activity
20242026
most citedExploring Optimal Transport for Event-Level Anomaly Detection at the Large Hadron Collider

3 citations · 3 across the 5 of their papers we have counts for

collaborators

7 papers

hep-ph2026

Collider event distances from the phase space sphere

Nathaniel Craig, Brian Henning, Andrew J. Larkoski +1

Can the distribution of collider events in phase space be used to distinguish the processes that produced them? We present a distance-based classification method that uses the glob…

hep-lat2026

A gauge-invariant measure for lattice chiral fermions

Nathaniel Craig

We nonperturbatively construct a gauge-invariant measure for overlap fermions on four-dimensional Euclidean lattices in a class of nonabelian chiral gauge theories that includes th…

hep-ph2026

The FERMIACC: Agents for Particle Theory

Prateek Agrawal, Nathaniel Craig, Amalia Madden +1

We present the FERMIACC, a scaffolded reasoning model built on OpenAI agents designed to autonomously generate and quantitatively validate theory hypotheses for high energy physics…

hep-ex2025

Optimal Transport for Particle Classification in LArTPC Neutrino Experiments

David Caratelli, Nathaniel Craig, Chuyue Fang +1

The efficient classification of electromagnetic activity from and electrons remains an open problem in the reconstruction of neutrino interactions in Liquid Argon Time Projec…

hep-ph2024

Loops in supergroups

Nathaniel Craig, Emanuele Gendy, Jessica N. Howard

We study the theory of a scalar in the fundamental representation of the internal supergroup . Remarkably, for its tree-level mass does not receive quantum correct…

hep-ph2024

Supergroup Symmetries and the Hierarchy Problem

Nathaniel Craig, Emanuele Gendy, Jessica N. Howard

We show that the mass of a scalar field transforming in the fundamental of an supergroup internal symmetry is protected against one-loop corrections from quartic, gauge…

hep-ph2024★ 3 cited

Exploring Optimal Transport for Event-Level Anomaly Detection at the Large Hadron Collider

Nathaniel Craig, Jessica N. Howard, Hancheng Li

Anomaly detection is a promising, model-agnostic strategy to find physics beyond the Standard Model. State-of-the-art machine learning methods offer impressive performance on anoma…