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Solving the Puzzles of Planet Formation in the Era of K2, TESS and PLATO

Solving the Puzzles of Planet Formation in the Era of K2, TESS and PLATO

A colloquium I gave at the University of Birmingham in the UK. The idea what to show what we can learn from terrestrial planet formation, conditioned on Kepler data.

Tom Barclay

July 10, 2016
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  1. Tom Barclay NASA Ames Research Center University of Birmingham July

    11, 2016 Solving the Puzzles of Planet Formation in the Era of K2, TESS and PLATO
  2. None
  3. None
  4. 4 How do Planets Form?

  5. Collapse of molecular cloud core proto-star + disk (Shu, Adams,

    & Lizano 1987) Planets Form From Disks
  6. Early stage dust grains planetesimals ~ μm ~1-10 km •

    non-gravitational sticking process remains poorly understood Classical Solar Nebular Theory
  7. Early stage dust grains planetesimals ~ μm ~1-10 km Middle

    stage planetesimals planetary embryos ~103 km Kokubo & Ida 2002 Classical Solar Nebular Theory
  8. Early stage dust grains planetesimals ~ μm ~1-10 km Middle

    stage planetesimals planetary embryos ~103 km Late stage embryos planets Classical Solar Nebular Theory
  9. Planetary systems are diverse! 3300 planets in 560 systems as

    of today (2300 discovered with Kepler)
  10. Exoplanet Detections, 2015 Earth >4600 Candidates

  11. None
  12. N-body Models Challenges: 1. Models assume perfect accretion (fragmentation increases

    N) 2. N-body systems are chaotic, need lots of simulations My modeling work addresses these two issues Mercury modified to include state-of-the-art collisions model We performed hundreds of N-body simulations to infer results statistically Chambers (2013) Quintana, Barclay et al. 2016 (arxiv 1511.03663) Barclay et al, submitted to ApJ
  13. rimp ℓ ℓ B B rimp rtar rtar θ θ

    ℓ = rtar + rimp - B ℓ = 2 rimp vimp vimp (a) (b) New Collision Model Based on model by Stewart & Leinhardt (2012) Mercury N-body integration package modified to include collision model that maps outcomes of a two-body collisions based on masses and impact geometry Outcomes include: -collision with central star, giant planet -perfect accretion -fragmentation -hit-and-run collisions (Asphaug 2006) Chambers (2013) Quintana, Barclay et al. 2016 (arxiv 1511.03663)
  14. Quantities We Can Track Time of final giant impact

  15. Giant Siblings

  16. Jupiter analogs are likely scarce! Occurrence Rates of Jupiter (RV

    + Transits) ~ 6% (Wittenmyer et al. 2016)
  17. Effect of Giant Planets on Formation of Earth-like Planets Case

    1: Sun + Jupiter + Saturn Case 2: Sun only (no giants) Disk model: 26 (lunar) embryos, 260 (Mars) planetesimals Smallest fragments = 0.5 lunar mass 300 simulations, each for 2 Gyr where all bodies fully interact gravitationally and collisionally,
  18. Jupiter+Saturn 18 No giant planets

  19. 19 Simulations

  20. Results from Early Tests

  21. We can use Modern Statistics Final giant impacts happen earlier

    if there are giant planets
  22. 22 WFIRST is going to be searching for free-floating planets.

    How many will it find?
  23. 23 Outer disk is ejected first Efficient radial mixing with

    no giant planets Fragments from inner system ejected later Dependence on Initial Semimajor Axis
  24. 24 WFIRST will find plenty of Mars’ but few earths

    If Giant planets are rare, WFIRST finds no FFP WFIRST Detections
  25. 25 Sun-Only

  26. 26 Micro-Oort Clouds?

  27. Planets Orbiting Cool Stars

  28. G dwarf M dwarf Kepler-186 Sun Detecting Planets around M

    dwarfs is Easier TIME BRIGHTNESS BRIGHTNESS TIME
  29. Kepler-186f Quintana, Barclay et al.

  30. Finding if Earth-analogs can form in these regimes has big

    implications for the abundance of life in the Universe 7 out of 10 stars in our galaxy are M dwarfs Most stars lack outer giant companions
  31. Raymond et al. 2007 Earth’s have been found around M

    stars of all sizes!! This assumption of disks cannot be true Planet Formation around Cool Stars TRAPPIST-1 M = 0.08 Msun Kepler-186 M = 0.5 Msun
  32. Kepler-186 Example We can form Earths around Kepler-186 if we

    use high mass disk (15 M⨁ within 0.5 AU) and steep surface density profile These types of disks aren’t predicted To resolve this: Invoke migration? We can use N-body simulations as a forward model to create synthetic catalogs: compare with Kepler/K2/ TESS/PLATO observed catalogs My future research will explore how Earth’s form around low mass stars
  33. Modeling Collisions Critical for M star Regime! My work will

    investigate these characteristics Shorter orbital periods High speed impacts Accreting and retaining water, atmospheres is more difficult
  34. Giants Planets Orbiting Giants Stars

  35. We can use asteroseismology to infer stellar properties We can

    use Gaussian Processes to get around increased noise from granulations K2 will observe 100,000+giants TESS will observe millions If red giant occurrence rates match main-sequence stars, TESS will find 1000s. BUT… We can learn the occurrence rates for planets around red giants Main Sequence star Red giant star Chaplin et al 2013
  36. We can use asteroseismology to infer stellar properties We can

    use Gaussian Processes to get around increased noise from granulations (to search as well as model) K2 will observe 100,000+giants TESS will observe millions If red giant occurrence rates match main-sequence stars, TESS will find 1000s. BUT… We can learn the occurrence rates for planets around red giants Barclay et al 2015
  37. We can use asteroseismology to infer stellar properties We can

    use Gaussian Processes to get around increased noise from granulations K2 will observe 100,000+giants TESS will observe millions Huber et al 2016
  38. We can use asteroseismology to infer stellar properties We can

    use Gaussian Processes to get around increased noise from granulations K2 will observe 100,000+giants TESS will observe millions If red giant occurrence rates match main-sequence stars, TESS will find 1000s. BUT… We can learn the occurrence rates for planets around red giants Barclay et al, in prep
  39. We can use asteroseismology to infer stellar properties We can

    use Gaussian Processes to get around increased noise from granulations K2 will observe 100,000+giants TESS will observe millions If red giant occurrence rates match main-sequence stars, TESS will find 1000s. BUT… We can learn the occurrence rates for planets around red giants
  40. Takeaways A. We can use observations to place constraints on

    planet formation models. B. From models we can learn about planet formation and evolution: composition, water delivery, impact history. C. Planets are heavily influenced by their siblings. We must think in terms of planetary systems. D. M-dwarfs are the first places we will search for life. We can predict what we will find and inform strategies. E. Planets around red giants provide unique laboratories that teach us about our own future. F. We can combine observations, theory and modern statistics.