The search for single transits

The search for single transits

My short talk from the Sagan Fellows Symposium at Caltech

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Dan Foreman-Mackey

May 08, 2015
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Transcript

  1. Single the search for Transits Dan Foreman-Mackey NYU→UW // github.com/dfm

    // @exoplaneteer // dfm.io
  2. David W. Hogg NYU Bernhard Schölkopf MPI-IS

  3. Population Inference

  4. treatment of false positives, dependent parameters, uncertainties & selection effects

    open source tools applicable to all existing & future exoplanet missions occurrence rate period, radius, mass, eccentricity, multiplicity, mutual inclination, etc. Flexible & robust inference of the exoplanet population
  5. 1 catalog of planet (candidates) measurement of completeness 2 3

    measurement of precision Ingredients of a population inference
  6. 101 102 orbital period [days] 100 101 planet radius [R

    ] Data from NASA Exoplanet Archive
  7. 101 102 orbital period [days] 100 101 planet radius [R

    ] Data from NASA Exoplanet Archive
  8. 100 101 102 103 104 105 orbital period [days] 100

    101 planet radius [R ] Data from NASA Exoplanet Archive
  9. 10 100 f 10 30 100 N detection S/N threshold

    # of detectable single transits Extrapolated from Dong & Zhu (2013)
  10. How to find a Transiting Planet the traditional way…

  11. 1 de-trending grid search in period, phase, and duration 2

    3 vetting of candidates How to find a (periodic) transit signal
  12. False Alarms & False Positives

  13. How to find a Transiting Planet the Planet Hunters way…

  14. None
  15. Can we Teach the Machine to Learn™?

  16. Bernhard Schölkopf MPI-IS Get rid of the pipeline!

  17. no_transit transit vs. 1 0 1 time [days] 1 0

    1 time [days] Supervised Classification
  18. Supervised Classification

  19. Random Forest™ Classification NYC LA 10 8 NYC LA 7

    2 NYC LA 3 6 Raining Sunny Car Subway NYC LA 0 6 NYC LA 3 0 NYC LA 0 2 NYC LA 7 0 Beach Park decision tree
  20. Random Forest™ Classification NYC LA 10 8 NYC LA 7

    2 NYC LA 3 6 Raining Sunny Car Subway NYC LA 0 6 NYC LA 3 0 NYC LA 0 2 NYC LA 7 0 Beach Park decision tree
  21. light curve sections simulated transits held-out light curve features training

    set test set
  22. 200 400 600 800 1000 1200 1400 time [KBJD] 0.003

    0.002 0.001 0.000 0.001 0.002 0.003 0.004
  23. no_transit transit vs. 1 0 1 time [days] 1 0

    1 time [days]
  24. scikit-learn.org

  25. Preliminary Results

  26. light curves false positives transit candidate 3,000 273 1

  27. 9821962 9847647 10544712 9834736 9763612 9763027 2 0 2 10554152

    2 0 2 9776926 time since transit [days] 9821962 9847647 10544712 9834736 9763612 9763027 2 0 2 10554152 2 0 2 9776926 time since transit [days] 10602068 10286702 10518652 9775416 9821962 9847647 10544712 9834736 9763612 9763027 False Positives
  28. 3.0 3.3 3.6 3.9 log10 P/day 0.21 0.22 0.23 0.24

    t0 830.8 KBJD [hr] 0.58 0.60 0.62 b 1.2 1.8 2.4 3.0 Rp [RJ ] 0.15 0.30 0.45 0.60 e 3.0 3.3 3.6 3.9 log10 P/day 0.21 0.22 0.23 0.24 t0 830.8 KBJD [hr] 0.58 0.60 0.62 b 0.15 0.30 0.45 0.60 e 824 826 828 830 832 834 836 838 0.90 0.92 0.94 0.96 0.98 1.00 1.02 824 826 828 830 832 834 836 838 0.90 0.92 0.94 0.96 0.98 1.00 1.02 824 826 828 830 832 834 836 0.90 0.92 0.94 0.96 0.98 1.00 1.02
  29. No good model of the non-transits…

  30. Temporary solution: Template likelihoods

  31. 1 can discover single transits using supervised classification false positives

    are still a problem (but maybe less) 2 3 would like to combine method with realistic noise model Conclusions