3 citations · 3 across the 5 of their papers we have counts for
5 papers
Dissecting Parton Showers with Multi-Point Energy Correlators
Mark Gonzalez, Philip Harris, Kyle Lee +2
The last several years have seen tremendous progress in the ability to both compute and measure multi-point correlations in energy flux. The highly differential nature of energy co…
AI Agents Can Already Autonomously Perform Experimental High Energy Physics
Eric A. Moreno, Samuel Bright-Thonney, Andrzej Novak +2
Large language model-based AI agents are now able to autonomously execute substantial portions of a high energy physics (HEP) analysis pipeline with minimal expert-curated input. G…
Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies
Gaia Grosso, Sai Sumedh R. Hindupur, Thomas Fel +3
Modern artificial intelligence has revolutionized how we extract representations from scientific data, yet the statistical properties of these representations remain poorly control…
AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing
Samuel Bright-Thonney, Christina Reissel, Gaia Grosso +6
Novelty detection in large scientific datasets faces two key challenges: the noisy and high-dimensional nature of experimental data, and the necessity of making statistically robus…
Chained Quantile Morphing with Normalizing Flows
Samuel Bright-Thonney, Philip Harris, Patrick McCormack +1
Accounting for inaccuracies in Monte Carlo simulations is a crucial step in any high energy physics analysis. It becomes especially important when training machine learning models,…