Radio Sources Segmentation and Classification with Deep Learning Article Swipe

MAKE IMPACT

Baoqiang Lao , Sumit Jaiswal , Zhen Zhao , Leping Lin , Junyi Wang , Xiaohui Sun , Sheng‐Li Qin · YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2306.01426

Modern large radio continuum surveys have high sensitivity and resolution, and can resolve previously undetected extended and diffuse emissions, which brings great challenges for the detection and morphological classification of extended sources. We present HeTu-v2, a deep learning-based source detector that uses the combined networks of Mask Region-based Convolutional Neural Networks (Mask R-CNN) and a Transformer block to achieve high-quality radio sources segmentation and classification. The sources are classified into 5 categories: Compact or point-like sources (CS), Fanaroff-Riley Type I (FRI), Fanaroff-Riley Type II (FRII), Head-Tail (HT), and Core-Jet (CJ) sources. HeTu-v2 has been trained and validated with the data from the Faint Images of the Radio Sky at Twenty-one centimeters (FIRST). We found that HeTu-v2 has a high accuracy with a mean average precision ($AP_{\rm @50:5:95}$) of 77.8%, which is 15.6 points and 11.3 points higher than that of HeTu-v1 and the original Mask R-CNN respectively. We produced a FIRST morphological catalog (FIRST-HeTu) using HeTu-v2, which contains 835,435 sources and achieves 98.6% of completeness and up to 98.5% of accuracy compared to the latest 2014 data release of the FIRST survey. HeTu-v2 could also be employed for other astronomical tasks like building sky models, associating radio components, and classifying radio galaxies.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2306.01426

PDF

https://arxiv.org/pdf/2306.01426

OA Status

green

Related Works

10

OpenAlex ID

https://openalex.org/W4379538466

Raw OpenAlex JSON

OpenAlex ID

https://openalex.org/W4379538466

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.48550/arxiv.2306.01426

Digital Object Identifier

Title

Radio Sources Segmentation and Classification with Deep Learning

Work title

Type

preprint

OpenAlex work type

Language

en

Primary language

Publication year

2023

Year of publication

Publication date

2023-06-02

Full publication date if available

Authors

Baoqiang Lao, Sumit Jaiswal, Zhen Zhao, Leping Lin, Junyi Wang, Xiaohui Sun, Sheng‐Li Qin

List of authors in order

Landing page

https://arxiv.org/abs/2306.01426

Publisher landing page

PDF URL

https://arxiv.org/pdf/2306.01426

Direct link to full text PDF

Open access

Yes

Whether a free full text is available

OA status

green

Open access status per OpenAlex

OA URL

https://arxiv.org/pdf/2306.01426

Direct OA link when available

Concepts

Sky, Artificial intelligence, Convolutional neural network, Computer science, Segmentation, Deep learning, Detector, Radio galaxy, Pattern recognition (psychology), Galaxy, Physics, Astrophysics, Telecommunications

Top concepts (fields/topics) attached by OpenAlex

Cited by

0

Total citation count in OpenAlex

Related works (count)

10

Other works algorithmically related by OpenAlex

Full payload