Selective Laser Processing of Particle Accelerator Beam Screen Surfaces for Electron Cloud Mitigation

Elena Bez, Ana Karen Reascos Portilla,Valentine Petit, Konstantinos Paraschou, Lotta Mether, Kristóf Brunner,Patrick Krkotić, Yasemin Askar,Sergio Calatroni,Mauro Taborelli,Marcel Himmerlich

RSC Applied Interfaces（2025）

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Abstract

Laser-induced surface roughening is a technique that facilitates the reduction of secondary electron emission (SEE) from materials, which is crucial for mitigating electron cloud (EC) formation in particle accelerators, that...

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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

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【要点】：论文提出了一种选择性激光加工方法，通过表面粗糙化降低粒子加速器中的次级电子发射，从而减轻电子云的形成，具有创新性。

【方法】：采用激光诱导表面粗糙化技术，精确控制加工参数以减少材料的次级电子发射。

【实验】：通过实验验证了该方法的有效性，使用的数据集名称未提及，但实验结果表明表面粗糙化显著降低了次级电子发射率。