Find FWHM: Pixel Values, Gaussian Curve, Standard Deviation

  • Thread starter bugra
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In summary, the conversation is discussing the calculation of the full width at half maximum (FWHM) for a set of pixel values. One person suggests calculating the standard deviation and multiplying it by 2.355, while another person suggests obtaining a gaussian fit and calculating the FWHM from that. They also mention that if the results are different, it may indicate an issue with the fitting algorithm. There is also a question about whether the FWHM should be the same for multiple sources on the same frame.
  • #1
bugra
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Hi,
I've been searching on google for minutes but couldn't find out a consistent way so far. I mean, it seems everybody has something to say about it :)

My data consists of pixel values (ADU's). I am writing a script to obtain fwhm of seeing disk.

By definition of the gaussian curve, i should only calculate the standart deviation of these values theni get fwhm=stdev*2.355 .
Am I correct?

Or should i obtain a gaussian fit then i calculate fwhm as its name suggests?

Thanks in advance,
bugra
 
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  • #2
If you do it both ways, and the answers are different, what does that tell you about the fitting algorithm that you used?
 
  • #3
for multiple sources on the same frame, should fwhm be same?
 
  • #4
I would think the actual value of FWHM is less important than the rate of change near perfect focus.
 
  • #5


Hi Bugra,

To find the FWHM (full-width at half-maximum) of a Gaussian curve, you can use either method you mentioned. Both approaches should give you the same result.

Calculating the standard deviation of the pixel values and multiplying it by 2.355 is the simplest and most straightforward method. This is based on the fact that the FWHM is equal to approximately 2.355 times the standard deviation of a Gaussian curve. So yes, you are correct in your approach.

Alternatively, you can also use a Gaussian fit to the data and calculate the FWHM from the fit. This method may be more accurate if the data does not perfectly follow a Gaussian distribution. However, it requires more steps and may be more complex to implement in your script.

In summary, both methods are valid and should give you the same result. It ultimately depends on the accuracy and complexity you require for your script. I hope this helps!
 

Related to Find FWHM: Pixel Values, Gaussian Curve, Standard Deviation

1. What is FWHM and why is it important in scientific research?

FWHM stands for full width at half maximum and it is a measure of the width of a peak in a data set. It is important in scientific research because it helps to quantify the spread or resolution of data, which can provide valuable information about the underlying process or phenomenon being studied.

2. How do you find FWHM from pixel values?

To find FWHM from pixel values, you will need to plot the data points on a graph and then identify the peak of the curve. Next, you will need to draw a horizontal line at half the maximum value of the peak. The points where this line intersects with the curve represent the FWHM. You can then calculate the width of the peak by measuring the distance between these two points.

3. What is a Gaussian curve and how does it relate to finding FWHM?

A Gaussian curve, also known as a normal distribution, is a symmetrical bell-shaped curve that represents the probability distribution of a continuous random variable. This curve is often used to model natural phenomena in scientific research. The FWHM of a Gaussian curve refers to the width of the curve at half of its maximum height.

4. How does the standard deviation affect the FWHM of a Gaussian curve?

The standard deviation of a Gaussian curve is directly related to the FWHM. In fact, the FWHM can be calculated by multiplying the standard deviation by 2.35482. This means that as the standard deviation increases, the FWHM also increases, resulting in a wider peak on the curve.

5. Can FWHM be used to compare data sets with different distributions?

Yes, FWHM can be used to compare data sets with different distributions as it provides a standardized measure of the width of a peak. However, it is important to note that the FWHM may not always be the most appropriate measure for comparing data sets and other measures such as the full width at tenth maximum (FWTM) may be more suitable in certain cases.

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