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Esperanza Lopez Aguilera - Using a Bayesian Neural Network in the Detection of Exoplanets

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Using a Bayesian Neural Network in the Detection of Exoplanets

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Esperanza Lopez Aguilera - Using a Bayesian Neural Network in the Detection of Exoplanets

  1. 1. 1The World’s Fastest Time-Series Database Esperanza López Aguilera 29 March 2019 Using a Bayesian Neural Network in the Detection of Exoplanets
  2. 2. 2The World’s Fastest Time-Series Database ● The World’s Fastest Time-Series Database ○ In-memory computing ○ Streaming analytics ● Q Language ○ Functional ○ Array based ○ Primitive temporal datatypes ○ Tables are the first class datatype ○ Qsql ○ Lambda architecture out of the box ○ Scarily efficient ○ Syntax highlighting for q What’s kdb+?
  3. 3. 3The World’s Fastest Time-Series Database ● The World’s Fastest Time-Series Database ○ In-memory computing ○ Streaming analytics ● Q Language ○ Functional ○ Array based ○ Primitive temporal datatypes ○ Tables are the first class datatype ○ Qsql ○ Lambda architecture out of the box ○ Scarily efficient ○ Syntax highlighting for q What’s kdb+? Extremely fast Elegant and concise
  4. 4. 4The World’s Fastest Time-Series Database Applications What do they have in common? Data - Time series Machine learning solutions
  5. 5. 5The World’s Fastest Time-Series Database NASA Frontier Development Lab ● Applied AI research accelerator ● Hosted by: ○ SETI Institute ○ NASA Ames Research Center ● 2-months programme ● 7 challenges ● Involved in 2 challenges: ○ Space weather: ■ How solar activity impacts Earth ■ Paper: https://code.kx.com/q/wp/space-weather/ ○ Exoplanets: ■ Find new planet candidates ■ Paper: https://code.kx.com/q/wp/exoplanets/
  6. 6. 6The World’s Fastest Time-Series Database Exoplanets Challenge - TESS ● Launched in April 2018 ● 2 years mission ● 26 sectors: ○ 27 days per sector ● Objective: Discovering new exoplanets in orbit around the brightest stars in the solar neighborhood
  7. 7. 7The World’s Fastest Time-Series Database Exoplanets challenge - Transits How do we detect exoplanets? Transits
  8. 8. 8The World’s Fastest Time-Series Database But it’s not so easy ... Background Eclipsing Binaries Eclipsing Binaries Stellar activity
  9. 9. 9The World’s Fastest Time-Series Database Data Images taken at a given frequency Target stars Optimal set of pixel representing each star Aggregate brightness extracted Remove noise, trends and other factors ● Simulated data ○ 4 sectors ● 64, 000 target stars ● Strong signal found in 9,139 stars ● 19,577 TCEs or planet candidates ● Optimal parameters inferred: ○ Epoch ○ Period ○ Duration ○ ... ● Issue: Many false positives Threshold Crossing Events Corrected flux - Light curve
  10. 10. 10The World’s Fastest Time-Series Database Light curves Where is the planet?
  11. 11. 11The World’s Fastest Time-Series Database Light curves What? Why?
  12. 12. 12The World’s Fastest Time-Series Database Light curves This is confusing
  13. 13. 13The World’s Fastest Time-Series Database Light curves - Local view Now, I know
  14. 14. 14The World’s Fastest Time-Series Database Classification techniques ● Humans looking at light curves ○ Statistical methods used ○ Too many hours ● Complex models ○ Several inputs ○ Time-consuming ○ Intensive preprocessing
  15. 15. 15The World’s Fastest Time-Series Database Benchmark model ● 77% accurate ● Very low precision ○ 47% ● Uncertainty? ● Confidence? Linear classifier
  16. 16. 16The World’s Fastest Time-Series Database Bayesian Neural Network Stochastic model + Neural Network Probabilistic confidence on predictions ● Weights follow a distribution ● Train parameters instead of weights ● Result: Distribution of probabilities ● Several criteria for decision making ○ Standard deviation ○ Mean ○ ...
  17. 17. 17The World’s Fastest Time-Series Database Bayesian Neural Network Oversampling - Random sample of the positive class Build Network - Define architecture and parameters EmbedPy
  18. 18. 18The World’s Fastest Time-Series Database Results - Performance ● Outputs of the BNN: ○ Probability of being a planet ○ Sample of size 500 per input ● Decision based on: ○ Average probability ○ P > 0.5 ⇒Planet ○ Flexibility ● Metrics: ○ Accuracy: 91% ○ Precision: 83% ○ Sensitivity: 68% Probability sample of one TCE
  19. 19. 19The World’s Fastest Time-Series Database Results - Confidence
  20. 20. 20The World’s Fastest Time-Series Database Results - Examples
  21. 21. 21The World’s Fastest Time-Series Database Esperanza López Aguilera Machine Learning Engineer, Kx elopezaguilera@kx.com ai@kx.com Useful links Machine learning: https://code.kx.com/q/ml/ Kx Github: https://github.com/KxSystems/ Join the conversation: twitter.com/kxsystems linkedin.com/company/kx-systems/ facebook.com/kxsystems instagram.com/kxsystems Thanks Contact details

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