Long-period transiting exoplanets

Long-period transiting exoplanets

Talk from the Exoplanets I conference

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Dan Foreman-Mackey

July 04, 2016
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Transcript

  1. LONG-PERIOD TRANSITING EXOPLANETS Dan Foreman-Mackey Sagan Fellow @ University of

    Washington github.com/dfm // dfm.io // @exoplaneteer Creative Commons Attribution 4.0 International License with Tim Morton (Princeton), Bernhard Schölkopf (MPIS), David W. Hogg (NYU), Eric Agol (UW)
  2. The Kepler Legacy

  3. 1 10 100 orbital period [days] 1 10 planet radius

    [R ] Data from NASA Exoplanet Archive
  4. The population of exoplanets

  5. The population of exoplanets 1 Occurrence rates 2 Physical processes

  6. The population of exoplanets 1 Occurrence rates 2 Physical processes

  7. 1 10 100 orbital period [days] 1 10 planet radius

    [R ] Data from NASA Exoplanet Archive
  8. Burke et al. (2015) Figure 6 more co variation (c)

    Previ break in et al. 20 (d) Final for altern dwarf sa (Dressin power la Sympt model o constrain When Rb become meaning Rp in the which w otherwis lower l Figure 7. Same as Figure 6, but marginalized over 0.75 < Rp < 2.5 Å R and bins of dP orb = 31.25 days. Figure 8. Shows the underlying planet occurrence rate model. Marginalized over 50 < P orb < 300 days and bins of dRp =0.25 Å R planet occurrence rates for the model parameters that maximize the likelihood (white dash line). Posterior distribution for the underlying planet occurrence rate for the median Figure 9. S of dP orb =
  9. Burke et al. (2015) ness model 3; Farr et al.

    hortcoming ipeline and a et al. coming by ness of the 14) through this study, ler pipeline the planet epler planet er highlight systematic rates with nd Dong & where we alculate the ut assump- eccentricity, Figure 1. Fractional completeness model for the host to Kepler-22b (KIC: 10593626) in the Q1-Q16 pipeline run using the analytic model described in Section 2. Burke et al.
  10. 1 10 100 orbital period [days] 1 10 planet radius

    [R ] Data from NASA Exoplanet Archive
  11. 1 Systematic planet candidate catalog Ingredients for population inference 2

    Measured completeness & reliability 3 Quantification of false positive rates
  12. The frequency of Solar System analogs

  13. 1 10 100 orbital period [days] 1 10 planet radius

    [R ] Data from NASA Exoplanet Archive
  14. 1 10 100 orbital period [days] 1 10 planet radius

    [R ] Data from NASA Exoplanet Archive
  15. 1 10 100 1000 10000 orbital period [days] 1 10

    planet radius [R ] Data from NASA Exoplanet Archive
  16. That's not fair…

  17. 1 10 100 1000 10000 orbital period [days] 0 100

    200 300 400 500 600 number of discoveries transit RV microlensing Data from The Open Exoplanet Catalogue
  18. Why Kepler?

  19. 1 Systematic target selection Why Kepler? 2 Sensitivity to small

    planets 3 Opportunity to measure densities
  20. 1 Systematic target selection Why Kepler? 2 Sensitivity to small

    planets 3 Opportunity to measure densities 4 The data exist
  21. 1 Systematic target selection The future of transit surveys 2

    Sensitivity to small planets 3 Opportunity to measure densities 4 The data will continue to be taken (with even shorter baselines…)
  22. 1 Systematic planet candidate catalog Ingredients for population inference 2

    Measured completeness & reliability 3 Quantification of false positive rates
  23. Why are long period transiting planets hard to find?

  24. The way we draw transits

  25. What an (idealized) transit actually looks like interesting boring boring

  26. 1 10 100 1000 10000 orbital period [days] 1 10

    planet radius [R ] Data from NASA Exoplanet Archive Ptransit / P 5/3
  27. 1 10 100 1000 10000 orbital period [days] 1 10

    planet radius [R ] Data from NASA Exoplanet Archive Ptransit / P 5/3
  28. Today's punch line

  29. 1 10 100 1000 10000 orbital period [days] 1 10

    planet radius [R ] Data from NASA Exoplanet Archive
  30. 1 10 100 1000 10000 orbital period [days] 1 10

    planet radius [R ] Candidates (green) from DFM et al. (in prep); Data from NASA Exoplanet Archive
  31. 1 Systematic planet candidate catalog Ingredients for population inference 2

    Measured completeness & reliability 3 Quantification of false positive rates
  32. How to find a transiting planet…

  33. How to find a transiting planet… short-period

  34. + planet star space craft detector signal + + =

    The anatomy of a transit
  35. + planet star space craft detector signal + + =

    The anatomy of a transit We have a model…
  36. … just do the inference?

  37. … just do the inference? p (data | a planet

    exists) p (data | there is no planet) AKA just straight up compute:
  38. no. p (data | a planet exists) p (data |

    there is no planet) AKA just straight up compute:
  39. How to find "short-period" transiting exoplanets 1 Filter the data

    to "remove" systematics 2 Template-based grid of likelihoods (restricted to systems with >=3 transits) 3 Remove false alarms by "visual inspection"
  40. Aside: Quasi-periodic search Using the EVEREST light curves and our

    modified QATS planet discovery pipeline, we searched all K2 stars (C0-7) for transiting planets. We have discovered over 800 K2 planet candidates (500+ new), including high multiplicity systems (eight systems with 4+ planets). NOTE: These are preliminary results. See our paper (Kruse et al., nearing submission) for final planet counts, planet and stellar properties, etc. Planets ion of the Quasiperiodic tion to finding periodic ore effectively discover d quality of the data n near that of the ore details). compared to any other a, and the bottom panel is ter fire outliers are Figure 4: Results showing only our new (unreported in the literature) K2 planet candidates. Planets with 0 radius do not have stellar parameters yet. Rodrigo Luger et al. (ApJ accepted), Ethan Kruse et al. (in prep)
  41. Aside: Quasi-periodic search Using the EVEREST light curves and our

    modified QATS planet discovery pipeline, we searched all K2 stars (C0-7) for transiting planets. We have discovered over 800 K2 planet candidates (500+ new), including high multiplicity systems (eight systems with 4+ planets). NOTE: These are preliminary results. See our paper (Kruse et al., nearing submission) for final planet counts, planet and stellar properties, etc. Planets ion of the Quasiperiodic tion to finding periodic ore effectively discover d quality of the data n near that of the ore details). compared to any other a, and the bottom panel is ter fire outliers are Figure 4: Results showing only our new (unreported in the literature) K2 planet candidates. Planets with 0 radius do not have stellar parameters yet. Rodrigo Luger et al. (ApJ accepted), Ethan Kruse et al. (in prep) See posters by Rodrigo Luger (30) & Ethan Kruse (29)
  42. How to find "short-period" transiting exoplanets 1 Filter the data

    to "remove" systematics 2 Template-based grid of likelihoods (restricted to systems with >=3 transits) 3 Remove false alarms by "visual inspection"
  43. How to find "short-period" transiting exoplanets 1 Filter the data

    to "remove" systematics 2 Template-based grid of likelihoods (restricted to systems with >=3 transits) 3 Remove false alarms using magic* * I will use similar magic shortly. See F. Mullally et al. (2016); Coughlin et al. (2016)
  44. How to find "short-period" transiting exoplanets 1 ~190,000 target stars

    2 Template-based grid of likelihoods (restricted to systems with >=3 transits) 3 Remove false alarms using magic* * I will use similar magic shortly. See F. Mullally et al. (2016); Coughlin et al. (2016)
  45. How to find "short-period" transiting exoplanets 1 ~190,000 target stars

    2 ~35,000 candidates 3 Remove false alarms using magic* * I will use similar magic shortly. See F. Mullally et al. (2016); Coughlin et al. (2016)
  46. How to find "short-period" transiting exoplanets 1 ~190,000 target stars

    2 ~35,000 candidates 3 ~5,000 exoplanets See F. Mullally et al. (2016); Coughlin et al. (2016)
  47. 1 10 100 1000 10000 orbital period [days] 1 10

    planet radius [R ] Data from NASA Exoplanet Archive
  48. How to find "long-period" transiting exoplanets e.g. Wang et al.

    (2015); Uehara et al. (2016); Kipping et al. (2014, 2016); Osborn et al. (2016)
  49. How to find "long-period" transiting exoplanets 1 Visual inspection e.g.

    Wang et al. (2015); Uehara et al. (2016); Kipping et al. (2014, 2016); Osborn et al. (2016)
  50. Ingredients for population inference 1 Systematic planet candidate catalog 2

    Measured completeness & reliability 3 Quantification of false positive rates
  51. Ingredients for population inference 1 A fully-automated detection method

  52. My method for finding long-period transiting planets 1 Filter the

    data to "remove" systematics 2 Template-based grid of likelihoods (restricted to high signal-to-noise candidates) 3 Remove false alarms using model comparison* * The aforementioned "magic"
  53. + planet star space craft detector signal + + =

  54. star space craft detector signal + + = vs. vs.

    vs. vs. ... +
  55. Still too expensive…

  56. …use BIC* * don't take this slide out of context

  57. star space craft detector signal + + = vs. vs.

    vs. vs. ... + Gaussian Process* * github.com/dfm/george; Ambikasaran, DFM et al. (2014) Also: S. Aigrain's talk
  58. star space craft detector signal + + = vs. vs.

    vs. vs. ... + Gaussian Process* * github.com/dfm/george; Ambikasaran, DFM et al. (2014) Also: S. Aigrain's talk Autodiff
  59. DFM et al. (in prep) 40 20 0 20 40

    hours since event (a) variability KIC 7220674 40 20 0 20 40 hours since event (b) step KIC 8631697 40 20 0 20 40 hours since event (c) box KIC 5521451 40 20 0 20 40 hours since event (d) transit KIC 8505215
  60. Why not Machine Learning? (e.g. supervised classification)

  61. The Kepler data are not Big™.

  62. The Kepler data are Boring™.* * don't quote me all

    but ~0.02% of
  63. Results

  64. 4500 6000 7500 Te↵ 3.6 4.0 4.4 4.8 log g

    DFM et al. (in prep)
  65. My method for finding long-period transiting planets 1 Filter the

    data to "remove" systematics 2 Template-based grid of likelihoods (restricted to high signal-to-noise candidates) 3 Remove false alarms using model comparison
  66. My method for finding long-period transiting planets 1 ~40,000 target

    stars 2 Template-based grid of likelihoods (restricted to high signal-to-noise candidates) 3 Remove false alarms using model comparison
  67. My method for finding long-period transiting planets 1 ~40,000 target

    stars 2 ~400 candidates 3 Remove false alarms using model comparison
  68. My method for finding long-period transiting planets 1 ~40,000 target

    stars 2 ~400 candidates 3 16 exoplanet candidates* * some contamination
  69. 1 10 100 1000 10000 orbital period [days] 1 10

    planet radius [R ] Candidates (green) from DFM et al. (in prep); Data from NASA Exoplanet Archive
  70. DFM et al. (in prep) some overlap with Wang et

    al. (2015); Uehara et al. (2016); Kipping et al. (2014, 2016) 0.50 0.25 0.00 10321319 1.2 0.6 0.0 10287723 1.6 0.8 0.0 8505215 0.8 0.0 6551440 0.8 0.0 8738735 3 2 1 0 8800954 4 2 0 10187159 4 2 0 3218908 3.0 1.5 0.0 4754460 5.0 2.5 0.0 8410697 4 2 0 10842718 8 4 0 11709124 16 8 0 3239945 4 2 0 8426957 50 25 0 9306307 80 40 0 10602068 Figure 3. Sections of PDC light curve centered on each candidate (black) with the posterior-median transit model over-plotted (orange). Candidates with two transits are folded on the posterior-median period. The plots are ordered by increasing planetary radius from the top-left to the bottom-right.
  71. Ingredients for population inference 1 Systematic planet candidate catalog 2

    Measured completeness & reliability 3 Quantification of false positive rates
  72. The blessing/curse of the transit method interesting boring boring

  73. DFM et al. (in prep) 3 5 10 20 period

    [years] 0.2 0.5 1.0 2.0 RP /RJ 0.052 0.222 0.513 0.683 0.757 0.805 0.823 0.050 0.202 0.477 0.627 0.692 0.729 0.755 0.048 0.203 0.469 0.618 0.666 0.699 0.708 0.044 0.184 0.444 0.544 0.578 0.603 0.607 0.0 0.3 0.6 0.0 0.3 0.6
  74. 3 5 10 20 period [years] 0.2 0.5 1.0 2.0

    RP /RJ 0.052 0.222 0.513 0.683 0.757 0.805 0.823 0.050 0.202 0.477 0.627 0.692 0.729 0.755 0.048 0.203 0.469 0.618 0.666 0.699 0.708 0.044 0.184 0.444 0.544 0.578 0.603 0.607 0.0 0.3 0.6 0.0 0.3 0.6 1 10 100 1000 10000 orbital period [days] 1 10 planet radius [R ] Candidates (green) from DFM et al. (in prep); Data from NASA Exoplanet Archive +
  75. DFM et al. (in prep) compare with Bryan et al.

    (2016); Shvartzvald et al. (2016) RE – RN ~0.40 RN – RJ ~0.17 per G/K- dwarf, per ln-radius, per ln-period occurrence rate in period range 2 – 25 years
  76. Current shortcomings & work in progress 1 Hard to follow

    up – because Kepler 2 False positive quantification 3 Quantitative comparison & joint analysis with other catalogs 4 Probabilistic follow-up prioritization
  77. Current shortcomings & work in progress See Hugh Osborn's poster

    (63) 1 Hard to follow up – because Kepler 2 False positive quantification 3 Quantitative comparison & joint analysis with other catalogs 4 Probabilistic follow-up prioritization
  78. 1 Fully automated discovery of long- period transiting exoplanets in

    Kepler archival data 2 Empirical measurement of search completeness 3 Estimate of the occurrence rate of long-period exoplanets Summary
  79. Dan Foreman-Mackey github.com/dfm // dfm.io // @exoplaneteer follow along at:

    github.com/dfm/peerless