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Future of asteroseismology II

Future of asteroseismology II. J ørgen Christensen-Dalsgaard Institut for Fysik og Astronomi, Aarhus Universitet Dansk AsteroSeismologisk Center. We need. Better data Better models. Better data. Better frequency precision ( s ( n ) < 0.1 m Hz) Lower noise level to reach more modes

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Future of asteroseismology II

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  1. Future of asteroseismology II Jørgen Christensen-Dalsgaard Institut for Fysik og Astronomi, Aarhus Universitet Dansk AsteroSeismologisk Center

  2. We need • Better data • Better models

  3. Better data • Better frequency precision (s(n) < 0.1 mHz) • Lower noise level to reach more modes • Data on a broader variety of stars • Identification of the modes (l, m) • Better ‘classical’ observables (M, R, L, Teff, X, Z) • g modes in the Sun to study the solar core

  4. Frequency precision Simply observe for longer • Easy for heat-engine modes (s(n) / tobs-1) • Harder for stochastically excited modes (s(n) / tobs-1/2 for t > tlife) Longer observations also improve detection of lower-amplitude modes

  5. Observational strategies • For very extended observations (weeks or months) we need dedicated instrumentation. • Space observations in intensity? Discussed by HK. • Helioseismology has shown the way: dedicated networks (BiSON, IRIS, TON) and • GONG (Global Oscillation Network Group) Hence we need ……

  6. SONG: Stellar Oscillation Network Group • SONG proposal (the Aarhus dream): • Network of small telescopes (60 cm equivalent) • Very efficient and highly stabilized spectrograph • Science goals: • Solar-like oscillations in relatively bright stars • Search for low-mass extrasolar planets in close orbits

  7. ? Possible distribution of sites

  8. Asteroseismic capabilities

  9. Planet-search capabilities

  10. Better data • Better frequency precision (s(n) < 0.1 mHz) • Lower noise level to reach more modes • Data on a broader variety of stars • Identification of the modes (l, m) • Better ‘classical’ observables (M, R, L, Teff, X, Z) • g modes in the Sun to study the solar core

  11. Data on a broader variety of stars • Multi-object spectrographs (but hard to ensure radial-velocity precision) • Intensity observations of multiple stars from space (HK lecture)

  12. Better data • Better frequency precision (s(n) < 0.1 mHz) • Lower noise level to reach more modes • Data on a broader variety of stars • Identification of the modes (l, m) • Better ‘classical’ observables (M, R, L, Teff, X, Z) • g modes in the Sun to study the solar core

  13. Mode identification • For stochastically excited oscillators, use nearly complete spectrum, regular structure of frequencies • For heat-engine oscillators, in general need independent information about mode geometry: • Combine amplitudes and phases of observations with different techniques (intensity in different colours, intensity and radial velocity, etc.)

  14. Doppler imaging Tau Peg (Kennelly et al. 1998; ApJ 495, 440)

  15. Doppler imaging Tau Peg (Kennelly et al. 1998) Major difficulty: Modelling of structure and oscillations of rapidly rotating star

  16. Better data • Better frequency precision (s(n) < 0.1 mHz) • Lower noise level to reach more modes • Data on a broader variety of stars • Identification of the modes (l, m) • Better ‘classical’ observables (M, R, L, Teff, X, Z) • g modes in the Sun to study the solar core

  17. Better ‘classical’ observables • Direct observations: • Magnitude • Colours • Spectra • With calibrations: • Luminosity (needs distance, bolometric correction) • Effective temperature (needs calibration) • Composition (needs model atmosphere) Solar abundance revisions are a reminder of the uncertainties in these analyses

  18. Better data • Better frequency precision (s(n) < 0.1 mHz) • Lower noise level to reach more modes • Data on a broader variety of stars • Identification of the modes (l, m) • Better ‘classical’ observables (M, R, L, Teff, X, Z) • g modes in the Sun to study the solar core Well, not yet, after 30 years of intensive efforts

  19. Better models of stellar evolution and oscillations • Better numerical reliability, accuracy • Better microphysics (equation of state, opacity, …) • Better treatment of convection • Better (i.e., some) treatment of energetics of oscillations • Inclusion of effects of rotation, on structure and oscillations • What about magnetic fields??? Use analysis of oscillation results to inspire improvements to the physics

  20. Numerical treatment • Are the evolution codes correct???? (Probably not) • Is the numerical precision adequate? (Compared with the observational precision) • How do we find out? Detailed comparisons of results of independent codes.

  21. Better microphysics • Extremely complex problems in many-body atomic physics • Coulomb interactions, excluded-volume effects, partial degeneracy, interaction with radiation …. • Some detailed testing using the Sun as a laboratory.

  22. Including relativistic effects No relativistic effects Example: relativistic electrons in the Sun Elliot & Kosovichev (1998; ApJ 500, L199)

  23. Modelling stellar convection • Mixing-length treatment (calibrated against the Sun) • Detailed hydrodynamical simulations (for a range of stellar parameters) • Simpler treatments, but calibrated against simulations Note: treatment of convection and hydrodynamics of stellar atmospheres crucial for the abundance determinations, calibrations of photometric indices.

  24. Simulation of convection in the Sun Nordlund et al.

  25. Effects of rotation on stellar structure • Spherically symmetric component of centrifugal force in hydrostatic equilibrium: fairly simple • Effects of circulation and instabilities: extremely hard • Evolution of internal angular momentum: worse Recall uniform slow rotation of solar interior

  26. 20 Msol on the ZAMS Meridional circulation • Circulation and associated instabilities lead to • transport of elements • transport of angular momentum Meynet

  27. Effect of rapid rotation on oscillations 3rd order 2nd order 1st order Analysis by Soufi et al. (1998; Astron. Astrophys. 334, 911)

  28. Development of analysis techniques • Fits to determine global parameters • Must worry about possible multiple maxima in likelihood function: use Monte-Carlo techniques (e.g. genetic algorithm) • Inversion based on just low-degree modes.

  29. Examples of potential analyses • Tests based on artificial data with realistic (we hope) properties • Properties of convective overshoot • Structure of the stellar core

  30. Effect of He ionization Base of convective envelope Monteiro et al. (2000; MNRAS 316, 165)

  31. Signal from base of convective envelope Monteiro et al. (2000)

  32. Inversion for core structure Models: 1 M¯ (Mixed core) – (normal) Degree l = 0 - 3 (Basu et al. 2002; ESA-SP 485, 249)

  33. NASA vision study. Launch 20?? The future: stellar tachoclines??

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