3D Detection and Characterisation of ALMA Sources through Deep Learning by Michele Delli Veneri et al. on Tuesday 22 November

We present a Deep-Learning (DL) pipeline developed for the detection and

characterization of astronomical sources within simulated Atacama Large

Millimeter/submillimeter Array (ALMA) data cubes. The pipeline is composed of

six DL models: a Convolutional Autoencoder for source detection within the

spatial domain of the integrated data cubes, a Recurrent Neural Network (RNN)

for denoising and peak detection within the frequency domain, and four Residual

Neural Networks (ResNets) for source characterization. The combination of

spatial and frequency information improves completeness while decreasing

spurious signal detection. To train and test the pipeline, we developed a

simulation algorithm able to generate realistic ALMA observations, i.e. both

sky model and dirty cubes. The algorithm simulates always a central source

surrounded by fainter ones scattered within the cube. Some sources were

spatially superimposed in order to test the pipeline deblending capabilities.

The detection performances of the pipeline were compared to those of other

methods and significant improvements in performances were achieved. Source

morphologies are detected with subpixel accuracies obtaining mean residual

errors of $10^{-3}$ pixel ($0.1$ mas) and $10^{-1}$ mJy/beam on positions and

flux estimations, respectively. Projection angles and flux densities are also

recovered within $10\%$ of the true values for $80\%$ and $73\%$ of all sources

in the test set, respectively. While our pipeline is fine-tuned for ALMA data,

the technique is applicable to other interferometric observatories, as SKA,

LOFAR, VLBI, and VLTI.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.11462v1