Bayesian Active Search on Parameter Space: a 95 GeV Spin-0 Resonance in the ($B-L$)SSM

TL;DR

This paper introduces a novel machine learning active search method, b-CASTOR, to efficiently explore the parameter space of the $(B-L)$SSM in search of a 95 GeV spin-0 resonance, outperforming traditional MCMC techniques.

Contribution

The paper presents a new multi-objective active search algorithm, b-CASTOR, that improves sample efficiency and diversity in parameter space exploration for new physics models.

Findings

b-CASTOR outperforms MCMC-based algorithms in efficiency and diversity.

Successfully identifies parameter regions consistent with a 95 GeV resonance.

Demonstrates effectiveness in a complex supersymmetric model context.

Abstract

In the attempt to explain possible data anomalies from collider experiments in terms of New Physics (NP) models, computationally expensive scans over their parameter spaces are typically required in order to match theoretical predictions to experimental observations. Under the assumption that anomalies seen at a mass of about 95 GeV by the Large Electron-Positron (LEP) and Large Hadron Collider (LHC) experiments correspond to a NP signal, which we attempt to interpret as a spin-0 resonance in the $(B-L)$ Supersymmetric Standard Model ($(B-L)$SSM), chosen as an illustrative example, we introduce a novel Machine Learning (ML) approach based on a multi-objective active search method, called b-CASTOR, able to achieve high sample efficiency and diversity, due to the use of probabilistic surrogate models and a volume based search policy, outperforming competing algorithms, such as those based on Markov-Chain Monte Carlo (MCMC) methods.