientific value from those data in a timely manner will be a challenge that quickly goes beyond traditional analyses and instead requires robust domain-specific AI solutions. Here I will discuss how we have been building foundation models that can be adapted across different SKA precursor instruments, by applying self-supervised learning with instance differentiation to learn a multi-purpose representation for use in radio astronomy. For a standard radio astronomy use case, our models exceed baseline supervised classification performance by a statistically significant margin for most label volumes in the in-distribution classification case and for all label volumes in the out-of-distribution case. I will also show how such learned representations can be more widely scientifically useful, for example in similarity searches that allow us to find hybrid radio galaxies without any pre-labelled examples.The Square Kilometre Array (SKA) will be the world's largest radio telescope, producing data volumes approaching exa-scale within a few years of operation. Extracting scientific value from those data in a timely manner will be a challenge that quickly goes beyond traditional analyses and instead requires robust domain-specific AI solutions. Here I will discuss how we have been building foundation models that can be adapted across different SKA precursor instruments, by applying self-supervised l…
Like the format? Trust SlidesLive to capture your next event!
Professional recording and live streaming, delivered globally.
Sharing
Recommended Videos
Presentations on similar topic, category or speaker
FedDisco: Federated Learning with Discrepancy-Aware Collaboration
Watch later
Rui Ye, …
Towards (Truly) Private and Communication-efficient Federated Learning
Watch later
Generalization Theory for Robot Learning
Watch later
Understanding the Role of Feedback in Online Learning with Switching Costs
Watch later
Duo Cheng, …
Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs
Watch later
Lirong Wu, …