Speaker
Description
The use of statistical models to accurately represent and interpret data from high-energy physics experiments is crucial to gaining a better understanding of the results of those experiments. However, there are many different methods and models that researchers are using for these representations, and although they often generate results that are useful for everyone in the field of HEP, they also often slightly deviate in results between different models, and that deviation is difficult to interpret. Fortunately, many statistical models use similar frameworks, as well as the same mathematics, so it is quite feasible to convert a model generated in one environment to a different environment. This will allow the results to be more consistently replicated across models, as well as give deeper insight into certain differences in results between models. In addition, both pyhf and Combine offer unique tools and nuances within their respective models, and an easy conversion would allow someone that is familiar with one environment to develop the model in that environment, and then transfer it to the other in order to take advantage of both sets of tools. The python script that I have developed successfully does this conversion, and I have also verified mathematical agreement between the models. Even with complex models, the inferences made by both pyhf and Combine agree within a 5% of the relative uncertainty of those inferences. Moreover, even for large models, the conversion takes less than 30 seconds.
