用生成式Transformer模拟高精度量能器簇射点云，无需固定网格
OmniJet-$α_C$: Learning point cloud calorimeter simulations using generative transformers
- 基于OmniJet-α模型将探测器击中点转换为整数序列，支持可变长度输入
- 首次实现生成式Transformer直接输出点云形式的簇射数据，不依赖预设体素网格
- 适合粒子物理仿真、新探测器设计及生成对抗网络替代方案研究者
我们首次将生成式Transformer用于高精度量能器中簇射点云的生成。利用OmniJet-α模型的分词器和生成部分，将探测器中的击中点表示为整数序列。该模型支持可变长度序列，因此能真实反映簇射演化过程，无需对击中数量进行条件约束。由于分词结果以点云形式呈现，模型在学习簇射几何结构时不受特定体素网格限制。
原文摘要 · Abstract (English)
We show the first use of generative transformers for generating calorimeter showers as point clouds in a high-granularity calorimeter. Using the tokenizer and generative part of the OmniJet-$α$ model, we represent the hits in the detector as sequences of integers. This model allows variable-length sequences, which means that it supports realistic shower development and does not need to be conditioned on the number of hits. Since the tokenization represents the showers as point clouds, the model learns the geometry of the showers without being restricted to any particular voxel grid.
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