# How to properly bin the data for a fit

I am working on a spectroscopy project in which we adjust the wavelength of a laser and get some counts on the detector from some laser-atom interactions. The data that we have is in the form: $$(\lambda$$, $$dt$$, $$dN)$$, where $$dt$$ is a time interval, $$\lambda$$ is the laser wavelength used in that time interval, and $$dN$$ is the number of events in that time interval.

I need to make a plot of the event rate ($$\frac{dN}{dt}$$) vs wavelength, and fit it with a Voigt profile. The wavelength is scanned over a long range. However, each individual wavelength is scanned for a short period of time i.e. $$dt$$ is small, but the difference between 2 consecutive wavelengths is small too. For example, an entry could be $$(10000 cm^{-1},0.01 s, 2)$$ and the next one could be $$(10000.1 cm^{-1},0.01 s,3)$$.

I need a bit of help related to how to do the fit properly and get a meaningful number for the peak of the Voigt profile. Given the numbers, it seems that I need to re-bin the data in frequency space (I might use frequency, wavelength or wavenumber interchangeably, what I mean is the x axis which in my case has units of $$cm^{-1}$$, sorry for that).

Is this a good thing to do? And how should I do the re-binning, as I get slightly different results for each re-binning. Right now I have the value of the peak for several (15) different binnnings, which are quite close, yet a bit different, for example: $$11001.5 \pm 0.2$$ and $$11001.4 \pm 0.3$$, where the error is given by the fitting program (I guess it is the standard deviation associated with the best estimate of the parameters, but I can check in more details if needed; I use lmfit in python).

I was thinking to use the mean of these as the reported value, but I am not sure what to use for the error. These numbers are clearly not independent (i.e the value of the peak when I double the bin size is not independent of the value before that, right?) so I can't just use $$\sigma/\sqrt{N}$$ for the error on mean.

Also how should I take into account the error on each measurement (the $$0.2$$ and $$0.3$$ in my examples above)? Or should I try a totally different approach? Any suggestion would be greatly appreciated. Thank you!

• You are assuming knowledge many of us do not have. What is a Voigt profile, for example? – mkt - Reinstate Monica Sep 24 at 9:52
• @mkt Voigt is a commonly-used peak equation used in spectroscopy, see en.wikipedia.org/wiki/Voigt_profile – James Phillips Sep 24 at 10:33
• Just a question: Why use a full fit to the curve when you are mainly interested in the peak location and height? Have you considered using a specific peak-finding algorithm, e.g. like this one: stackoverflow.com/questions/22583391/… – mzunhammer Sep 24 at 12:02
• Is your example of different peak location estimates typical? If so, you have no problem, because there is no material difference between $11001.5\pm0.2$ and $11001.4\pm0.3:$ they are indistinguishable within the errors given. This process of varying the bins approximates the process I recently described of varying density estimator bandwidths to explore modes (that is, peaks) of empirical densities: see stats.stackexchange.com/a/428083/919. Your study seems to be conducted in a similar spirit, so similar considerations might apply. – whuber Sep 24 at 12:15
• @mzunhammer I actually need more than just the position of the peak from the Voigt profile. I need all the parameters of the curve, so just the location wouldn't be enough. – user260669 Sep 24 at 14:02