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Finetuning Foundation Models for Joint Analysis Optimization
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abstract
In this work we demonstrate that significant gains in performance and data efficiency can be achieved in High Energy Physics (HEP) by moving beyond the standard paradigm of sequential optimization or reconstruction and analysis components. We conceptually connect HEP reconstruction and analysis to modern machine learning workflows such as pretraining, finetuning, domain adaptation and high-dimensional embedding spaces and quantify the gains in the example usecase of searches of heavy resonances decaying via an intermediate di-Higgs system to four $b$-jets.
Forward citations
Cited by 2 Pith papers
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Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough
Verification of ML in fundamental physics is essential precisely when models enter statistical modeling, inference, or hypothesis testing, and is bounded by unavoidable inductive bias, sample complexity, and experimen...
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Communicating Likelihoods with Normalising Flows
A normalizing-flow workflow compresses sample-based likelihoods into small files, validated with a radial Kolmogorov-Smirnov test on three high-energy physics examples.
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