3D Detection and Characterisation of ALMA Sources through Deep Learning

Abstract

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.

References in corpus (35)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
• Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies, and the Distant Universe
• The EAGLE simulations of galaxy formation: calibration of subgrid physics and model variations
• First Results from High Angular Resolution ALMA Observations Toward the HL Tau Region
• CASA, the Common Astronomy Software Applications for Radio Astronomy
• WSClean: an implementation of a fast, generic wide-field imager for radio astronomy
• Multi-Scale CLEAN deconvolution of radio synthesis images
• Wide Activation for Efficient and Accurate Image Super-Resolution
• Considerations for a Multi-beam Multi-purpose Survey with FAST
• SoFiA: a flexible source finder for 3D spectral line data
• SoFiA 2 -- An automated, parallel HI source finding pipeline for the WALLABY survey
• Machine Learning in Astronomy: a practical overview
• BLOBCAT: Software to Catalogue Flood-Filled Blobs in Radio Images of Total Intensity and Linear Polarization
• Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging
• Super-resolution Full Polarimetric Imaging for Radio Interferometry with Sparse Modeling
• Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range
• Identifying Galaxy Mergers in Observations and Simulations with Deep Learning
• Data-Driven Reconstruction of Gravitationally Lensed Galaxies using Recurrent Inference Machines
• Superresolution Interferometric Imaging with Sparse Modeling Using Total Squared Variation --- Application to Imaging the Black Hole Shadow
• Detecting outliers in astronomical images with deep generative networks
• Visual Attention Methods in Deep Learning: An In-Depth Survey
• DeepSource: Point Source Detection using Deep Learning
• Deep Generative Models for Galaxy Image Simulations
• Scalable End-to-end Recurrent Neural Network for Variable star classification
• Attention-gating for improved radio galaxy classification
• Deep Learning-based Imaging in Radio Interferometry
• Concat Convolutional Neural Network for Pulsar Candidate Selection
• Deep Learning-Based Super-Resolution and De-Noising for XMM-Newton Images
• The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch
• Classification of Blazar Candidates of Uncertain Type from the Fermi LAT 8-Year Source Catalog with an Artificial Neural Network
• Automatic detection of low surface brightness galaxies from SDSS images
• Foreword to the Focus Issue on Machine Learning in Astronomy and Astrophysics
• Finding quadruply imaged quasars with machine learning. I. Methods
• Black Hole Weather Forecasting with Deep Learning: A Pilot Study

Cited by in corpus (4)

• Deep learning-based radiointerferometric imaging with GAN-aided training
• Regularized Maximum Likelihood Image Synthesis and Validation for ALMA Continuum Observations of Protoplanetary Disks
• Challenging interferometric imaging: Machine learning-based source localization from uv-plane observations
• Unmasking the physical information inherent to interstellar spectral line profiles with Machine Learning. I. Application of LTE to HCN and HNC transitions