I would like to know the difference between a Gaussian function and a Lorentzian function. In particular, is it right to say that the second one is more peaked (sharper) than the first one that has a more smoothed bell-like shape ? In fact, also here it tells that the Lorentzian distribution has a much smaller degree of tailing than Gaussian distribution. Is it true? Could you prove this with some relationship between the Gaussian FHWM and the Lorentzian FHWM, for example ?
My question comes from the fact that I have to fit two curves obtained by experimental data (the ones of the image that I attach) and I was wondering if fitting the sharper peak with a Lorentzian function and the more bell-like shape curve with a Gaussian fuction would give more precise results of the parameters that I am searching (with respect to the case in which I use for both of them the Gaussian equation).
Actually, I fit the red curve using the Lorentzian equation and the blue one (more smoothed) with a Gassian equation in order to find the X value corresponding to the peaks of the two curves (for instance, for the red curve, I wrote a code in which I put the equation of the Lorentzian and left the parameter, which I am interested in, free so that the fitting gives me its value).
