Time/frequency analysisof some MOST data

F. Baudin (IAS) & J. Matthews (UBC)

Just few words about time/frequency analysis

• Classical Fourier transform:

FT[S(t)]()= S(t) eit dt

• Windowed Fourier transform:

WFT[S(t)](,t0) = S(t) W(t-t0) eit dt

If W(t) = gaussian => Gabor transformIf W(t,) => wavelet transform

Just a drawing about time/frequency analysis

MOST data

• Equ [roAp]• Oph [red giant]• Boo [Post MS]• Procyon [MS]

Equ : a simple case?

Equ : a simple case?

Equ : a simple case?

Equ : a simple case of beating

Confirmation with simulation: modulation due to beating

Oph : a more interesting case

Oph : a more interesting case

Oph : a more interesting case

Signal + sine wave of constant amplitude => noise estimation

Oph : a more interesting case

Temporal modulation not due to noise: which origin?

[ Boo] Noise : not so interesting but…

[ Boo] Noise : not so interesting but…

Instrumental periodicities (CCD temperature?)

Procyon: variability of the signal?

Procyon: variability of the signal

T < 10 days

T > 10 days

Procyon: variability of the signal

T < 10 days

T > 10 days

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]

[Procyon] Noise : not so interesting but…