33 research outputs found
Neural Empirical Bayes
Peer reviewe
Fitting a Deep Generative Hadronization Model
Hadronization is a critical step in the simulation of high-energy particle
and nuclear physics experiments. As there is no first principles understanding
of this process, physically-inspired hadronization models have a large number
of parameters that are fit to data. Deep generative models are a natural
replacement for classical techniques, since they are more flexible and may be
able to improve the overall precision. Proof of principle studies have shown
how to use neural networks to emulate specific hadronization when trained using
the inputs and outputs of classical methods. However, these approaches will not
work with data, where we do not have a matching between observed hadrons and
partons. In this paper, we develop a protocol for fitting a deep generative
hadronization model in a realistic setting, where we only have access to a set
of hadrons in data. Our approach uses a variation of a Generative Adversarial
Network with a permutation invariant discriminator. We find that this setup is
able to match the hadronization model in Herwig with multiple sets of
parameters. This work represents a significant step forward in a longer term
program to develop, train, and integrate machine learning-based hadronization
models into parton shower Monte Carlo programs.Comment: 14 pages, 4 figure
Designing Observables for Measurements with Deep Learning
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.Comment: Submitted to EPJ