Silvio, How Autobk / spline() works: First, the definition of a spline: A spline is a flexible function that is made up from a series of polynomial segments. Each polynomial is 4th order (ie, a 'cubic polynomial' a + b*x + c*x*x + d*x*x*x). The polynomial segments are joined together such that the value are first two derivatives are continuous across the joint (also known as a 'knot'). With this definition, and a few constraints about the endpoints, a flexible, smooth function can be defined simply defining the y values at the knots, and the flexibility of the function is determined by the number and placement of the knots. Autobk / spline() uses such a spline with Nbkg knots evenly spaced in k-space to approximate mu0(E), where Nbkg = 1 + (2*Delta K * Rbkg )/ pi . where Delta_K is the k-range of the data (or to be considered, as using kmin=~0.5 is common and sometimes you specify kmax). In ifeffit, Nbkg is truncated, not rounded. That gives the *number* of variables, quantifying the flexibility of mu0(E).. The values of the mu0(E) spline at the Nbkg knots are then optimized in a fitting procedure. The criteria for "best values" is that chi(R) = FT{chi(k)=[mu(k)-mu0(k)]/step} be "as small as possible" between 0 and Rbkg. [When using a standard spectra, 'as small as possible' is replaced with 'as close to the the standard as possible']. The fit procedure goes something like this: guess values of mu0(E_i) for i = 1, N_bkg knots subtract this background FT this result generate sum of squares chi(R) between 0 and R_bkg refine values of mu0(E_i) and start over So, Rbkg has two purposes: a) set the flexibility of mu0(E) b) set the highest R component to consider in the optimization. In signal processing, Rbkg is commonly called a Nyquist frequency. You can also think of it as the frequencey of a high-pass filter It isn't perfectly sharp, so there can be changes in spectral content around R=Rbkg with small tweaks to Rbkg. I highly recommend playing wtih Rbkg with some decent model compound data (where you know the first shell species). Try setting Rbkg=0.2, 1.0, 2.0, and 5.0. With Athena, you can nicely clone a group and plot all the results together in E, k, and R-spaces. You'll see that you're removing low frequency components, that Rbkg really is (roughly) the cutoff, and why you don't want to use Rbkg=5.0! Hope that clears up most questions, --Matt