Bayesian active search on parameter space: a 95 GeV spin-0 resonance in the (B−L)SSM
Bayesian active search on parameter space: a 95 GeV spin-0 resonance in the (B−L)SSM
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.
hep-ph
Diaz, Mauricio A.
b929a911-11c3-43c8-bd8a-eb2173a4b14e
Cerro, Giorgio
c4363eb0-a9d8-4456-b749-6d4380898b25
Dasmahapatra, Srinandan
eb5fd76f-4335-4ae9-a88a-20b9e2b3f698
Moretti, Stefano
b57cf0f0-4bc3-4e02-96e3-071255366614
Diaz, Mauricio A.
b929a911-11c3-43c8-bd8a-eb2173a4b14e
Cerro, Giorgio
c4363eb0-a9d8-4456-b749-6d4380898b25
Dasmahapatra, Srinandan
eb5fd76f-4335-4ae9-a88a-20b9e2b3f698
Moretti, Stefano
b57cf0f0-4bc3-4e02-96e3-071255366614
[Unknown type: UNSPECIFIED]
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.
Text
2404.18653v1
- Author's Original
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Accepted/In Press date: 29 April 2024
Keywords:
hep-ph
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Local EPrints ID: 491632
URI: http://eprints.soton.ac.uk/id/eprint/491632
PURE UUID: 1a22fde1-1f4c-4d81-be0d-3ef189645875
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Date deposited: 03 Jul 2024 09:10
Last modified: 12 Jul 2024 01:41
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Author:
Mauricio A. Diaz
Author:
Srinandan Dasmahapatra
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