Getting started

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

Slack channel: #skyllh

The user can find pre-defined IceCube log-likelihood analyses in i3skyllh project.

SkyLLH’s analysis workflow

To set-up and run an analysis the following procedure applies:

  1. Create an analysis instance (preferably based on pre-defined create_analysis functions). It takes care of the following parts:

    1. Add the datasets and their PDF ratio instances via the Analysis.add_dataset method.

    2. Construct the log-likelihood ratio function via the Analysis.construct_llhratio method.

  2. Call the Analysis.do_trial or Analysis.unblind method to perform a random trial or to unblind the data. Both methods will fit the global fit parameters using the set up data. Finally, the test statistic is calculated internally via the Analysis.calculate_test_statistic method.