Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder

Conference · 13 December 2021

DOI:https://doi.org/10.1109/ICMLA52953.2021.00179· OSTI ID:1842806

Huang, Yi; Ren, Yihui; Yoo, Shinjae; Huang, Jin.

Real-time data collection and analysis in large experimental facilities present a great challenge across multiple domains, including high energy physics, nuclear physics, and cosmology. To address this, machine learning (ML)-based methods for real-time data compression have drawn significant attention. However, unlike natural image data, such as CIFAR and ImageNet that are relatively small-sized and continuous, scientific data often come in as three-dimensional 3D data volumes at high rates with high sparsity (many zeros) and non-Gaussian value distribution. This makes direct application of popular ML compression methods, as well as conventional data compression methods, suboptimal. To address these obstacles, this work introduces a dual-head autoencoder to resolve sparsity and regression simultaneously, called Bicephalous Convolutional AutoEncoder (BCAE). This method shows advantages both in compression fidelity and ratio compared to traditional data compression methods, such as MGARD, SZ, and ZFP. To achieve similar fidelity, the best performer among the traditional methods can reach only half the compression ratio of BCAE. Moreover, a thorough ablation study of the BCAE method shows that a dedicated segmentation decoder improves the reconstruction.

• Research Organization: Brookhaven National Laboratory (BNL), Upton, NY (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Nuclear Physics (NP) (SC-26)
• DOE Contract Number: SC0012704
• OSTI ID: 1842806
• Report Number(s): BNL-222679-2022-COPA
• Country of Publication: United States
• Language: English

Related Subjects

73 NUCLEAR PHYSICS AND RADIATION PHYSICS
Autoencoder
Data Compression
Deep Learning
High-energy and Nuclear Physics
Sparse Data