Related papers: Artificial intelligence for celestial object censu…

We present the first high spatial resolution radio continuum survey of the southern Galactic plane. The CORNISH project has mapped the region defined by $295^{\circ} < l < 350^{\circ}$; $|b| < 1^{\circ}$ at 5.5-GHz, with a resolution of…

This research addresses the growing challenge of artificial satellite trail interference in ground-based astronomical observations by developing an efficient deep learning identification method. With the proliferation of satellite…

Advances in high resolution remote sensing image analysis are currently hampered by the difficulty of gathering enough annotated data for training deep learning methods, giving rise to a variety of small datasets and associated…

We calculate the expected number of gravitationally lensed optical, radio and sub-mm lensed sources on the whole sky due to foreground galaxy clusters for different cosmological models. We improve previous calculations of lensed arc…

We present a classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance on a manually labeled training set. While reasonable results can already be obtained…

Deep learning has become increasingly important in remote sensing image classification due to its ability to extract semantic information from complex data. Classification tasks often include predefined label hierarchies that represent the…

With the rapid development of deep learning, many deep learning-based approaches have made great achievements in object detection task. It is generally known that deep learning is a data-driven method. Data directly impact the performance…

A new sample of radio sources, with the designated name CENSORS (A Combined EIS-NVSS Survey Of Radio Sources), has been defined by combining the NRAO VLA Sky Survey (NVSS) at 1.4 GHz with the ESO Imaging Survey (EIS) Patch D, a 3 by 2…

High-redshift quasars are important to study galaxy and active galactic nuclei (AGN) evolution, test cosmological models, and study supermassive black hole growth. Optical searches for high-redshift sources have been very successful, but…

The continuum emission from radio galaxies can be generally classified into different morphological classes such as FRI, FRII, Bent, or Compact. In this paper, we explore the task of radio galaxy classification based on morphology using…

Semantic segmentation is a process of partitioning an image into multiple segments for recognizing humans and objects, which can be widely applied in scenarios such as healthcare and safety monitoring. To avoid privacy violation, using RF…

To understand the feedback of black holes on their environment or the acceleration of ultra-high energy cosmic rays in the present cosmic epoch, a systematic, all-sky inventory of radio galaxies in the local universe is needed. Here we…

Modern radio interferometers deliver large volumes of data containing high-sensitivity sky maps over wide fields-of-view. These large area observations can contain various and superposed structures such as point sources, extended objects,…

The FIRST survey, begun over twenty years ago, provides the definitive high-resolution map of the radio sky. This VLA survey reaches a 20cm detection sensitivity of 1 mJy over 10,575 deg**2 largely coincident with the SDSS area. Images and…

Using the JVLA, we explored the Galactic center (GC) with a resolution of 0.05" at 33.0 and 44.6 GHz. We detected 64 hyper-compact radio sources (HCRs) in the central parsec. The dense group of HCRs can be divided into three spectral types:…

Radio weak lensing, while a highly promising complementary probe to optical weak lensing, will require incredible precision in the measurement of galaxy shape parameters. In this paper, we extend the Bayesian Inference for Radio…

We present SHEEP, a new machine learning approach to the classic problem of astronomical source classification, which combines the outputs from the XGBoost, LightGBM, and CatBoost learning algorithms to create stronger classifiers. A novel…

We present radio, optical and X-ray detected counterparts to the sub-mm sources found using SCUBA in the Hubble Deep Field North region (GOODS-N). A new counterpart identification statistic is developed to identify properties of galaxies…

Vision Transformers are used via a customized TransUNet architecture, which is a hybrid model combining Transformers into a U-Net backbone, to achieve precise, automated, and fast segmentation of radio astronomy data affected by calibration…

The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the…