![Page 1: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/1.jpg)
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/
![Page 2: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/2.jpg)
What are VLBI baselines?
![Page 3: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/3.jpg)
Goal
![Page 4: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/4.jpg)
“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
![Page 5: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/5.jpg)
Question: How to select key baselines?
Preserve key observable information
Decrease processing time
Image credit: SPDO/Swinburne Astronomy Productions
Goals
![Page 6: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/6.jpg)
Tools
![Page 7: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/7.jpg)
Genetic Algorithm
(GA)
Structural Similarity (SSIM)
Masking
Machine Learning
Guidance by the Researcher Evaluation
Case studies
Optimization Approach
Application
![Page 8: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/8.jpg)
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
![Page 9: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/9.jpg)
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.
![Page 10: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/10.jpg)
Intelligent Baseline Selection for Radio Interferometric Imaging
Genetic Algorithm
All baselines
Selected baselines
![Page 11: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/11.jpg)
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
![Page 12: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/12.jpg)
Pick the first generation’s uv coverage randomly
Randomly pick first generation
Select the fittest individuals
Perform crossover and mutation
Form next generation
![Page 13: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/13.jpg)
SSIM=0.7
SSIM=0.1
SSIM=0.8
SSIM=0.9Randomly pick first generation
Select the fittest individuals
Perform crossover and mutation
Form next generation
Find the best individuals using structural similarity (SSIM)
![Page 14: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/14.jpg)
SSIM=0.7
SSIM=0.1
SSIM=0.8
SSIM=0.9Randomly pick first generation
Select the fittest individuals
Perform crossover and mutation
Form next generation
Find the best individuals using structural similarity (SSIM)
![Page 15: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/15.jpg)
The best individuals mate via crossover
SSIM=0.9
SSIM=0.8
Randomly pick first generation
Select the fittest individuals
Perform crossover and mutation
Form next generation
![Page 16: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/16.jpg)
The best individuals mate via crossover
SSIM=0.9
SSIM=0.8
Randomly pick first generation
Select the fittest individuals
Perform crossover and mutation
Form next generation
![Page 17: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/17.jpg)
The best individuals mate via crossover
Randomly pick first generation
Select the fittest individuals
Perform crossover and mutation
Form next generation
SSIM=0.9
SSIM=0.8
![Page 18: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/18.jpg)
Create new, fitter generations until we reach goal SSIM
Randomly pick first generation
Select the fittest individuals
Perform crossover and mutation
Form next generation
![Page 19: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/19.jpg)
Initial results
![Page 20: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/20.jpg)
Our approach generally
outperforms random baseline selection on
the synthetic disk image
Evaluation on synthetic disk image
![Page 21: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/21.jpg)
Training image
Our approach generally outperforms
random baseline selection on the
Horsehead Nebula
Evaluation on the Horsehead Nebula
![Page 22: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/22.jpg)
Suppose one observing run requires 1800 terabytes of
storage.
Image author: Scott Schiller
![Page 23: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/23.jpg)
How much information can we retain with selected optimized baselines?
![Page 24: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/24.jpg)
How much information can we retain with selected optimized baselines?
![Page 25: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/25.jpg)
How much information can we retain with selected optimized baselines?
![Page 26: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/26.jpg)
How much information can we retain with selected optimized baselines?
![Page 27: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/27.jpg)
How much information can we retain with selected optimized baselines?
![Page 28: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/28.jpg)
How much information can we retain with selected optimized baselines?
![Page 29: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/29.jpg)
How much information can we retain with selected optimized baselines?
![Page 30: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/30.jpg)
How much information can we retain with selected optimized baselines?
![Page 31: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/31.jpg)
How much information can we retain with selected optimized baselines?
![Page 32: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/32.jpg)
Conclusion
![Page 33: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/33.jpg)
• 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)
![Page 34: Algorithmic VLBI Baseline Selection · 2016-08-15 · •With increasing number of baselines, data volumes make processing more challenging • In certain experimental scenarios,](https://reader035.vdocument.in/reader035/viewer/2022081611/5f0ae9877e708231d42df41a/html5/thumbnails/34.jpg)
Acknowledgements
Michael GowanlockJustin Li
Victor Pankratius
Phil Erickson Vincent Fish Frank Lind
Big data radio science group