用Transformer自编码器压缩中微子探测数据，提升分析效率
Learning Efficient Representations of Neutrino Telescope Events
- 用Transformer变分自编码器学习光子到达时间的紧凑表示
- 生成的隐变量表示能显著降低计算开销，提升下游任务效率
- 适合处理大规模高维中微子探测数据的科研人员参考
中微子望远镜探测宇宙极端环境中产生的粒子相互作用。通过在立方千米尺度的天然透明介质中部署光传感器实现探测。由于设备规模庞大且背景事件频繁，这些望远镜产生海量、高方差、高维度的数据。这一特性给数据分析与相互作用重建带来巨大挑战，尤其在使用机器学习技术时更为明显。本文提出一种新方法om2vec，采用基于Transformer的变分自编码器，高效表示中微子望远镜事件中探测到的光子到达时间分布，学习紧凑且描述性强的隐空间表示。实验表明，这些隐表示具有更强灵活性和更高计算效率，有效促进后续数据分析任务。
原文摘要 · Abstract (English)
Neutrino telescopes detect rare interactions of particles produced in some of the most extreme environments in the Universe. This is accomplished by instrumenting a cubic-kilometer scale volume of naturally occurring transparent medium with light sensors. Given their substantial size and the high frequency of background interactions, these telescopes amass an enormous quantity of large variance, high-dimensional data. These attributes create substantial challenges for analyzing and reconstructing interactions, particularly when utilizing machine learning (ML) techniques. In this paper, we present a novel approach, called om2vec, that employs transformer-based variational autoencoders to efficiently represent the detected photon arrival time distributions of neutrino telescope events by learning compact and descriptive latent representations. We demonstrate that these latent representations offer enhanced flexibility and improved computational efficiency, thereby facilitating downstream tasks in data analysis.
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