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.
Early stage dust grains planetesimals ~ μm ~1-10 km Middle stage planetesimals planetary embryos ~103 km Kokubo & Ida 2002 Classical Solar Nebular Theory
Early stage dust grains planetesimals ~ μm ~1-10 km Middle stage planetesimals planetary embryos ~103 km Late stage embryos planets Classical Solar Nebular Theory
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
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)
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,
23 Outer disk is ejected first Efficient radial mixing with no giant planets Fragments from inner system ejected later Dependence on Initial Semimajor Axis
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
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
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
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
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
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
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
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
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.
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