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We present a new and highly efficient algorithm for computing a power spectrum made from evenly spaced data which combines the noise‐reducing advantages of the weighted fit with the computational advantages of the fast Fourier transform. We apply this method to a 10‐yr data set of the solar p‐mode oscillations obtained by the Birmingham Solar Oscillations Network (BiSON) and thereby uncover three new low‐frequency modes. These are the ℓ= 2,  n = 5 and  n = 7 modes and the ℓ= 3,  n =7 mode. In the case of the ℓ= 2,  n = 5 mode, this is believed to be the first such identification of this mode in the literature. The statistical weights needed for the method are derived from a combination of the real data and a sophisticated simulation of the instrument performance. Variations in the weights are due mainly to the differences in the noise characteristics of the various BiSON instruments, the change in those characteristics over time and the changing line‐of‐sight velocity between the stations and the Sun. It should be noted that a weighted data set will have a more time‐dependent signal than an unweighted set and that, consequently, its frequency spectrum will be more susceptible to aliasing.

A new efficient method for determining weighted power spectra: detection of low‐frequency solar p‐modes by analysis of BiSON data