# Neural Networks for Full Phase-space Reweighting and Parameter Tuning

## TL;DR

This paper introduces DCTR, a neural network method for reweighting and tuning particle physics simulations across the full phase space, enabling more precise and comprehensive analysis than existing techniques.

## Contribution

The paper presents a novel neural network-based approach that leverages high-dimensional classifiers for full phase-space reweighting and parameter tuning in particle physics simulations.

## Key findings

- Demonstrates high fidelity in reweighting broad and localized phase space regions.
- Enables estimation of non-perturbative fragmentation uncertainties.
- Shows potential for improved simulation accuracy and systematic uncertainty assessment.

## Abstract

Precise scientific analysis in collider-based particle physics is possible because of complex simulations that connect fundamental theories to observable quantities. The significant computational cost of these programs limits the scope, precision, and accuracy of Standard Model measurements and searches for new phenomena. We therefore introduce Deep neural networks using Classification for Tuning and Reweighting (DCTR), a neural network-based approach to reweight and fit simulations using all kinematic and flavor information -- the full phase space. DCTR can perform tasks that are currently not possible with existing methods, such as estimating non-perturbative fragmentation uncertainties. The core idea behind the new approach is to exploit powerful high-dimensional classifiers to reweight phase space as well as to identify the best parameters for describing data. Numerical examples from $e^+e^-\rightarrow\text{jets}$ demonstrate the fidelity of these methods for simulation parameters that have a big and broad impact on phase space as well as those that have a minimal and/or localized impact. The high fidelity of the full phase-space reweighting enables a new paradigm for simulations, parameter tuning, and model systematic uncertainties across particle physics and possibly beyond.