Speaker
Description
In recent years, the ATLAS collaboration has provided full statistical models for some of their analyses, enabling highly precise reinterpretation of experimental limits. These models account for multiple nuisance parameters and correlations between signal bins, but their complexity often leads to lengthy computation times. This project aims to develop a method for efficient yet accurate reinterpretation of experimental results in phenomenological studies. Specifically, we are training Deep Neural Networks (DNNs) to perform likelihood interpolation, serving as surrogates for full statistical models. This approach can reduce computation times by several orders of magnitude while maintaining high precision.
In my talk, I will introduce the project and present recent advancements, including the development of a framework for generating data with Markov Chain Monte Carlo (MCMC) methods, training Neural Networks to interpolate likelihoods, and validating these models on real-world analyses. Our approach has been tested on several experimental analyses, demonstrating promising results. The long-term goal is to create a publicly available and maintainable database of trained machine learning models that can be integrated into various reinterpretation tools, providing a valuable resource for the particle physics community.
| Track | Theory |
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