Statistical Analysis in High-Energy Physics: Methods and Applications

In summary, the conversation discusses the various statistical tests and methods used in estimating parameters of the standard model in particle physics. The maximum likelihood method is commonly used, but different groups may use different methods. There is also debate and collaboration among groups to get analyses approved. Some groups also use more advanced methods such as neural nets and decision trees. At a higher level, various groups perform statistical analyses of experimental results using different methods, such as Bayesian methods. The Particle Data Group serves as a comprehensive source for compiling and comparing these approaches and their results.
  • #1
iibewegung
16
0
Hi,

Could anyone tell me what type of statistical test is used to estimate the parameters of the standard model?

I hear many particle physicists say, eg. "we have a 95% confidence that this quark mass falls between A and B" and what immediately comes to mind are the methods of statistical hypothesis testing like... z test or t test.

Any explanations or comments about the statistical analysis being done on the data that we get from colliders?
Thanks in advance!
 
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  • #2
When I was a grad student in HEP many years ago, we commonly used the maximum likelihood method. Don't press me on the details, though. It's been a long time since I did this stuff. :redface:
 
  • #3
Different groups can and do use different methods. I (as a theorist) imagine that debating what methods to use is a big part of life in the major experimental collaborations (ATLAS, CMS, CDF, DØ, etc.), which are the ones who directly analyze collider data. I believe standard procedure is for smaller working groups to perform analyses that they then try to get approved by various committees. As signals get harder to see, more elaborate methods (neural nets, decision trees) have to be used.

http://physics.bu.edu/~tulika/Teaching/Spring09/lectures/Lecture8.pdf" are some slides you may find interesting.

At a higher level, various groups (http://www.utfit.org/", etc.) perform statistical analyses of many experimental results, again using different methods. (The UTFit group likes Bayesian methods, for instance.)

The http://pdg.lbl.gov/" is the one-stop spot for compiling and comparing the various approaches and their results.
 
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Related to Statistical Analysis in High-Energy Physics: Methods and Applications

1. What is the purpose of statistical tests in High Energy Physics (HEP)?

Statistical tests in HEP are used to analyze and interpret data collected from particle collisions and experiments. They are essential for making conclusions about the underlying physics processes and determining the significance of any observed results.

2. What are the most commonly used statistical tests in HEP?

The most commonly used statistical tests in HEP include the Chi-square test, the t-test, the F-test, and the Kolmogorov-Smirnov test. These tests are used to compare observed data with expected distributions, determine the significance of differences between samples, and assess the goodness of fit for a specific model.

3. How do statistical tests in HEP account for uncertainties and errors in data?

In HEP, statistical tests take into account uncertainties and errors in data by using probability distributions and confidence intervals. These allow for the quantification of the level of confidence in the results and provide a measure of the uncertainty associated with the data.

4. What is the role of p-values in statistical tests used in HEP?

P-values are used in statistical tests in HEP to determine the likelihood of obtaining the observed results by chance. A lower p-value indicates a higher level of significance, meaning that the results are less likely to be due to random chance. P-values are typically compared to a predetermined threshold (e.g. 0.05) to determine if the results are statistically significant.

5. How do researchers choose which statistical test to use in HEP?

The choice of statistical test in HEP depends on the specific research question, the type of data being analyzed, and the assumptions of the underlying statistical model. Researchers must carefully consider these factors and choose the most appropriate test to ensure accurate and reliable results.

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