timeseries) or autocorrelations of the power frequency spectra (e.g., see Scargle 1989; Ransom, Eikenberry & Middleditch 2002). We also note that JB, RAG and SJJ-R worked on “denoised” power frequency spectra of timeseries, using the curvelet transform (Lambert et al. 2006). For analysis of several timeseries, some hounds divided the timeseries into shorter pieces. Power frequency spectra of these shorter pieces were then computed and co-added for subsequent analysis.

In all cases the hounds had to make choices over what ranges in frequency to use for the analyses. Estimates of the large spacings are affected to some extent by the range that is chosen, because the large spacings are frequency dependent. The angle of inclination and the magnitudes of the mode frequency splittings – both of which affect the appearance of the mode peaks, both within multiplets, and from one degree $l$ to another – will also have a subtle effect on the results. The estimates may also be affected by the implementation of the analysis, for example from the manner in which peaks in the autocorrelation, or Fourier transform, of the power frequency spectrum are fitted to yield the estimated spacings. Before we show the main results of the 