Modeling inclusive electron-nucleus scattering with Bayesian artificial neural networks
Year: 2024
Type: article
Abstract: We introduce a Bayesian protocol based on artificial neural networks that is suitable for modeling inclusive electron-nucleus scattering on a variety of nuclear targets with quantified uncertainties. Unlike previous applications in the field, which directly parameterize the cross sections, our approach employs artificial neural networks to represent the longitudinal and transverse response functions. In contrast to cross sections, which depend on the incoming energy, scattering angle, and energy transfer, the response functions are determined solely by the energy and momentum transfer to the system, allowing the angular component to be treated analytically. We assess the accuracy and predictive power of our framework against the extensive data in the quasielastic inclusive electron-scattering database. Additionally, we present novel extractions of the longitudinal and transverse response functions and compare them with previous experimental analysis and nuclear ab-initio calculations.
Source: Physics Letters B
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
Awards: G102163711, G107546177, G1313983767, G1664757510, G1760870335, G2121348967, G2287611682, G262962599, G2762820993, G2896472831, G3075337988, G3479632140, G3523651922, G434337789, G4404904460, G4795914640, G4845049930, G498139845, G5607711389, G5608225264, G585908603, G6160761920, G6237201456, G6256534168, G6848031779, G6918803902, G6992476341, G7133536890, NU 4 ν: nuclear ab initio methods for neutrino physics, G716412294, G8581667079, G86560745, G969889393, G993149817
OpenAlex ID: W4399554263