  In our work, we examine, for the first time, the possibility of fast and
efficient source localization directly from the uvobservations, omitting the
recovering of the dirty or clean images. We propose a deep neural network-based
framework that takes as its input a low-dimensional vector of sampled uvdata
and outputs source positions on the sky. We investigated a representation of
the complex-valued input uv-data via the real and imaginary and the magnitude
and phase components. We provided a comparison of the efficiency of the
proposed framework with the traditional source localization pipeline based on
the state-of-the-art Python Blob Detection and Source Finder (PyBDSF) method.
The investigation was performed on a data set of 9164 sky models simulated
using the Common Astronomy Software Applications (CASA) tool for the Atacama
Large Millimeter Array (ALMA) Cycle 5.3 antenna configuration. We investigated
two scenarios: (i) noise-free as an ideal case and (ii) sky simulations
including noise representative of typical extra-galactic millimeter
observations. In the noise-free case, the proposed localization framework
demonstrates the same high performance as the state-of-the-art PyBDSF method.
For noisy data, however, our new method demonstrates significantly better
performance, achieving a completeness level that is three times higher for
sources with uniform signal-to-noise (S/N) ratios between 1 and 10, and a high
increase in completeness in the low S/N regime. Furthermore, the execution time
of the proposed framework is significantly reduced (by factors about 30) as
compared to traditional methods that include image reconstructions from the
uv-plane and subsequent source detections.
