Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation
<|reference_start|>Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation: We introduce a novel machine learning method developed for the fast simulation of calorimeter detector response, adapting vector-quantized variational autoencoder (VQ-VAE). Our model adopts a two-stage generation strategy: initially compressing geometry-aware calorimeter data into a discrete latent space, followed by the application of a sequence model to learn and generate the latent tokens. Extensive experimentation on the Calo-challenge dataset underscores the efficiency of our approach, showcasing a remarkable improvement in the generation speed compared with conventional method by a factor of 2000. Remarkably, our model achieves the generation of calorimeter showers within milliseconds. Furthermore, comprehensive quantitative evaluations across various metrics are performed to validate physics performance of generation.<|reference_end|>
@article{liu2024calo-vq:,
    title={Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter
  Simulation},
    author={Qibin Liu, Chase Shimmin, Xiulong Liu, Eli Shlizerman, Shu Li and
  Shih-Chieh Hsu},
    journal={arXiv preprint arXiv:2405.06605},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06605},
    primaryClass={physics.ins-det cs.LG hep-ph}
}