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Variable rate neural compression for sparse detector data.

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  • 1Computing and Data Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA.

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View abstract on PubMed

Summary
This summary is machine-generated.

A new deep learning model, BCAE-VS, efficiently compresses sparse 3D data from particle colliders. This method enhances data transmission and storage for scientific experiments and other fields like LiDAR.

Keywords:
autoencoderdata compressiondeep learninghigh-energy and nuclear physicshigh-throughput inferencesparse datasparse neural network

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Area of Science:

  • High Energy Physics
  • Data Science
  • Machine Learning

Background:

  • Particle colliders generate massive datasets, straining transmission and storage infrastructure.
  • Existing compression methods, including traditional and deep learning approaches, struggle with highly sparse 3D trajectory data from experiments like sPHENIX.

Purpose of the Study:

  • To develop a novel compression algorithm that effectively handles sparse 3D data from particle physics experiments.
  • To improve compression ratio and accuracy while maintaining high throughput for sparse datasets.

Main Methods:

  • Introduction of BCAE-VS (Bicephalous Convolutional Autoencoder with Variable compression ratio for Sparse data), a deep learning model.
  • Utilizes key-point identification and sparse convolution to adapt compression to input complexity.
  • Leverages a convolutional neural network architecture designed for sparse data exploitation.

    • Main Results:

    • BCAE-VS achieves superior compression ratios and accuracy compared to existing neural network methods.
    • The model is significantly smaller than previous approaches.
    • Throughput increases with data sparsity, a unique advantage over other methods.

    Conclusions:

    • BCAE-VS offers an efficient solution for compressing sparse 3D data, particularly relevant for particle collider experiments.
    • The algorithm demonstrates broad applicability to other domains with sparse data, including LiDAR and 3D microscopy.