TensorFit a tool to analyse spectral cubes in a tensor mode
As it is already known, modern observatories like the Atacama Large Millimeter/submillimeter Array (ALMA) and the Very Long Baseline Array (VLBA) generate large-scale data, which will be accentuated with the incorporation of new observatories, such as the Square Kilometre Array (SKA). It is projected by 2020 to obtain an archived astronomical data in a PB-scale (60 Petabyte). The Chilean Virtual Observatory (ChiVO) has stored the spectral cubes of ALMA and seeks to offer these data openly to the community, but downloading and processing these data should be done in its facilities. To this end, our proposal considers the cubes as a high order tensor, specifically 3-way tensor with 2 spatial dimensions (galactic latitude and longitude), and a velocity dimension. This opens a new approach and opportunity for computational prohibitive massive analysis of these cubes. Based on this premise, we propose TensorFit, a natural and scalable library to handle spectral cubes in a tensor mode. The implementation is built on parallel oriented frameworks, and distributed processing of n-arrays on PyTorch (GPU and CPU). To verify the impact of this proposal, our focus is on showing the benefits of tensor compression, in particular to Tucker implementations. These have demonstrated outstanding results of dimensionality reduction of multidimensional data in other scientific domains.