Background
Following on from Tony's scistaff meeting about Astronomy AI institutes in August 2023, we decided it would be good to set up a regular (~monthly) series of meetings on using AI/ML focused especially on VLASS, which, as it produces a large, uniform set of images and other products makes it a good candidate for this sort of work, but also covering other areas e.g. science ready data products, pipeline QA etc, particularly with an eye towards what will be needed for ngVLA.
Aim
To draft a white paper/VLASS memo on these topics (on a timescale ~ 1 year) and, if we find some particularly compelling use cases, to include an implementation plan and an estimate of the resources needed to carry it out.
Resources
There exists a rich literature of astronomical uses of AI/ML, here are some interesting papers/talks that might be relevant:
Image/artifact classification
CIRADA (Canadian Initiative for Radio Astronomy Data Analysis)'s use of Self-organizing maps for their VLASS catalogs: CIRADA catalog users guide
Felix Stoehr's ADASS talk on ALMA archive development "You might also like these images"
Interferometric imaging with machine learning
https://arxiv.org/abs/2311.06349 - Interferometric Image Reconstruction using Closure Invariants and Machine Learning
RFI detection and excision
DSC based Dual-Resunet for radio frequency interference identification