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Optimal Transport Maps Calibrate ML Simulations for Particle Physics

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel calibration approach using optimal transport maps to address discrepancies between machine learning simulations and experimental data in particle physics. This method, applied to high-dimensional jet tagging data inspired by the CMS experiment at the Large Hadron Collider, effectively calibrates internal representations. The calibrated high-dimensional representation enables unbiased utilization of foundation models and opens new applications for jet flavor information in LHC analyses, with broader implications for correcting high-dimensional simulations across scientific disciplines. AI

IMPACT This calibration framework could enable the unbiased use of foundation models in particle physics and other scientific fields.

RANK_REASON The cluster contains an academic paper detailing a new methodology for machine learning simulations in particle physics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Optimal Transport Maps Calibrate ML Simulations for Particle Physics

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The cluster contains an academic paper detailing a new methodology for machine learning simulations in particle physics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Malte Algren, Tobias Golling, Francesco Armando Di Bello, Christopher Pollard ·

    Mind the Gap: Navigating Inference with Optimal Transport Maps

    arXiv:2507.08867v3 Announce Type: replace-cross Abstract: Machine learning (ML) techniques have recently enabled enormous gains in sensitivity to new phenomena across the sciences. In particle physics, much of this progress has relied on excellent simulations of a wide range of p…

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