  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.
