Universal New Physics Latent Space
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Abstract (arXiv)
We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (latent) space while conserving information about the relationship between the underlying theories. We apply our method to three examples of new physics at the LHC of increasing complexity, showing that models can be clustered according to their LHC phenomenology: different models are mapped to distinct regions in latent space, while indistinguishable models are mapped to the same region. This opens interesting new avenues on several fronts, such as model discrimination, selection of representative benchmark scenarios, and identifying gaps in the coverage of model space.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 34 references that are in the map. First posted on arXiv 29 Jul 2024.
Builds on
9 of 34 references.
References · 9
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2020Supervised Contrastive Learningcited by 2,803
- 2021Machine Learning in the Search for New Fundamental Physicscited by 50
- 2023Improving Contrastive Learning of Sentence Embeddings from AI Feedbackcited by 29
- 2023Machine Learning for Anomaly Detection in Particle Physicscited by 29
- 2021The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collidercited by 28
- 2021An Exploration of Learnt Representations of W Jetscited by 8
- 2022Neural Embedding: Learning the Embedding of the Manifold of Physics Datacited by 7
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.