Uncertainty estimation in pgopher

In summary, uncertainty estimation in pgopher is a method for quantifying the uncertainties associated with spectroscopic data. It allows scientists to assess the reliability and accuracy of their measurements, and is performed using a Monte Carlo approach. Factors such as input data quality and model complexity can affect the estimation, and the resulting uncertainty values represent the range of possible values for each parameter. These values should be taken into consideration when interpreting and reporting spectroscopic data.
  • #1
BillKet
312
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Hello! Can someone who used pgopher before (I am fitting a diatomic molecular spectra) help me understand how does it calculate the uncertainty on the parameters when doing a line fit. I found very little online and it is not totally clear to me. Mainly I am not sure how, just by providing the relative uncertainties between different assigned lines, one can get the uncertainty on the parameters. Naively (my statistics is not great), I would imagine that if I have an uncertainty of, say, 0.1 cm##^{-1}## on the measured transitions the error on the parameters would be much bigger compared to when I would have, say 0.0001 cm##^{-1}## uncertainty. Yet, in pgopher, you would assign a "1" as the Std Dev parameter in both cases (i.e. you would say that all the lines have the same error), so the information about the magnitude of the errors would be gone. What am I missing?
 
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  • #2
When using pgopher to fit a diatomic molecular spectrum, the uncertainty on the parameters is calculated by taking into account the relative uncertainty between different assigned lines. Specifically, the Std Dev parameter that you enter for each line indicates how much larger the uncertainty is for that line compared to the other lines. For instance, if you assign 0.0001 cm##^{-1}## as the Std Dev for one line and 1 cm##^{-1}## for another line, pgopher will calculate the appropriate uncertainty for each parameter based on these relative uncertainties. This means that even though you are assigning the same Std Dev value for both lines, pgopher will take into account the magnitude of the errors when calculating the uncertainty on the parameters.
 

Related to Uncertainty estimation in pgopher

1. What is uncertainty estimation in pgopher?

Uncertainty estimation in pgopher is a process used to determine the accuracy and reliability of measurements and calculations performed in the pgopher software. It involves quantifying the potential errors and uncertainties in the data and results, and providing a measure of confidence in the final values.

2. Why is uncertainty estimation important in pgopher?

Uncertainty estimation is important in pgopher because it allows for a better understanding of the limitations and potential errors in the data and results. This information is crucial for making informed decisions and interpretations based on the data, and for ensuring the accuracy and reliability of scientific research.

3. How is uncertainty estimation performed in pgopher?

Uncertainty estimation in pgopher is typically performed by using statistical methods such as Monte Carlo simulations or error propagation calculations. These methods involve randomly varying the input parameters and performing multiple calculations to determine the range of possible outcomes and the associated uncertainties.

4. Can uncertainty estimation be applied to all types of data in pgopher?

Yes, uncertainty estimation can be applied to all types of data in pgopher, including experimental measurements and theoretical calculations. However, the specific methods and techniques used may vary depending on the type of data and the level of uncertainty present.

5. How can the results of uncertainty estimation in pgopher be interpreted?

The results of uncertainty estimation in pgopher can be interpreted by looking at the range of possible values for the calculated parameters and their associated uncertainties. This information can be used to determine the confidence level in the final results and to identify any potential sources of error that may need to be addressed.

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