Rebinning Strategies for Unequally Spaced Data in Spectroscopy Experiments

In summary: There is no definitive answer to this question. It depends on the purpose of the rebinning, and on the data.
  • #1
kelly0303
561
33
Hello! I am working on a spectroscopy experiment and for each wavelength of a laser I have some counts. For the purpose of my question I will make up some data to illustrate my problem, in the table below (these are just numbers, without any relevance for the physical reality of the experiment):
$$
\begin{array}{|c|c|c|c|}
\hline counts & 100 & 100 & 100 & 121 & 121 \\
\hline wavelength & 10\pm 1 & 20 \pm 1 & 30 \pm 1 & 50 \pm 1 & 60 \pm 1 \\
\hline
\end{array}$$

I have some errors on the "wavelength" due to the error on the knowledge of the laser frequency and the error on the counts is just Poisson error. I want to re-bin this data, but I am not sure what is the best way to do it. As you can see, the data is not equally spaced (the wavelength = 40 is missing), so I can't bin in terms of bin width. If I would bin, let's say, in 2 bins between 0 and 30 and between 30 and 60, the first bin would have 300 counts while the second one 242, but this is just because the data is missing, not because the physics process has a lower probability at that wavelength. Should I do the rebinning in terms of number of points per bin? Or how should I proceed? Also, if I do it in terms of points per bin, what would be the value of the wavelength? The average of the points in a bin? And what would be the error? I just do error propagation for the average of N numbers? For my experiment I have few tens of thousands of data points, and the missing data is not regularly spaced, so I would need a general approach for this i.e. not too much data dependent. Thank you!
 
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  • #2
Why do you want to rebin it? The best way to do that will depend on what you want to achieve.
 
  • #3
mfb said:
Why do you want to rebin it? The best way to do that will depend on what you want to achieve.
I want to make a fit to some peaks in the data. However the way it is now, is very noisy. I know where the peaks should be, I just want to make the fit actually work. But it doesn't work so far, so I am trying to do a rebinning to make the data a bit more smooth (I know I would lose some information, but hopefully I can make the fit work). Thank you!
 
  • #4
If rebinning would help the fit then something else went wrong.
 
  • #5
Data analysis doesn't require equal bin sizes. I can't remember the details but, as with sampling of waveforms, the initial sampling need not be regular. One way round the 'bin' problem could be to over-sample and spread the contents of bins over more bins to fill the gap. That would be a form of filtering / interpolation, I guess, and that's a valid thing to do.
 

Related to Rebinning Strategies for Unequally Spaced Data in Spectroscopy Experiments

1. What is rebinning in data analysis?

Rebinning is a process in data analysis where the original data is grouped or binned into larger or smaller intervals. This can help to simplify the data and make it easier to analyze.

2. Why is rebinning important in data analysis?

Rebinning is important in data analysis because it can help to reduce noise and improve the signal-to-noise ratio. It can also make the data easier to interpret and visualize, and can help to identify patterns and trends.

3. How is rebinning different from binning?

Rebinning and binning are similar processes, but they differ in the way that they group the data. Binning usually involves dividing the data into equal-sized intervals, while rebinning allows for more flexibility in the size and number of intervals.

4. What are some common methods for rebinning in data analysis?

Some common methods for rebinning in data analysis include equal-width binning, equal-frequency binning, and quantile binning. These methods differ in the way they group the data and can be used depending on the specific needs of the analysis.

5. What are some potential challenges or limitations of rebinning in data analysis?

One potential challenge of rebinning in data analysis is that it can lead to loss of information, as the original data is being grouped into larger or smaller intervals. Additionally, the choice of bin size and number of bins can greatly impact the results, so it is important to carefully consider these factors when using rebinning in data analysis.

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