Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,

Algorithmic VLBI Baseline

Selection Sasha Safonova

Victor Pankratius, Michael Gowanlock, Justin Li MIT Haystack Observatory REU

11 August 2016

Image credit: ESO/B. Tafreshi http://www.eso.org/public/images/potw1253a/

What are VLBI baselines?

Goal

“The dishes of the SKA will produce

10 times the global internet traffic.”1

1 https://www.skatelescope.org/amazingfacts/ August 9, 2016 Image credit: SPDO/Swinburne Astronomy Productions

Question: How to select key baselines?

Preserve key observable information

Decrease processing time

Image credit: SPDO/Swinburne Astronomy Productions

Goals

Tools

Genetic Algorithm

(GA)

Structural Similarity (SSIM)

Masking

Machine Learning

Guidance by the Researcher Evaluation

Case studies

Optimization Approach

Application

Case studies

Horsehead Nebula:-Dense with a lot of structure -Challenging current image

reconstruction methods -Inspires masking

Simulated point source:Sparse

(Most image reconstruction techniques focus on sparse images)

Case studies allow us to assess how our method’s efficacy varies by object type.

Image source: astropy.org

http://astropy.org

Masks let the researcher emphasize important features

Genetic Algorithm

GA trains on the masked image with the goal of retaining an image’s salient features.

Intelligent Baseline Selection for Radio Interferometric Imaging

Genetic Algorithm

All baselines

Selected baselines

Create a population of uv baselines

Randomly pick first generation

Select the fittest individuals

Perform crossover and mutation

Form next generation

Baseline coverage

Simplified baseline coverage

Pick the first generation’s uv coverage randomly





SSIM=0.7

SSIM=0.1

SSIM=0.8

SSIM=0.9Randomly pick first generation




Find the best individuals using structural similarity (SSIM)

SSIM=0.7

SSIM=0.1

SSIM=0.8

SSIM=0.9Randomly pick first generation




Find the best individuals using structural similarity (SSIM)

The best individuals mate via crossover

SSIM=0.9

SSIM=0.8






SSIM=0.9

SSIM=0.8










SSIM=0.9

SSIM=0.8

Create new, fitter generations until we reach goal SSIM





Initial results

Our approach generally

outperforms random baseline selection on

the synthetic disk image

Evaluation on synthetic disk image

Training image

Our approach generally outperforms

random baseline selection on the

Horsehead Nebula

Evaluation on the Horsehead Nebula

Suppose one observing run requires 1800 terabytes of

storage.

Image author: Scott Schiller

How much information can we retain with selected optimized baselines?









Conclusion

• With increasing number of baselines, data volumes make processing more challenging

• In certain experimental scenarios, scientists might choose to operate with fewer baselines (e.g., for quick previews)

• In some cases similar image quality can be obtained with fewer baselines

• Our exploration shows promising results, optimized selection performs better than random selection

• Applicability varies by type of observable object

• More case studies are forthcoming: • Supernova 1993J • Solar dynamics • M31

Conclusions

Image credit: NASA, ESA, and G. Bacon (STScI)

Acknowledgements

Michael GowanlockJustin Li

Victor Pankratius

Phil Erickson Vincent Fish Frank Lind

Big data radio science group

Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,

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