Speaker
Description
Abstract: Machine learning methods are now ubiquitous in physics, but often target objectives that are one or two steps removed from our physics goals. A prominent example of this is the discrimination between signal and background processes, which doesn’t account for the presence of systematic uncertainties — something crucial for the calculation of quantities such as the discovery significance and upper limits.
To combat this, we show that physics analysis workflows can be optimised in an end-to-end fashion, including the treatment of nuisance parameters that model systematic uncertainties, provided that the workflow is differentiable. By leveraging automatic differentiation and surrogates for non-differentiable operations, we’ve made this possible for the first time, and demonstrate its use in a proof-of-concept scenario.
This talk will motivate the use of end-to-end optimisation as described above, cover the techniques that make it possible, and show recent developments in a high-energy physics context. Future directions that aim to scale and apply these methods will also be highlighted.
