相关论文: Radio Galaxy Zoo: ClaRAN - A Deep Learning Classif…

The forthcoming generation of radio telescope arrays promises significant advancements in sensitivity and resolution, enabling the identification and characterization of many new faint and diffuse radio sources. Conventional manual…

Radio galaxies can extend far beyond the stellar component of their originating host galaxies, and their radio emission can consist of multiple discrete components. Furthermore, the apparent source structure will depend on survey…

Radio loud active galactic nuclei (RLAGNs) are often morphologically complex objects that can consist of multiple, spatially separated, components. Astronomers often rely on visual inspection to resolve radio component association. However,…

Morphological classification is a key piece of information to define samples of galaxies aiming to study the large-scale structure of the universe. In essence, the challenge is to build up a robust methodology to perform a reliable…

Active Galactic Nuclei (AGN) can often be identified in radio images as two lobes, sometimes connected to a core by a radio jet. This multi-component morphology unfortunately creates difficulties for source-finders, leading to components…

Finding and classifying astronomical sources is key in the scientific exploitation of radio surveys. Source-finding usually involves identifying the parts of an image belonging to an astronomical source, against some estimated background.…

Morphologically classifying radio sources in continuum images with the SKA has the potential to address some of the key questions in cosmology and galaxy evolution. In particular, we may use different classes of radio sources as independent…

We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and…

This paper demonstrates a novel and efficient unsupervised clustering method with the combination of a Self-Organising Map (SOM) and a convolutional autoencoder. The rapidly increasing volume of radio-astronomical data has increased demand…

Autonomous digital sky surveys such as Pan-STARRS have the ability to image a very large number of galactic and extra-galactic objects, and the large and complex nature of the image data reinforces the use of automation. Here we describe…

Machine learning techniques have been increasingly used in astronomical applications and have proven to successfully classify objects in image data with high accuracy. The current work uses archival data from the Faint Images of the Radio…

In this paper we introduce a reliable, fully automated and fast algorithm to detect extended extragalactic radio sources (cluster of galaxies, filaments) in existing and forthcoming surveys (like LOFAR and SKA). The proposed solution is…

In this study, we examine over 14,000 radio galaxies finely selected from Radio Galaxy Zoo (RGZ) project and provide classifications for approximately 5,900 FRIs and 8,100 FRIIs. We present an analysis of these predicted radio galaxy…

With the advent of large scale surveys the manual analysis and classification of individual radio source morphologies is rendered impossible as existing approaches do not scale. The analysis of complex morphological features in the spatial…

Detecting diffuse radio emission, such as from halos, in galaxy clusters is crucial for understanding large-scale structure formation in the universe. Traditional methods, which rely on X-ray and Sunyaev-Zeldovich (SZ) cluster…

Bent radio active galactic nuclei (RAGNs) -- wide-angle tails (WATs) and narrow-angle tails (NATs) -- trace dense environments in galaxy groups and clusters, yet no multiclass classifier simultaneously separates them from straight…

Modern high-sensitivity radio telescopes are discovering an increased number of resolved sources with intricate radio structures and fainter radio emissions. These sources often present a challenge because source detectors might identify…

Radio galaxies exhibit a rich diversity of characteristics and emit radio emissions through a variety of radiation mechanisms, making their classification into distinct types based on morphology a complex challenge. To address this…

We present a technique to search for fast radio bursts in records obtained with broadband radiometers having few radio channels. The technique is applied to the RATAN-600 surveys carried out at its Western Sector since the year 2017. A 1D…

Galaxy morphology classification plays a crucial role in understanding the structure and evolution of the universe. With galaxy observation data growing exponentially, machine learning has become a core technology for this classification…