WeChat Mini Program
Old Version Features

    • Assessment of Photon and Proton-Induced Activation in Particles Accelerators

    Abdel-Mjid Nourreddine, Jonathan Collin,Nicolas Arbor, Francois Begin,Massimo Barbagallo,Federico Carminati,Giuliana Galli Carminati, Jean-Michel Horodynski

    RADIATION PROTECTION DOSIMETRY（2024）

    Univ Strasbourg

    Cited 0|Views1
    Abstract
    Nuclear activation affects all operating, future, or dismantled particle accelerators used in various fields, from medical applications to industrial applications. This work is concerned with the study of the radioactivity induced in various materials (Sc, Cu, Tb, Ta, W, Au) irradiated by hard Bremsstrahlung photons from an electron beam and by secondary neutrons induced by a proton beam. In both cases, the primary beam features an 18 MeV kinetic energy. We have performed Monte Carlo simulations with MCNP, GEANT4, FLUKA, and PHITS, coupled with the analytical codes CINDER'90 or FISPACT-II, to estimate the threshold reaction rates and the induced radioactivity in the samples. We compared the simulations with experimental activation measurements performed by high-resolution gamma spectrometry at a decommissioned medical LINAC and around the CYRC & eacute; cyclotron. We also used CR-39 solid-state nuclear track detectors to characterize the thermal and fast neutron components of the secondary fields.
    More
    Translated text
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    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
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    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
    Summary is being generated by the instructions you defined