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摘要
We present the first accurate characterization of high-degree modes, derived using the best Michelson Doppler Imager (MDI) full-disk full-resolution data set available. A 90 day long time series of full-disk 2 arcsec pixel(-1) resolution Dopplergrams was acquired in 2001, thanks to the high rate telemetry provided by the Deep Space Network. These Dopplergrams were spatially decomposed using our best estimate of the image scale and the known components of MDI's image distortion. A multi-taper power spectrum estimator was used to generate power spectra for all degrees and all azimuthal orders, up to l = 1000. We used a large number of tapers to reduce the realization noise, since at high degrees the individual modes blend into ridges and thus there is no reason to preserve a high spectral resolution. These power spectra were fitted for all degrees and all azimuthal orders, between l = 100 and l = 1000, and for all the orders with substantial amplitude. This fitting generated in excess of 5.2x10(6) individual estimates of ridge frequencies, line widths, amplitudes, and asymmetries (singlets), corresponding to some 5700 multiplets (l, n). Fitting at high degrees generates ridge characteristics, characteristics that do not correspond to the underlying mode characteristics. We used a sophisticated forward modeling to recover the best possible estimate of the underlying mode characteristics (mode frequencies, as well as line widths, amplitudes, and asymmetries). We describe in detail this modeling and its validation. The modeling has been extensively reviewed and refined, by including an iterative process to improve its input parameters to better match the observations. Also, the contribution of the leakage matrix on the accuracy of the procedure has been carefully assessed. We present the derived set of corrected mode characteristics, which includes not only frequencies, but line widths, asymmetries, and amplitudes. We present and discuss their uncertainties and the precision of the ridge-to-mode correction schemes, through a detailed assessment of the sensitivity of the model to its input set. The precision of the ridge-to-mode correction is indicative of any possible residual systematic biases in the inferred mode characteristics. In our conclusions, we address how to further improve these estimates, and the implications for other data sets, like GONG+ and HMI.
- 出版日期2013-8-1
