Analysis of Reactor Burnup Simulation Uncertainties for Antineutrino Spectrum Prediction
EUROPEAN PHYSICAL JOURNAL PLUS(2024)
INFN Sezione di Milano Bicocca e Dipartimento di Fisica | INFN Sezione di Milano Bicocca e Dipartimento di Energia | INFN Sezione di Catania | INFN Dipartimento di Fisica | INFN Sezione di Padova | INFN Dipartimento di Fisica e Matematica | INFN Sezione di Perugia e Università degli Studi di Perugia | Laboratori Nazionali dell'INFN di Frascati | Università degli Studi di Ferrara Dipartimento di Fisica e Scienze della Terra
Abstract
Nuclear reactors are a source of electron antineutrinos due to the presence of unstable fission products that undergo β ^- decay. They will be exploited by the JUNO experiment to determine the neutrino mass ordering and to get very precise measurements of the neutrino oscillation parameters. This requires the reactor antineutrino spectrum to be characterized as precisely as possible both through high-resolution measurements, as foreseen by the TAO experiment, and detailed simulation models. In this paper, we present a benchmark analysis utilizing Serpent Monte Carlo simulations in comparison with real pressurized water reactor spent fuel data. Our objective is to study the accuracy of fission fraction predictions as a function of different reactor simulation approximations. Then, using the BetaShape software, we construct reactor antineutrino spectrum using the summation method, thereby assessing the influence of simulation uncertainties on it.
MoreTranslated text
Key words
Neutrino Detection,Neutrino Oscillations,Neutrino Interactions,Supernova Simulations
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2012
被引用129 | 浏览
2013
被引用60 | 浏览
2017
被引用226 | 浏览
2016
被引用209 | 浏览
2019
被引用99 | 浏览
2020
被引用51 | 浏览
2011
被引用3687 | 浏览
2021
被引用5 | 浏览
2023
被引用6 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话