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Speaker: Matthias Vigl (Technical University of Munich) – Title: Machine-Learning Scaling Laws for LHC Physics: How Scale Unlocks Breakthrough Gains in Physics Sensitivity
January 13 @ 4:00 pm - 5:00 pm
Research Progress Meeting
Date: January 13, 2026
Time: 4:00- 5:00 pm
Location: Sessler Conference Room- 50A-5132 [In-Person and HYBRID]
Speaker: Matthias Vigl (Technical University of Munich)
Title: Machine-Learning Scaling Laws for LHC Physics: How Scale Unlocks Breakthrough Gains in Physics Sensitivity
Abstract: High Energy Physics and deep learning have historically taken different routes to data processing: in collider physics, performance has been driven by deep, hand-engineered pipelines that encode decades of domain knowledge, while modern machine learning has advanced primarily through scale, leveraging large datasets and increasingly generic model architectures. While machine learning has long been embedded in the HEP analysis pipeline, the rate of improvement has remained slower than the rapid, scale-driven progress observed in industry.
This talk contrasts physics-driven and scale-driven approaches to data processing and shows how foundation-model principles (scaling laws, transfer learning, and end-to-end optimization) can be applied to HEP analyses. Compute-optimal scaling laws are derived for the state-of-the-art ATLAS jet flavor tagger and validated by training models two orders of magnitude beyond previous compute regimes, yielding predictable performance improvements in line with industry-scale models. When translated into physics sensitivity for flagship ATLAS analyses such as HH(4b) at the High-Luminosity LHC, these gains correspond to improvements equivalent to multiple years of data taking, motivating a shift toward large-scale ML model training and deployment within LHC experiments.
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https://lbnl.zoom.us/j/95679892182?pwd=RU5xU2dDRFNabnR1U3pQMklkYWFIdz09
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