arXiv Machine Learning By Ivan Kharuk (Institute for Nuclear Research of the Russian Academy of Sciences, Moscow Institute of Physics and Technology)

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

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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.

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arXiv Machine Learning
Aug 24

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

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.

By Matthias Schott, Lucie Flek