Neural Conditional Reweighting

Abstract

There is a growing use of neural network classifiers as unbinned, high-dimensional (and variable-dimensional) reweighting functions. To date, the focus has been on marginal reweighting, where a subset of features are used for reweighting while all other features are integrated over. There are some situations, though, where it is preferable to condition on auxiliary features instead of marginalizing over them. In this paper, we introduce neural conditional reweighting, which extends neural marginal reweighting to the conditional case. This approach is particularly relevant in high-energy physics experiments for reweighting detector effects conditioned on particle-level truth information. We leverage a custom loss function that not only allows us to achieve neural conditional reweighting through a single training procedure, but also yields sensible interpolation even in the presence of phase space holes. As a specific example, we apply neural conditional reweighting to the energy response of high-energy jets, which could be used to improve the modeling of physics objects in parametrized fast simulation packages.

15 pages, 8 figures. v2: journal version

References in corpus (12)

• Array Programming with NumPy
• PYTHIA 6.4 Physics and Manual
• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• An Introduction to PYTHIA 8.2
• Matching NLO QCD computations with Parton Shower simulations: the POWHEG method
• Event generation with SHERPA 1.1
• Herwig++ Physics and Manual
• Improving the Simulation of Quark and Gluon Jets with Herwig 7
• Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution
• A survey of machine learning-based physics event generation
• Resonance Searches with Machine Learned Likelihood Ratios
• Deep-Learned Event Variables for Collider Phenomenology

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• Reweighting simulated events using machine-learning techniques in the CMS experiment
• Describing Hadronization via Histories and Observables for Monte-Carlo Event Reweighting