标题： 面向结合离散和连续数据的实验读出系统的基础模型 显示英文标题

标题： Towards Foundation Models for Experimental Readout Systems Combining Discrete and Continuous Data

摘要： 我们提出了一种(原型)核物理基础模型，能够处理未来电子离子对撞机上成像切连科夫探测器的低级探测器输入。 在已有的下一个标记预测方法的基础上，我们旨在解决现有分词方案可能导致的分辨率损失和条件生成支持有限等潜在挑战。 我们提出了四项关键创新：(i) 通过因果多头交叉注意力（CMHCA）结合离散和连续变量的独立词表， (ii) 通过预先添加的上下文嵌入实现连续运动学条件， (iii) 在不增加联合词表的情况下，实现可扩展且简单的高分辨率连续变量分词，以及 (iv) 通过专家混合实现类别条件生成。 我们的模型实现了切连科夫光子像素和时间序列的快速、高保真生成，通过高性能DIRC中的闭合测试进行了验证。 我们还展示了我们的模型可以推广到重建任务，如介子/K介子识别和噪声过滤，在这些任务中我们展示了其在特定目标下进行微调的能力。 显示英文摘要

摘要： We present a (proto) Foundation Model for Nuclear Physics, capable of operating on low-level detector inputs from Imaging Cherenkov Detectors at the future Electron Ion Collider. Building upon established next-token prediction approaches, we aim to address potential challenges such as resolution loss from existing tokenization schemes and limited support for conditional generation. We propose four key innovations: (i) separate vocabularies for discrete and continuous variates, combined via Causal Multi-Head Cross-Attention (CMHCA), (ii) continuous kinematic conditioning through prepended context embeddings, (iii) scalable and simple, high-resolution continuous variate tokenization without joint vocabulary inflation, and (iv) class conditional generation through a Mixture of Experts. Our model enables fast, high-fidelity generation of pixel and time sequences for Cherenkov photons, validated through closure tests in the High Performance DIRC. We also show our model generalizes to reconstruction tasks such as pion/kaon identification, and noise filtering, in which we show its ability to leverage fine-tuning under specific objectives.