arXiv subjects

Lauren Hay

Publications and source records attributed to Lauren Hay.

5 recordsLinked to original sources

arXiv2026

Optimizing Cell-Based Negative Weight Mitigation with Optimal Transport

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.

hep-ph↗

arXiv2026

A Phase-Space Inclusive Figure of Merit Based in Optimal Transport for Validating Monte Carlo Reweightings

Validating whether the underlying physics of a model has been retained after a full phase-space reweighting poses a unique challenge. Often, validation relies on comparing histograms of 1D observables; however, this can mask correlations and biases in the complete prediction. We present a novel, unbinned approach to comparing the performance of such reweighting schemes based on the "Cross-Section-Mover's Distance", an application of Optimal Transport that quantifies the work required to transform one theoretical prediction into another and enables an interpretation of results in terms of metric spaces. We demonstrate its utility when benchmarking various reweighting schemes that mitigate the effects of negative weights in a Monte Carlo simulation. This approach can be broadly applied in other scenarios where biases in full phase-space reweighting schemes should be studied in an unbinned way.

hep-ph↗

arXiv2026

Learning the Geometry of Collider Events with Metric-Aware Deep Sets

Optimal transport gives structured data a geometry, but exact evaluation is costly in large pairwise analyses that exploit relationships among distances. Learned surrogates are faster, but need not preserve this metric structure. We develop a Deep Sets surrogate for OT between variable-size weighted point clouds that enforces non-negativity, exchange symmetry, and zero self-distance, leaving the triangle inequality unconstrained. Applied to the Energy Mover's Distance between collider events in a particle physics application, the Metric-Aware Particle Flow Network achieves percent-level mean absolute percentage error while significantly improving inference throughput over other exact and approximate methods surveyed. The architectural constraints are found to improve properties that are not explicitly enforced: across $10^6$ held-out event triplets, triangle-inequality violations fall from 199 for a matched unconstrained network to 2, and the maximum from 149.5 to 5.8 GeV. These results demonstrate that targeted inductive biases can yield fast neural surrogates with substantially improved geometric fidelity.

hep-ph↗

arXiv2026

Optimal-Transport-Based Cell Resampling for Negative and Pathological Event Weights

Negative and pathologically large Monte Carlo event weights strain the computing budgets of experiments at the Large Hadron Collider. Cell resampling algorithms locally redistribute event weights among nearby events in a metric space. We study the performance of metrics defined in terms of Optimal Transport, namely the Energy Mover's Distance and a spectral variant, in the context of such algorithms. As these metrics are insensitive to the addition of soft and collinear radiation, they may be applied directly to particles at any stage of event generation. When applied to samples simulated at next-to-leading-order in quantum chromodynamics, this approach reduces the observed bias relative to other cell resampling techniques presented in the literature. We also study the Cross-Section Mover's Distance as an unbinned, broadly-applicable figure of merit for quantifying the bias introduced by any full-phase-space reweighting.

hep-ph↗

arXiv2021

Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation

A framework is presented to extract and understand decision-making information from a deep neural network (DNN) classifier of jet substructure tagging techniques. The general method studied is to provide expert variables that augment inputs ("eXpert AUGmented" variables, or XAUG variables), then apply layerwise relevance propagation (LRP) to networks both with and without XAUG variables. The XAUG variables are concatenated with the intermediate layers after network-specific operations (such as convolution or recurrence), and used in the final layers of the network. The results of comparing networks with and without the addition of XAUG variables show that XAUG variables can be used to interpret classifier behavior, increase discrimination ability when combined with low-level features, and in some cases capture the behavior of the classifier completely. The LRP technique can be used to find relevant information the network is using, and when combined with the XAUG variables, can be used to rank features, allowing one to find a reduced set of features that capture part of the network performance. In the studies presented, adding XAUG variables to low-level DNNs increased the efficiency of classifiers by as much as 30-40\%. In addition to performance improvements, an approach to quantify numerical uncertainties in the training of these DNNs is presented.

physics.data-an↗