SkyLLH documentation
SkyLLH is a Python based framework to develop and to perform general maximum likelihood ratio hypothesis testing. The idea of SkyLLH is to provide a framework with a class structure that is tied to the mathematical objects of the likelihood functions, rather than to entire abstract likelihood models. Hence with SkyLLH it is supposed to be easy to perform an entire maximum likelihood ratio test once the user (likelihood developer) defined the mathematical likelihood function.
User Documentation
- Installation
- Concepts
- Tutorials
- Getting started
- Working with the public 10-year IceCube point-source data
- Creating a configuration
- Getting the datasets
- Getting the analysis
- Initializing a trial
- Maximizing the log-likelihood ratio function
- Calculating the test-statistic
- Unblinding the data
- Calculating the corresponding flux normalization
- Evaluating the log-likelihood ratio function
- Calculating the significance (local p-value)
- Time dependent analyses with the public 10-year IceCube point-source data
- Examples
- skyllh package
- Notes
Developer Documentation