Speaker
Description
In High Energy Physics, when testing theoretical models of new physics against
experimental results, the customary approach is to simply sample random points from
the parameter space of the model, calculate their predicted values for the desired
observables and compare them to experimental data. However, due to the typically
large number of parameters in these models, this process is highly time consuming
and inefficient. We propose a solution to this by adopting optimization algorithms
which make use of Machine Learning methods in order to improve the efficiency of
this validation task.
A first study applied these methods to conventional Supersymmetry realisations,
cMSSM and pMSSM, when confronted against Higgs mass and Dark Matter relic
density constraints and the results show an increase in up to 3 orders of magnitude in
sampling efficiency when compared to random sampling.
In a much more challenging scenario, a followup analysis was implemented for
the scotogenic model, using an evolutionary multiobjective optimization algorithm,
confronted against experimental constraints coming from the Higgs and neutrinos
masses, lepton flavor violating decays, neutrino mixing and the anomalous magnetic
moment of the muon. Preliminary results show at least 6 orders of magnitude increase
in efficiency over random sampling.
| Which topic best fits your talk? | High Energy Physics and Cosmology |
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