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

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Presentation on theme: "Time/frequency analysis of some MOST data F. Baudin (IAS) & J. Matthews (UBC)"— Presentation transcript:

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

2 Just few words about time/frequency analysis Classical Fourier transform: FT[S(t)](  )=  S(t) e i  t dt Windowed Fourier transform: WFT[S(t)]( ,t 0 ) =  S(t) W(t-t 0 ) e i  t dt If W(t) = gaussian => Gabor transform If W(t,  ) => wavelet transform

3 Just a drawing about time/frequency analysis

4 MOST data  Equ [roAp]  Oph [red giant]  Boo [Post MS] Procyon [MS]

5  Equ : a simple case?

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

9  Oph : a more interesting case

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11 Signal + sine wave of constant amplitude => noise estimation

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

13 [  Boo] Noise : not so interesting but…

14 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 [  Boo, Procyon] simple interpretation (beating) of amplitude modulation [  Equ] evidence of temporal variation of modes of unknown origin [  Oph]

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20 [Procyon] Noise : not so interesting but…


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