Page History

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 . and outreach to possible partners from the NSF's AI institute effort.

Resources

There exists a rich literature of astronomical uses of AI/ML, here are some interesting papers that staff have noticed recently:

Image/artifact classification

/talks that might be relevant. See also this set of PASP papers on machine learning in astronomy

Image and 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"

Brian Kent's demo for the 2023 June AAS splinter session, classifying VLASS sources with a neural network

Point source detection: DEEPSOURCE: point source detection using deep learning

Interferometric imaging with machine learning

https://arxiv.org/abs/2311.06349 - Interferometric Image Reconstruction using Closure Invariants and Machine Learning

Deep radio-interferometric imaging with POLISH: DSA-2000 and weak lensing (Connor et al. 2022; ADS)

First AI for Deep Super-resolution Wide-field Imaging in Radio Astronomy: Unveiling Structure in ESO 137-006 (Dabbech et al. 2022; ADS); R2D2 algorithm https://arxiv.org/pdf/2403.05452

3D detection and characterization of ALMA sources through deep learning ADS Github

ESO's BRAIN study (ALMA dev. program) ArXiv