- #1
kelly0303
- 580
- 33
Hello! 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!