  The advent of deep learning has yielded powerful tools to automatically
compute gradients of computations. This is because training a neural network
equates to iteratively updating its parameters using gradient descent to find
the minimum of a loss function. Deep learning is then a subset of a broader
paradigm; a workflow with free parameters that is end-to-end optimisable,
provided one can keep track of the gradients all the way through. This work
introduces neos: an example implementation following this paradigm of a fully
differentiable high-energy physics workflow, capable of optimising a learnable
summary statistic with respect to the expected sensitivity of an analysis.
Doing this results in an optimisation process that is aware of the modelling
and treatment of systematic uncertainties.
