Active learning BSM parameter spaces

Abstract

Active learning (AL) has interesting features for parameter scans of new models. We show on a variety of models that AL scans bring large efficiency gains to the traditionally tedious work of finding boundaries for BSM models. In the MSSM, this approach produces more accurate bounds. In light of our prior publication, we further refine the exploration of the parameter space of the SMSQQ model, and update the maximum mass of a dark matter singlet to 48.4 TeV. Finally we show that this technique is especially useful in more complex models like the MDGSSM.

29 pages, 9 figures, 9 tables

References in corpus (22)

• HiggsSignals-2: Probing new physics with precision Higgs measurements in the LHC 13 TeV era
• Bound-state formation for thermal relic dark matter and unitarity
• Machine Learning in the String Landscape
• Higgs-mass predictions in the MSSM and beyond
• Prediction for the Lightest Higgs Boson Mass in the CMSSM using Indirect Experimental Constraints
• Constraining new physics with SModelS version 2
• Understanding Event-Generation Networks via Uncertainties
• Supersymmetric and non-supersymmetric models without catastrophic Goldstone bosons
• Publishing statistical models: Getting the most out of particle physics experiments
• Improved bounds on ${\mathbb{Z}}_3$ singlet dark matter
• Pseudo-Goldstone dark matter: gravitational waves and direct-detection blind spots
• Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks
• Simple and statistically sound recommendations for analysing physical theories
• The GAMBIT Universal Model Machine: from Lagrangians to Likelihoods
• Has the Origin of the Third-Family Fermion Masses been Determined?
• Constraining Electroweakinos in the Minimal Dirac Gaugino Model
• Bayesian Neural Networks for Fast SUSY Predictions
• Signal region combination with full and simplified likelihoods in MadAnalysis 5
• Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
• How heavy can dark matter be? Constraining colourful unitarity with SARAH
• Heavy dark matter through the dilaton portal
• Picking the low-hanging fruit: testing new physics at scale with active learning

Cited by in corpus (6)

• Combining Evolutionary Strategies and Novelty Detection to go Beyond the Alignment Limit of the $Z_3$ 3HDM
• Probing intractable beyond-standard-model parameter spaces armed with Machine Learning
• Bayesian Active Search on Parameter Space: a 95 GeV Spin-0 Resonance in the ($B-L$)SSM
• Exploring Scotogenic Parameter Spaces and Mapping Uncharted Dark Matter Phenomenology with Multi-Objective Search Algorithms
• Exploring the BSM parameter space with Neural Network aided Simulation-Based Inference
• Graph Reinforcement Learning for Exploring BSM Model Spaces