Speaker
Description
The future deployment of the Square Kilometer Array (SKA) will lead to a massive increase in astronomical data which means that automatic detection and characterization of sources will be crucial for utilizing its full potential. We suggest how existing astronomical knowledge and tools can be utilized in a machine learning-based pipeline for finding 3D spectral line sources. We present a source-finding pipeline, designed to detect 21-cm emission from galaxies, that provided the second-best submission of SKA Science Data Challenge 2. The first pipeline step was galaxy segmentation, which consisted of a convolutional neural network (CNN) that took an HI cube as input and output a binary mask to separate galaxy and background voxels. The CNN was trained to output a target mask algorithmically constructed from the underlying source catalog of the simulation. For each source in the catalog, its listed properties were used to mask the voxels in its neighborhood that capture plausible signal distributions of the galaxy. To make the training more efficient, regions containing galaxies were oversampled compared to the background regions. In the following source characterization step, the final source catalog was generated by the merging and dilation modules of the existing source finding software SoFiA, and some complementary calculations, with the CNN-generated mask as input. To cope with the large size of HI cubes but also allow deployment on various computational resources, the pipeline was implemented with flexible and configurable memory usage. We show that once the segmentation CNN is trained, the performance can be fine-tuned by adjusting the parameters involved in producing the catalog from the mask. Different sets of parameter values give a trade-off between completeness and reliability
