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Setting Limits on Supersymmetry Using Simplified Models
Published on: November 15, 2013
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Symbolic regression and differentiable fits in beyond the standard model physics
Shehu AbdusSalam1, Steven Abel2,3, Deaglan Bartlett4
1Department of Physics, Shahid Beheshti University, Tehran, Iran.
Summary
Symbolic regression (SR) accelerates analysis of Beyond the Standard Model (BSM) physics, like the Constrained Minimal Supersymmetric Standard Model (CMSSM). SR provides accurate expressions for fitting parameters, offering a robust alternative to neural networks.
Area of Science:
- Particle Physics
- Cosmology
- Computational Physics
Background:
- Beyond the Standard Model (BSM) physics theories, such as the Constrained Minimal Supersymmetric Standard Model (CMSSM), introduce new parameters influencing observable phenomena.
- Analyzing the complex phenomenology of BSM models requires efficient computational methods to explore parameter spaces and compare with experimental data.
Purpose of the Study:
- To demonstrate the efficacy of symbolic regression (SR) in accelerating the analysis of BSM physics models.
- To derive accurate symbolic expressions for key observables within the CMSSM.
- To compare the performance of SR with conventional fitting methods and neural network (NN) regression.
Main Methods:
- Symbolic regression (SR) was employed to generate analytical expressions for observables in the CMSSM.
- Global fits were performed using these SR-derived expressions to determine parameter probability densities.
- The SR approach was compared against traditional sampling methods and neural network (NN) regression.
Main Results:
- SR successfully generated remarkably accurate symbolic expressions for Higgs mass, dark matter relic density, and muon anomalous magnetic moment.
- Global fits using SR-derived expressions yielded posterior probability densities consistent with conventional methods.
- SR enabled differentiable fitting methods, offering an advantage over sampling-based techniques.
Conclusions:
- Symbolic regression is a powerful and efficient tool for probing BSM physics models, significantly accelerating phenomenological analysis.
- SR provides more globally robust results compared to neural networks, which require focused training data.
- The SR method facilitates differentiable fitting, enhancing the analysis of complex physical models.
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