Modeling inclusive electron-nucleus scattering with Bayesian artificial neural networks
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.
- Research Organization:
- Argonne National Laboratory (ANL), Argonne, IL (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Mainz U., Inst. Kernphys.; TIFPA-INFN, Trento; U. Mainz, PRISMA
- Sponsoring Organization:
- US Department of Energy
- Grant/Contract Number:
- 89243024CSC000002; AC02-07CH11359
- OSTI ID:
- 2376216
- Report Number(s):
- FERMILAB-PUB-24-0299-T; oai:inspirehep.net:2796550; arXiv:2406.06292
- Journal Information:
- Phys.Lett.B, Journal Name: Phys.Lett.B Vol. 859
- Country of Publication:
- United States
- Language:
- English
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