hep-aid: A Python Library for Sample Efficient Parameter Scans in Beyond the Standard Model Phenomenology

This paper presents hep-aid, a modular Python library conceived for utilising, implementing, and developing parameter scan algorithms. Originally devised for sample-efficient, multi-objective active search approaches in computationally expensive Beyond Standard Model (BSM) phenomenology, the library currently integrates three Machine Learning (ML)-based approaches: a Constraint Active Search (CAS) algorithm, a multi-objective Active Search (AS) method (called b-CASTOR), and a self-exploration method named Machine Learning Scan (MLScan). These approaches address the challenge of multi-objective optimisation in high-dimensional BSM scenarios by employing surrogate models and strategically exploring parameter spaces to identify regions that satisfy complex objectives with fewer evaluations. Additionally, a Markov-Chain Monte Carlo method using the Metropolis-Hastings algorithm (MCMC-MH) is implemented for method comparison. The library also includes a High Energy Physics (HEP) module based on SPheno as the spectrum calculator. However, the library modules and functionalities are designed to be easily extended and used also with other external software for phenomenology. This manual provides an introduction on how to use the main functionalities of hep-aid and describes the design and structure of the library. Demonstrations based on the aforementioned parameter scan methods show that hep-aid methodologies enhance the efficiency of BSM studies, offering a versatile toolset for complex, multi-objective searches for new physics in HEP contexts exploiting advanced ML-based approaches.