stellar parameters through analysis of the kepler oscillation data chen jiang & biwei jiang...
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Stellar Parameters through Analysis of the Kepler Oscillation Data
Chen Jiang & Biwei JiangDepartment of AstronomyBeijing Normal University2 April 2010
Kepler Mission : A search for habitable planets
SUCCESSFULLY LAUNCHED:On 7 March 2009 at 03:50 Universal Time (6 March at 10:50 p.m. local time at Kennedy Space Center)
The Extended Solar Neighborhood
Kepler mission will not only be able to search for planets around other stars, but also yield new insights into the parent stars themselves.
How old are stars? How do they evolve? Is the Sun a typical star? How does matter behave under the extreme conditions in stars?
Stellar Parameters Determination
Objects: red giants Data : red giant oscillation data from Kepler
project Code: Yale Stellar Evolution Code (YREC7) Parameters to determine: mass and Z Comparison: L, Teff, and Δν
Solar-like Oscillation in Red Giants
Solar-like oscillations are caused by turbulent convective motions. They are stochastically excited and have very small amplitudes.
Solar-like oscillations are predicted for low-mass main sequence stars and stars located the red edge of the classical instability strip with mass about 1.6Msun, as well as in red giants.
Data Analysis
Purpose: to identify the frequency of maximum power (νmax )
and the large separation of the oscillations (Δ ) from the ν
power spectrum;
Method: Fourier transform to obtain νmax , Δ ;ν
Data: 50 low-luminosity stars (νmax > 100 μHz, L 30L ), long-
cadence(29.4-min sampling), A total of 1639 integrations ( 14
bad ones), 34 days (T. R. Bedding, D. Huber, et al. 2010)
νmax = 100.988 μHz
Δ = 9.8205ν μHz
Light curve and power spectrum of a star in the Kepler Data
The relation between Δ and ν νmax
0.778 0.027max max( / )
Known:
R/R , Teff , log(G) , [Fe/H]
(Z/X) = 0.0245
( Grevesse & Noels, 1993)(Z/X)=0.031
To know: Z , mixing length , age ,
mass
[ / ] log( / ) log( / )sFe H Z X Z X
Estimate the mass:
Kjeldsen & Bedding (1995)
,Toutain & Fröhlich (1992)
Preliminary estimation:
3 2/ ( / ) ( / )M M R R
/ 4.301, 15.2065 0.4 HzR R
134.92 Hz
/ 1.01 0.06M M
Grid of evolutionary tracks:
For the sets of the modelling parameters that agree with the observational constraints, we used a fine resolution,
0.95 1.07 , 0.02
1.8
0.017 0.022,0.54 0.70, 0.001
M M M M M
Z X Z
0.0002Z
Modelling parameters:
/ 1.05,0.017 0.022,0.54 0.70, 0.001M M Z X Z
Observational constraints
0.031
0.98583.6933
0.6336
15.2065
Modelling inputs
M1 M2 M3 M4 M5 M6 M7
0.95 0.97 0.99 1.01 1.03 1.05 1.07
0.0179 0.0180 0.0182 0.0183 0.0184 0.0186 0.0187
0.577 0.580 0.587 0.590 0.593 0.600 0.603
ModelCharacteristics
0.031 0.031 0.031 0.031 0.031 0.031 0.031
0.9845 0.9860 0.9856 0.9867 0.9856 0.9854 0.9865
3.6936 3.6932 3.6925 3.6929 3.6932 3.6928 3.6932
0.6283 0.6299 0.6311 0.6309 0.6297 0.6304 0.6302
5.2890 5.1692 5.0556 4.8624 4.6478 4.6304 4.4580
15.2169 15.2902 15.3838 15.5521 15.7737 15.8887 16.0515
/
log /
log
log /
[ Hz]
Z X
L L
T
R R
Models that agree with the observational constraints
/M M
Z
X
/
log /
log
log /
age[Gyr]
[ Hz]
Z X
L L
T
R R
Way to Go…
Use a criterion to choose the best fitted models, χ2 minimization maybe.
Add to constrain the age of the model.δν Consider the to be a input parameter.α
Thanks!