POWER SPECTRUM MODELIZATION OF HELIOSEISMIC DATA - AN APPLICATION TO THE MEASUREMENT OF SOLAR P-MODE UNCERTAINTIES

We estimate the statistical uncertainties of low-1 solar p-modes param eters based on a Monte Carlo approach. Random perturbations of ideal L orentz profiles L(a, nu(i)) can provide many estimations of the set of p-modes parameters a and allow one to estimate statistical error-bars sigma(a) by modelling the parameters' distribution function. Unlike f requencies, which show symmetric distributions, amplitudes and linewid ths have asymmetric probability density function similar to the distri bution function for time-averaged energies of stochastically excited s olar p-modes (Kumar, 1988). A comparison between sigma(nu) and uncerta inties based on Hessian's computation (Libbrecht 1992, Toutain and App ourchaux 1994) shows a nice agreement. However, our error-bars rake in to account more statistical effects, and rely less on the initial para meters' estimation. Such a technique has been used on the IRIS power s pectra computed from gapped data, and on one GONG power spectrum compu ted from almost continuous data. We also present IRIS linewidths and e rror bars averaged over the years 1989-92 and computed with a fitting strategy using imposed frequency which improves the value of both the parameter and its uncertainty.