Deep Investigation of Neutral Gas Origins (DINGO): Options for the Processing and Storage of Radio Astronomy Data for robust Deep Spectral Line Imaging in the SKA-Era using uv-Grids

Deep Investigation of Neutral Gas Origins (DINGO): Options for the Processing and Storage of Radio Astronomy Data for robust Deep Spectral Line Imaging in the SKA-Era using uv-Grids
Alexander Williamson, Richard Dodson, Pascal J. Elahi, Jonghwan Rhee, Qian Gong, Martin Meyer, Kristof Rozg’onyi, Andreas Wicenec, Jieyang Chen, Norbert Podhorszki, Scott Klasky, Daniel Mitchell
arXiv:2410.02285v4 Announce Type: replace
Abstract: The next generation of radio astronomy telescopes are challenging existing data analysis paradigms, as they have an order of magnitude more antennas and larger bandwidth. Foremost amongst these are deep spectral line surveys, because these have the largest number of epochs and spectral channels per dataset. For example, the Deep Investigation of Neutral Gas Origins (DINGO) project on the Australian Square Kilometre Array Pathfinder (ASKAP) aims to observe over 3,200 hours spread over hundreds of observing sessions, covering two tiles, two footprints and two frequency settings. The two primary problems encountered when processing this data are the need for storage and that processing is primarily I/O limited. To address these issues, we have implemented a deep imaging pipeline based on the storage of an intermediate data product in the software ASKAPsoft, that of the uv-gridded data, and have demonstrated lossy and lossless compression of this data on ASKAP, using MGARD and ADIOS2 libraries. We find data compression ratios from a factor of 7 (lossless) up to 20 (using lossy compression with an absolute error bound of $10^{-4}$), and processing is significantly faster for lossless compression. We discuss the effectiveness of lossy MGARD compression and its adherence to the designated error bounds, the trade-off between these error bounds and the corresponding compression ratios, as well as the potential consequences of these I/O and storage improvements on the science quality of the data products. As lossless compression allows us to achieve the DINGO goals within the storage limitations for the project, this will be the option adopted.arXiv:2410.02285v4 Announce Type: replace
Abstract: The next generation of radio astronomy telescopes are challenging existing data analysis paradigms, as they have an order of magnitude more antennas and larger bandwidth. Foremost amongst these are deep spectral line surveys, because these have the largest number of epochs and spectral channels per dataset. For example, the Deep Investigation of Neutral Gas Origins (DINGO) project on the Australian Square Kilometre Array Pathfinder (ASKAP) aims to observe over 3,200 hours spread over hundreds of observing sessions, covering two tiles, two footprints and two frequency settings. The two primary problems encountered when processing this data are the need for storage and that processing is primarily I/O limited. To address these issues, we have implemented a deep imaging pipeline based on the storage of an intermediate data product in the software ASKAPsoft, that of the uv-gridded data, and have demonstrated lossy and lossless compression of this data on ASKAP, using MGARD and ADIOS2 libraries. We find data compression ratios from a factor of 7 (lossless) up to 20 (using lossy compression with an absolute error bound of $10^{-4}$), and processing is significantly faster for lossless compression. We discuss the effectiveness of lossy MGARD compression and its adherence to the designated error bounds, the trade-off between these error bounds and the corresponding compression ratios, as well as the potential consequences of these I/O and storage improvements on the science quality of the data products. As lossless compression allows us to achieve the DINGO goals within the storage limitations for the project, this will be the option adopted.