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?

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

9.  Oph : a more interesting case

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]

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