← Back to all news
arXiv Machine Learning By Matthias Schott, Lucie Flek

Learning Minimal-Deviation Corrections for Multi-Dimensional Mismodelling in HEP Simulations

Read the original on arXiv Machine Learning →

The paper introduces a neural‑network method that learns a minimal‑deviation transformation of Monte Carlo simulated events to match one‑dimensional target distributions while preserving the multidimensional correlation structure of the original simulation. By operating under limited experimental information, the approach avoids the pitfalls of traditional one‑dimensional reweighting and the data‑hungry fully multidimensional corrections. Controlled pseudo‑data studies demonstrate improved agreement with target distributions and consistent multidimensional structure, making the method suitable for complex, high‑dimensional analyses where conventional techniques fall short.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Aug 20

Safe Domain Adaptation for Physics: Overcoming Nuisances, Label Shifts, and Simulation Priors

The paper introduces a new approach to domain adaptation in physics, addressing the fact that simulations often differ from experimental data not only in nuisances but also in the target quantity distribution. By studying a toy air‑shower benchmark with separate nuisance, simulation, and spectrum shifts, the authors show that standard adversarial adaptation can misalign spectra, leading to bias. They propose adaptive domain adaptation that reweights simulated events to focus on genuine physical mismatches and provide a label‑free rule for selecting the best model configuration.

By Ivan Kharuk (Institute for Nuclear Research of the Russian Academy of Sciences, Moscow Institute of Physics and Technology)
fine-tuningbenchmarkssafety
More like this →
arXiv Machine Learning
Sep 10

Mind the Gap: Navigating Inference with Optimal Transport Maps

The paper introduces a model calibration method using optimal transport to address discrepancies between simulation and experimental data in high-dimensional machine learning applications. Applied to jet tagging in particle physics, the technique calibrates a 128‑dimensional latent representation from a general‑purpose classifier, ensuring downstream derived quantities are properly calibrated. This enables more reliable use of foundation models for jet flavor analysis in LHC experiments and offers a general framework for correcting high‑dimensional simulations across scientific fields.

By Malte Algren, Tobias Golling, Francesco Armando Di Bello, Christopher Pollard
More like this →