An Open-Source Autoencoder Compression Tool for High Energy Physics - Gallén,Axel (2023)
A common problem across scientific fields and industries is data storage. This thesis presents an open-source lossy data compression tool with its foundation in Machine Learning - Baler. Baler has been used to compress High Energy Physics (HEP) data, and initial compression tests on Computational Fluid Dynamics (CFD) toy data have been performed. For HEP, a compression ratio of R = 1.6 has generated reconstructions that can be deemed sufficiently accurate for physics analysis. In contrast, CFD data compression has successfully yielded sufficient results for a significantly lower compression ratio, R = 88. Baler’s reconstruction accuracy at different compression ratios has been compared to a lossless compression method, gzip, and a lossy compression method, Principal Component Analysis (PCA), with case-wise larger compression ratios over gzip; and accuracy at the same compression ratio overall exceeding that of PCA.
Keywords: Physics; Particle Physics; Analysis; Machine Learning; Neural Networks; Autoencoders; Data Compression; Lossy Compression; Baler