Speaker
Mr
Nathan Daniel Simpson
(Lund University (SE))
Description
This tutorial will cover how to optimise various aspects of analyses -- such as cuts, binning, and learned observables like neural networks -- using gradient-based optimisation. This has been made possible due to work on the relaxed software package, which offers a set of standard HEP operations that have been made differentiable.
In addition to targeting Asimov significance, we will also use the full analysis significance that incorporates systematic uncertainties as an optimisation objective. Finally, we will reproduce the neos method for learning systematic-aware observables, and you'll see how you can modify it for your use-case.
Author
Mr
Nathan Daniel Simpson
(Lund University (SE))
