Designing Observables for Measurements with Deep Learning
Owen Long Benjamin Nachman
Many analyses in particle and nuclear physics use simulations to infer
fundamental, effective, or phenomenological parameters of the underlying
physics models. When the inference is performed with unfolded cross sections,
the observables are designed using physics intuition and heuristics. We propose
to design optimal observables with machine learning. Unfolded, differential
cross sections in a neural network output contain the most information about
parameters of interest and can be well-measured by construction. We demonstrate
this idea using two physics models for inclusive measurements in deep inelastic
scattering.