EasyScan_HEP 2 adds AI-agent interfaces to a HEP parameter scan framework for natural-language to .ini config translation and new sampler integration.
Staub, xBIT: an easy to use scanning tool with machine learning abilities (6 2019).arXiv: 1906.03277
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abstract
xBIT is a tool for performing parameter scans in beyond the Standard Model theories. It's written in Python and fully open source. The main purpose of xBIT is to provide an easy to use tool to help phenomenologists with their daily task: exploring the parameter space of new models. It was developed under the impression of the SARAH/SPheno framework, but should be use-able with other tools as well that use the SLHA format to transfer data. It also supports by default MicrOmegas for dark matter calculations, HiggsBounds and HiggsSignals for checking the Higgs properties, and Vevacious for testing the vacuum stability. Classes for other tools can be added if necessary. In order to improve the efficiency of the parameter scans, the recently proposed 'Machine Learning Scan' approach is included. For this purpose, xBIT uses pyTorch to deal with artificial neural networks.
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BSMArt version 2 adds new scanning algorithms including Affine MC, MLScanner, and CMA-ES variants to simplify and accelerate parameter space exploration in new physics models, demonstrated on soft lepton excess searches at the LHC.
Jarvis-HEP introduces a YAML-based Python framework for composing workflows and performing parameter scans in high-energy physics.
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Jarvis-HEP: A lightweight Python framework for workflow composition and parameter scans in high-energy physics
Jarvis-HEP introduces a YAML-based Python framework for composing workflows and performing parameter scans in high-energy physics.