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Full-Text Articles in Physics
Measurement Of Charged-Pion Production In Deep-Inelastic Scattering Off Nuclei With The Clas Detector, Clas Collaboration, S. Morán, R. Dupre, H. Hakobyan, Moskov J. Amaryan, Dilini Bulumulla, Mohammad Hattawy, Florian Hauenstein, Sebastian Kuhn, Pushpa Pandey, Jiwan Poudel, Yelena Prok, Lawrence B. Weinstein, N. Zachariou, J. Zhang, Z. W. Zhao, Et Al.
Measurement Of Charged-Pion Production In Deep-Inelastic Scattering Off Nuclei With The Clas Detector, Clas Collaboration, S. Morán, R. Dupre, H. Hakobyan, Moskov J. Amaryan, Dilini Bulumulla, Mohammad Hattawy, Florian Hauenstein, Sebastian Kuhn, Pushpa Pandey, Jiwan Poudel, Yelena Prok, Lawrence B. Weinstein, N. Zachariou, J. Zhang, Z. W. Zhao, Et Al.
Physics Faculty Publications
Background: Energetic quarks in nuclear deep-inelastic scattering propagate through the nuclear medium. Processes that are believed to occur inside nuclei include quark energy loss through medium-stimulated gluon bremsstrahlung and intranuclear interactions of forming hadrons. More data are required to gain a more complete understanding of these effects.
Purpose: To test the theoretical models of parton transport and hadron formation, we compared their predictions for the nuclear and kinematic dependence of pion production in nuclei.
Methods: We have measured charged-pion production in semi-inclusive deep-inelastic scattering off D, C, Fe, and Pb using the CLAS detector and the CEBAF 5.014-GeV electron beam. …
Machine Learning-Based Event Generator For Electron-Proton Scattering, Y. Alanazi, P. Ambrozewicz, M. Battaglieri, A.N. Hiller Blin, M. P. Kuchera, Y. Li, T. Liu, R. E. Mcclellan, W. Melnitchouk, E. Pritchard, M. Robertson, N. Sato, R. Strauss, L. Velasco
Machine Learning-Based Event Generator For Electron-Proton Scattering, Y. Alanazi, P. Ambrozewicz, M. Battaglieri, A.N. Hiller Blin, M. P. Kuchera, Y. Li, T. Liu, R. E. Mcclellan, W. Melnitchouk, E. Pritchard, M. Robertson, N. Sato, R. Strauss, L. Velasco
Computer Science Faculty Publications
We present a new machine learning-based Monte Carlo event generator using generative adversarial networks (GANs) that can be trained with calibrated detector simulations to construct a vertex-level event generator free of theoretical assumptions about femtometer scale physics. Our framework includes a GAN-based detector folding as a fast-surrogate model that mimics detector simulators. The framework is tested and validated on simulated inclusive deep-inelastic scattering data along with existing parametrizations for detector simulation, with uncertainty quantification based on a statistical bootstrapping technique. Our results provide for the first time a realistic proof of concept to mitigate theory bias in inferring vertex-level event …