Simulation-based inference produces stronger combined LHC constraints on SMEFT Wilson coefficients than histogram-based inference for four di-boson processes.
Profile Likelihoods on ML-Steroids
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abstract
Profile likelihoods, for instance, describing global SMEFT analyses at the LHC are numerically expensive to construct and evaluate. Especially profiled likelihoods are notoriously unstable and noisy. We show how modern numerical tools, similar to neural importance sampling, lead to a huge numerical improvement and allow us to evaluate the complete SFitter SMEFT likelihood in five hours on a single GPU.
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Unbinning global LHC analyses
Simulation-based inference produces stronger combined LHC constraints on SMEFT Wilson coefficients than histogram-based inference for four di-boson processes.