Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation

• URL: http://arxiv.org/abs/2405.06605v3
• Date: Tue, 6 Aug 2024 15:20:00 GMT
• Title: Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation
• Authors: Qibin Liu, Chase Shimmin, Xiulong Liu, Eli Shlizerman, Shu Li, Shih-Chieh Hsu,
• Abstract summary: 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: 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. Remarkably, our model achieves the generation of calorimeter showers within milliseconds.
• Score: 14.42579802774594
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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