Why do we only average and sum over two independent polarizations in scattering?

  • Thread starter knightq
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In summary, when considering the scattering of photon and electron, if the initial photon is unpolarized and the final polarization is not observed, it is only necessary to average the initial polarization and sum over the possible final polarizations, rather than considering all possible combinations of the two independent polarizations. This is because unpolarized light contains all possible polarizations and all possible final polarizations contribute the same amount of intensity.
  • #1
knightq
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Take the scattering of photon and electron as example, we first figure out the amptitude for transition definte polarization,
say f([itex]\epsilon_{\alpha}(\textbf{k}),\epsilon_{\alpha'} (\textbf{k}')[/itex])
If the initial photon is unpolarized and the final polarization is not observed, we need to average the initial polarization and sum over the possible final polarization.
But why we just average and sum over the two independent polarization,[itex]\frac{1}{2}\sum_{\alpha}\sum_{\alpha'}f[/itex]? the initial and final polarization should be all possible combination of the independent polarizatition.
 
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  • #2
The reason why we only average and sum over the two independent polarizations is because we are assuming that the initial photon is unpolarized. Unpolarized light contains all possible polarizations, so it is not necessary to consider all possible combinations of the two independent polarizations. Additionally, since we are not observing the final polarization, we can take advantage of the fact that all possible final polarizations will contribute the same amount of intensity, so it is not necessary to consider each combination individually. Instead, we can just average the initial polarization and sum over the possible final polarizations.
 

Related to Why do we only average and sum over two independent polarizations in scattering?

1. What is the problem of scatter amplitude?

The problem of scatter amplitude is a phenomenon that occurs in scientific experiments, particularly in the field of physics. It refers to the variation or inconsistency in the amplitude of measured data, which can be caused by external factors such as experimental error or noise. This can make it difficult to accurately determine the true amplitude of a signal or phenomenon.

2. How does the problem of scatter amplitude affect scientific research?

The problem of scatter amplitude can significantly impact the accuracy and reliability of scientific research. It can make it difficult to draw meaningful conclusions from experimental data, as the measured amplitudes may not reflect the true values. This can lead to incorrect or misleading findings, which can have serious implications in various fields of science.

3. What are some common causes of the problem of scatter amplitude?

There are several factors that can contribute to the problem of scatter amplitude, including experimental error, measurement noise, and environmental interference. Additionally, the sensitivity of measurement instruments and the complexity of the phenomenon being studied can also play a role in causing scatter amplitude.

4. How can scientists address the problem of scatter amplitude?

There are various methods that scientists can use to minimize the impact of scatter amplitude on their research. These include increasing the precision of measurement instruments, reducing sources of experimental error, and using statistical analysis techniques to account for variation in data. Additionally, conducting multiple trials and averaging the results can also help to mitigate the effects of scatter amplitude.

5. Are there any potential solutions to completely eliminate the problem of scatter amplitude?

While it is not possible to completely eliminate the problem of scatter amplitude, there are ongoing efforts to develop more advanced measurement techniques and technologies that can significantly reduce its impact. This includes the use of advanced signal processing algorithms and machine learning techniques to filter out noise and enhance the accuracy of measurements.

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