AI-driven symbolic evolution discovers interpretable event-level observables that retain substantially more local Fisher information than angular baselines for CP-sensitive HZ interference in two collider channels.
Exploring supersymmetry with machine learning
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abstract
Investigation of well-motivated parameter space in the theories of Beyond the Standard Model (BSM) plays an important role in new physics discoveries. However, a large-scale exploration of models with multi-parameter or equivalent solutions with a finite separation, such as supersymmetric models, is typically a time-consuming and challenging task. In this paper, we propose a self-exploration method, named Machine Learning Scan (MLS), to achieve an efficient test of models. As a proof-of-concept, we apply MLS to investigate the subspace of MSSM and CMSSM and find that such a method can reduce the computational cost and may be helpful for accelerating the exploration of supersymmetry.
fields
hep-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
EasyScan_HEP 2 adds AI-agent interfaces to a HEP parameter scan framework for natural-language to .ini config translation and new sampler integration.
citing papers explorer
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AI-Driven Discovery of Information-Efficient Collider Observables for Interference Measurements
AI-driven symbolic evolution discovers interpretable event-level observables that retain substantially more local Fisher information than angular baselines for CP-sensitive HZ interference in two collider channels.
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EasyScan_HEP 2: Agent-Ready Parameter Scans for High-Energy Physics
EasyScan_HEP 2 adds AI-agent interfaces to a HEP parameter scan framework for natural-language to .ini config translation and new sampler integration.