Changes

<math>\lambda_{(p)}, \lambda_{(s)}, \sigma_0, \sigma_1, g, x_0 \quad</math> are the fit parameters. Note the absence of a vertical scale parameter. The vertical scale depends on the number of samples collected, whereas the equation in this model is normalized.  Rescaling works as follows:

<math>\lambda_{(p)}, \lambda_{(s)}, \sigma_0, \sigma_1, g, x_0 \quad</math> are the fit parameters. Note the absence of a vertical scale parameter. The vertical scale depends on the number of samples collected, whereas the equation in this model is normalized.  Rescaling works as follows:

If <math>f(x)=T*f(q)</math>, where <math>T</math> is a vertical scaling parameter and since <math>dq=dx/g</math>,  

If <math>f(x)=T\,f(q)</math>, where <math>T</math> is a vertical scaling parameter and since <math>dq=dx/g</math>,  

<math>\int_{-\infty}^{\infty} f(x)\, dx = Tg \int_{-\infty}^{\infty} f(q)\, dq = Tg </math> implies that Tg is the number of events collected times the bin width (in Vs).  

<math>\int_{-\infty}^{\infty} f(x)\, dx = Tg \int_{-\infty}^{\infty} f(q)\, dq = Tg </math> implies that Tg is the number of events collected times the bin width (in Vs).