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The search for single transits

The search for single transits

My short talk from the Sagan Fellows Symposium at Caltech

Dan Foreman-Mackey

May 08, 2015
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  1. 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
  2. 1 catalog of planet (candidates) measurement of completeness 2 3

    measurement of precision Ingredients of a population inference
  3. 101 102 orbital period [days] 100 101 planet radius [R

    ] Data from NASA Exoplanet Archive
  4. 101 102 orbital period [days] 100 101 planet radius [R

    ] Data from NASA Exoplanet Archive
  5. 100 101 102 103 104 105 orbital period [days] 100

    101 planet radius [R ] Data from NASA Exoplanet Archive
  6. 10 100 f 10 30 100 N detection S/N threshold

    # of detectable single transits Extrapolated from Dong & Zhu (2013)
  7. 1 de-trending grid search in period, phase, and duration 2

    3 vetting of candidates How to find a (periodic) transit signal
  8. no_transit transit vs. 1 0 1 time [days] 1 0

    1 time [days] Supervised Classification
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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