# Parameterized Machine Learning for High-Energy Physics

arXiv: https://arxiv.org/abs/1601.07913 · HTML: https://mlsanity.com/paper/1601.07913/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2016.

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- [Knothe-Rosenblatt transport for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2110.02716/) (2021)
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- [Fine-Tuning Vision-Language Models for Neutrino Event Analysis in High-Energy Physics Experiments](https://mlsanity.com/paper/2508.19376/) (2025)
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## Abstract (arXiv)

We investigate a new structure for machine learning classifiers applied to problems in high-energy physics by expanding the inputs to include not only measured features but also physics parameters. The physics parameters represent a smoothly varying learning task, and the resulting parameterized classifier can smoothly interpolate between them and replace sets of classifiers trained at individual values. This simplifies the training process and gives improved performance at intermediate values, even for complex problems requiring deep learning. Applications include tools parameterized in terms of theoretical model parameters, such as the mass of a particle, which allow for a single network to provide improved discrimination across a range of masses. This concept is simple to implement and allows for optimized interpolatable results.