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Open Access Publications from the University of California

Scholarly Works (2 results)

  • Thesis
  • Peer Reviewed
UC Irvine Electronic Theses and Dissertations (2021)

Several theoretical parameter spaces are analysed using techniques from machine learning. First, machine learning is used to leverage high dimensional detector information to constrain the mass and coupling of a simplified model which extends the standard model with a heavy Z' boson. The high dimensional information is seen to improve the exclusion contours of the analysis relative to a low dimensional analysis that instead uses summary statistics. Next, generative models are used to sample a high dimensional parameter space of a supersymmetric model subject to a constraint. Generative models are seen to provide an increase in efficiency of over an order of magnitude in a search for models that satisfy the constraint when compared to random sampling. Finally, we analyse a dataset of supersymmetric theories to understand how they may best discovered in experiment. We propose a set of experiments to be performed at the Large Hadron Collider that are most sensitive to these models.

    Cover page: Machine learning applied to parameter spaces of theories beyond the Standard Model
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2021)
    Models of physics beyond the Standard Model often contain a large number of parameters. These form a high-dimensional space that is computationally intractable to fully explore. Experimental constraints project onto a subspace of viable parameters, but mapping these constraints to the underlying parameters is also typically intractable. Instead, physicists often resort to scanning small subsets of the full parameter space and testing for experimental consistency. We propose an alternative approach that uses generative models to significantly improve the computational efficiency of sampling high-dimensional parameter spaces. To demonstrate this, we sample the constrained and phenomenological Minimal Supersymmetric Standard Models subject to the requirement that the sampled points are consistent with the measured Higgs boson mass. Our method achieves orders of magnitude improvements in sampling efficiency compared to a brute force search.
      Cover page: Efficient sampling of constrained high-dimensional theoretical spaces with machine learning
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