Zhenghao Fu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Zhenghao Fu has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Aobo Li
University of California San Diego
2 shared publications C. Grant
Department of Physics Boston University 590 Commonwealth Ave 02215 Boston MA USA
1 shared publicationChristopher Grant
Department of Physics, Boston University, Boston, USA
1 shared publicationD. M. Krawiec
Department of Physics University of Warwick CV4 7AL Coventry United Kingdom
1 shared publicationDominika M. Krawiec
Department of Physics, University of Warwick, Coventry, UK
1 shared publicationL. Winslow
Laboratory of Nuclear Science Massachusetts Institute of Technology 77 Massachusetts Ave 02139 Cambridge MA USA
1 shared publication
Latest Publications
- Open Access
- 27 June 2024
- Published by Springer Nature in The European Physical Journal C
- Vol. 84 (6) , 1-10
Generative models for simulation of KamLAND-Zen
The next generation of searches for neutrinoless double beta decay ( $$0 \nu \beta \beta $$ ) are poised to answer deep questions on the nature of neutrinos and the source of the Universe’s matter–antimatter asymmetry. They will be looking for event rates of less than one event per ton of instrumented isotope per year. To claim discovery, accurate and efficient simulations of detector events that mimic $$0 \nu \beta \beta $$ is critical. Traditional Monte Carlo (MC) simulations can be supplemented by machine-learning-based generative models. This work describes the performance of generative models that we designed for monolithic liquid scintillator detectors like KamLAND to produce accurate simulation data without a predefined physics model. We present their current ability to recover low-level features and perform interpolation. In the future, the results of these generative models can be used to improve event classification and background rejection by providing high-quality abundant generated data.- 27 June 2024
- Published by Springer Nature in The European Physical Journal C
- Vol. 84 (6) , 1-10
Open AccessGenerative models for simulation of KamLAND-Zen
The next generation of searches for neutrinoless double beta decay ( $$0 \nu \beta \beta $$ ) are poised to answer deep questions on the nature of neutrinos and the source of the Universe’s matter–antimatter asymmetry. They will be looking for event rates of less than one event per ton of instrumented isotope per year. To claim discovery, accurate and efficient simulations of detector events that mimic $$0 \nu \beta \beta $$ is critical. Traditional Monte Carlo (MC) simulations can be supplemented by machine-learning-based generative models. This work describes the performance of generative models that we designed for monolithic liquid scintillator detectors like KamLAND to produce accurate simulation data without a predefined physics model. We present their current ability to recover low-level features and perform interpolation. In the future, the results of these generative models can be used to improve event classification and background rejection by providing high-quality abundant generated data. - Open Access
Generative Models for Simulation of KamLAND-Zen
The next generation of searches for neutrinoless double beta decay (0ν\b{eta}\b{eta}) are poised to answer deep questions on the nature of neutrinos and the source of the Universe's matter-antimatter asymmetry. They will be looking for event rates of less than one event per ton of instrumented isotope per year. To claim discovery, accurate and efficient simulations of detector events that mimic 0ν\b{eta}\b{eta} is critical. Traditional Monte Carlo (MC) simulations can be supplemented by machine-learning-based generative models. In this work, we describe the performance of generative models designed for monolithic liquid scintillator detectors like KamLAND to produce highly accurate simulation data without a predefined physics model. We demonstrate its ability to recover low-level features and perform interpolation. In the future, the results of these generative models can be used to improve event classification and background rejection by providing high-quality abundant generated data.
Open AccessGenerative Models for Simulation of KamLAND-Zen
The next generation of searches for neutrinoless double beta decay (0ν\b{eta}\b{eta}) are poised to answer deep questions on the nature of neutrinos and the source of the Universe's matter-antimatter asymmetry. They will be looking for event rates of less than one event per ton of instrumented isotope per year. To claim discovery, accurate and efficient simulations of detector events that mimic 0ν\b{eta}\b{eta} is critical. Traditional Monte Carlo (MC) simulations can be supplemented by machine-learning-based generative models. In this work, we describe the performance of generative models designed for monolithic liquid scintillator detectors like KamLAND to produce highly accurate simulation data without a predefined physics model. We demonstrate its ability to recover low-level features and perform interpolation. In the future, the results of these generative models can be used to improve event classification and background rejection by providing high-quality abundant generated data.