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The search for single transits
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Dan Foreman-Mackey
May 08, 2015
Science
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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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Transcript
Single the search for Transits Dan Foreman-Mackey NYU→UW // github.com/dfm
// @exoplaneteer // dfm.io
David W. Hogg NYU Bernhard Schölkopf MPI-IS
Population Inference
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
1 catalog of planet (candidates) measurement of completeness 2 3
measurement of precision Ingredients of a population inference
101 102 orbital period [days] 100 101 planet radius [R
] Data from NASA Exoplanet Archive
101 102 orbital period [days] 100 101 planet radius [R
] Data from NASA Exoplanet Archive
100 101 102 103 104 105 orbital period [days] 100
101 planet radius [R ] Data from NASA Exoplanet Archive
10 100 f 10 30 100 N detection S/N threshold
# of detectable single transits Extrapolated from Dong & Zhu (2013)
How to find a Transiting Planet the traditional way…
1 de-trending grid search in period, phase, and duration 2
3 vetting of candidates How to find a (periodic) transit signal
False Alarms & False Positives
How to find a Transiting Planet the Planet Hunters way…
None
Can we Teach the Machine to Learn™?
Bernhard Schölkopf MPI-IS Get rid of the pipeline!
no_transit transit vs. 1 0 1 time [days] 1 0
1 time [days] Supervised Classification
Supervised Classification
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
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
light curve sections simulated transits held-out light curve features training
set test set
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
no_transit transit vs. 1 0 1 time [days] 1 0
1 time [days]
scikit-learn.org
Preliminary Results
light curves false positives transit candidate 3,000 273 1
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
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
No good model of the non-transits…
Temporary solution: Template likelihoods
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