xBIT: an easy to use scanning tool with machine learning abilities

TL;DR

xBIT is an open-source Python tool that simplifies parameter scans in beyond Standard Model theories, integrating machine learning for efficient exploration and supporting various physics analysis tools.

Contribution

It introduces an easy-to-use framework combining parameter scanning with machine learning capabilities for phenomenological research.

Findings

Supports multiple physics analysis tools like MicrOmegas and HiggsBounds.

Incorporates machine learning to enhance scan efficiency.

Open source and adaptable to different data transfer formats.

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.