Exoplanet- Asteroseismology Synergies Bill Chaplin, School of Physics & Astronomy University of Birmingham, UK EAHS2012, Oxford, 2012 March 15

Credit: IAC

Oscillations as clocks: FM stars Shibahashi & Kurtz (2012), MNRAS, submitted (arXiv: )

Oscillations as clocks: V391 Peg Silvotti et al. (2007), Nature, 449, 189

Pulsations in the HR diagram Aerts, Christensen-Dalsgaard & Kurtz 2009

O-C diagram, prominent oscillation frequencies of V391 Peg Silvotti et al. (2007), Nature, 449, 189 Frequency #1Frequency #2

Understanding stellar systems like our own…

Jupiter: 1% area of the Sun (1/100 or 10,000 ppm) Earth or Venus: 0.01% area of the Sun (1/10,000 or 100 ppm) Photometry to detect transits

Evolution and properties of stellar systems  Precise, accurate fundamental stellar properties for modelling exoplanet systems: ­ Seismic densities, radii, masses, ages ­ Seismic log(g) for “boot strapping” spectroscopic analysis

 Internal rotation, stellar angle of inclination: ­ Constraints on dynamical histories of stellar systems Evolution and properties of stellar systems

 Intrinsic activity, variability of host stars, influence on local environment: ­ “Sound” stellar activity cycles ­ Depths of convective envelopes, tests of stellar dynamos Evolution and properties of stellar systems

asteroFLAG Hare and Hounds How Kepler would see Sun at v = 10

asteroFLAG Hare and Hounds Stello, Chaplin et al. 2009, ApJ

asteroFLAG Hare and Hounds

Asteroseismic ensemble tests Kepler Input Catalogue Finds an underestimation bias in KIC radii Verner et al., 2011, ApJ, 738, L28 KIC – seismic log g (dex) KIC – seismic radii (%)

Inferences on stellar activity, stellar cycles, activity of the “Sun in time”

Sun (SOHO/VIRGO) Kepler: G  type dwarf

Sun (SOHO/VIRGO)

Frequency spectrum of  Cyg Solar-like oscillations Granulation Activity Guzik et al. (2012), in preparation

Stellar activity suppresses oscillations Inference on magnetic fields and convection Detected oscillations?noyes Proxy for “stellar activity” Chaplin et al., 2011, ApJ, 732, 5L T eff (K)

Frequency spectra of 16 Cyg A & B Metcalfe et al. (2012), ApJ

Broomhall et al., 2009, ApJ, 700, L162 “Sounding” stellar activity cycles: Sun

Broomhall et al., 2012, ApJ, 420, 1405 Quasi-biennial variation After removal of 11-yr cycle signature

CoRoT reveals a short activity cycle in HD49933 García et al., 2010, Science, 329, 1032

One example from the seismic Zoo Oscillation amplitudes Oscillation frequencies Light curve Variation of seismic frequencies and amplitudes Courtesy Salabert (Elsworth et al., work in progress)

 Inference on distribution:  From frequency shifts of different modes  From frequency asymmetry of components of non-radial modes Inference: surface distribution of activity sizes and phases of frequency shifts depend on ( l, m) Chaplin (2011), Proceedings Tenerife Winter School

 Activity distribution: non homogeneous, preferred bands of latitude  Response of modes: depends on ( l, m) Inference: surface distribution of activity sizes and phases of frequency shifts depend on ( l, m) Chaplin (2011), Proceedings Tenerife Winter School

Effects of near-surface activity on modes Depends on spherical harmonic of mode ( l, m) (1,0)(1,1)(2,0) (3,0)(2,2) (2,1)

Chaplin et al. 2007, MNRAS, 377, 17 Spatial dependence of the frequency shifts Inference on active latitudes Predicted shifts simple model for latitudinal distribution surface activity Match to observed ratios

Chaplin et al. 2007, MNRAS, 377, 17 Spatial dependence of the frequency shifts Inference on active latitudes Predicted shifts simple model for latitudinal distribution surface activity Match to observed ratios

Chaplin et al. 2007, MNRAS, 377, 17 Spatial dependence of the frequency shifts Inference on active latitudes Predicted shifts simple model for latitudinal distribution surface activity Match to observed ratios

Chaplin et al. 2007, MNRAS, 377, 17 Spatial dependence of the frequency shifts Inference on active latitudes Predicted shifts simple model for latitudinal distribution surface activity Match to observed ratios

Inference: surface distribution of activity sizes and phases of frequency shifts depend on ( l, m ) Chaplin (2011), Proceedings Tenerife Winter School Sun-as-a-star data max =40 ± 10 degrees

Stellar activity squashes mode peaks!  = See Chaplin et al., 2008, MNRAS, 384, 1668

Credit: IAC Asteroseismic analysis Kepler Objects of Interest (KOIs)

Seismology of exoplanet host stars HAT-P-7 Christensen-Dalsgaard et al Kepler-10b Batalha et al. 2011

Howell et al. (2012), ApJ, 746, 123 Kepler 21b 1.6R E planet orbiting bright F-type sub-giant

 Brightest Kepler exoplanet host star  High-precision stellar properties from asteroseismology:  Stellar radius to 2.2%  Stellar mass to 4.5%  Stellar age to 12%  Planetary radius to 2.4% Kepler 21b 1.6R E planet orbiting bright F-type sub-giant

Kepler 22b 2.4R E planet in habitable zone of Sun-like star Borucki et al. (2012), ApJ, 745, 120 Strong signature of large frequency separation

Inclination affects mode visibility (1,0)(1,1)(2,0) (3,0)(2,2) (2,1)

Inclination affects mode visibility Gizon & Solanki, 2003, ApJ, 589, 1009 m=1m=1 0+1 m=2m=2 0 11 +2 l =1 l =2

Inference on stellar inclination Height ratios depend on angle 2 yrs

1 month Need long datasets Inference on stellar inclination

Fin