Optimizing Cell-Based Negative Weight Mitigation with Optimal Transport
Abstract
As the accuracy of experimental results in high energy physics (HEP) increases, so does the demand for precision Monte Carlo (MC) simulation. Higher-accuracy event generation at next-to-leading order (NLO) and beyond brings with it negatively weighted events. These negatively weighted events reduce the statistical power of samples, increasing the number of events that need to be produced and straining already limited computational resources. We present a post-hoc reweighting scheme that employs cell-based resampling using an IRC-safe metric to define the cell radii. An optimal metric embeds kinematically similar events within the same cells, minimizing bias when reweighted. This motivates our exploration of a Optimal Transport (OT) based distance metrics. We compare the performance of the reweighting algorithm with different choices of metric, and explicitly demonstrate the performance on simulated Z+jets events produced at NLO accuracy.
Related
- Optimizing optimal transport: Role of final distributions in finite-time thermodynamics
- Optimizing diffusion-limited transport, with applications to electrochemical systems
- Optimizing Atom Transport, Gate-Count and Depth with Parity Twine
- Optimizing bounds for energy-constrained optimal cooling problems in two dimensions
- Optimal weight allocation for steering network dynamics to a target time scale
BibTeX
@misc{hay2026optimizing,
title = {{Optimizing Cell-Based Negative Weight Mitigation with Optimal Transport}},
author = {Lauren Hay and Rishabh Jain and Matt LeBlanc and Jennifer Roloff},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.30357},
eprint = {2609.30357},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.30357},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Optimizing Cell-Based Negative Weight Mitigation with Optimal Transport
AU - Lauren Hay
AU - Rishabh Jain
AU - Matt LeBlanc
AU - Jennifer Roloff
PY - 2026
DA - 2026/09/24
PB - arXiv
DO - 10.48550/arxiv.2609.30357
UR - https://arxiv.org/abs/2609.30357
AB - As the accuracy of experimental results in high energy physics (HEP) increases, so does the demand for precision Monte Carlo (MC) simulation. Higher-accuracy event generation at next-to-leading order (NLO) and beyond brings with it negatively weighted events. These negatively weighted events reduce the statistical power of samples, increasing the number of events that need to be produced and straining already limited computational resources. We present a post-hoc reweighting scheme that employs cell-based resampling using an IRC-safe metric to define the cell radii. An optimal metric embeds kinematically similar events within the same cells, minimizing bias when reweighted. This motivates our exploration of a Optimal Transport (OT) based distance metrics. We compare the performance of the reweighting algorithm with different choices of metric, and explicitly demonstrate the performance on simulated Z+jets events produced at NLO accuracy.
ER -