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Time/frequency analysis of some MOST data

Time/frequency analysis of some MOST data. F. Baudin (IAS) & J. Matthews (UBC). Just few words about time/frequency analysis. Classical Fourier transform: FT[S(t)]( w )=  S(t) e i w t dt Windowed Fourier transform: WFT[S(t)]( w ,t 0 ) =  S(t) W(t-t 0 ) e i w t dt

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Time/frequency analysis of some MOST data

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  1. Time/frequency analysisof some MOST data F. Baudin (IAS) & J. Matthews (UBC)

  2. Just few words about time/frequency analysis • Classical Fourier transform: • FT[S(t)](w)=  S(t) eiwt dt • Windowed Fourier transform: • WFT[S(t)](w,t0) =  S(t) W(t-t0) eiwt dt • If W(t) = gaussian => Gabor transform • If W(t,w) => wavelet transform

  3. Just a drawing about time/frequency analysis

  4. MOST data • g Equ [roAp] • z Oph [red giant] • h Boo [Post MS] • Procyon [MS]

  5. g Equ : a simple case?

  6. g Equ : a simple case?

  7. g Equ : a simple case?

  8. g Equ : a simple case of beating Confirmation with simulation: modulation due to beating

  9. z Oph : a more interesting case

  10. z Oph : a more interesting case

  11. z Oph : a more interesting case Signal + sine wave of constant amplitude => noise estimation

  12. z Oph : a more interesting case Temporal modulation not due to noise: which origin?

  13. [h Boo] Noise : not so interesting but…

  14. [h Boo] Noise : not so interesting but… Instrumental periodicities (CCD temperature?)

  15. Procyon: variability of the signal?

  16. Procyon: variability of the signal T < 10 days T > 10 days

  17. Procyon: variability of the signal T < 10 days T > 10 days

  18. Conclusion • Time/Frequency analysis allows : • variation with time of the (instrumental) noise [h Boo, Procyon] • simple interpretation (beating) of amplitude modulation [g Equ] • evidence of temporal variation of modes of unknown origin [z Oph]

  19. [Procyon] Noise : not so interesting but…

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