Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder
This addresses real-time data compression for experimental facilities in high energy physics and related domains, representing a domain-specific incremental improvement.
The paper tackled the challenge of compressing high-rate, sparse 3D scientific data by introducing a Bicephalous Convolutional Autoencoder, which achieved double the compression ratio of traditional methods like MGARD, SZ, and ZFP at similar fidelity.
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 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 \textit{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.