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相关论文: Classification of Spiral Galaxies by Spiral Arm Nu…

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We report an automated morphological classification of galaxies into S-wise spirals, Z-wise spirals, and non-spirals using big image data taken from Subaru/Hyper Suprime-Cam (HSC) Survey and a convolutional neural network(CNN)-based deep…

Distinguishing galaxies as either fast or slow rotators plays a vital role in understanding the processes behind galaxy formation and evolution. Standard techniques, which are based on the $\lambda_R$-spin parameter obtained from stellar…

星系天体物理 · 物理学 2025-03-14 Amirmohammad Chegeni , Fatemeh Fazel Hesar , Mojtaba Raouf , Bernard Foing , Fons J. Verbeek

Classifying the morphologies of galaxies is an important step in understanding their physical properties and evolutionary histories. The advent of large-scale surveys has hastened the need to develop techniques for automated morphological…

星系天体物理 · 物理学 2021-12-28 Mitchell K. Cavanagh , Kenji Bekki , Brent A. Groves

Spiral galaxies can be classified into the {\it Grand-designs} and {\it Flocculents} based on the nature of their spiral arms. The {\it Grand-designs} exhibit almost continuous and high contrast spiral arms and are believed to be driven by…

星系天体物理 · 物理学 2022-11-30 Suman Sarkar , Ganesh Narayanan , Arunima Banerjee , Prem Prakash

In this paper we study the morphological properties of spiral galaxies, including measurements of spiral arm number and pitch angle. Using Galaxy Zoo 2, a stellar mass-complete sample of 6,222 SDSS spiral galaxies is selected. We use the…

In this work we determine the parameters of spiral structure for a sample of face-on spiral galaxies. In practice, the solution of this problem is a hard task because of the diversity of the observed characteristics of spiral structure,…

星系天体物理 · 物理学 2020-09-02 Aleksandr Mosenkov , Sergey Savchenko , Alexander Marchuk

Spatial correlations between spiral arms and other galactic components such as giant molecular clouds and massive OB stars suggest that spiral arms can play vital roles in various aspects of disk galaxy evolution. Segmentation of spiral…

星系天体物理 · 物理学 2021-03-24 Kenji Bekki

Given an approximately centered image of a spiral galaxy, we describe an entirely automated method that finds, centers, and sizes the galaxy and then automatically extracts structural information about the spiral arms. For each arm segment…

星系天体物理 · 物理学 2015-06-18 Darren R. Davis , Wayne B. Hayes

Morphology is the starting point for understanding galaxies. Elmegreen et al. classified spiral galaxies into flocculent, multiple-arm, and grand-design galaxies based on the regularity of their spiral arm structure. With the release of a…

星系天体物理 · 物理学 2024-10-11 Junye Wei , Ye Xu , Zehao Lin , Chaojie Hao , Yingjie Li , Dejian Liu , Shuaibo Bian

The majority of galaxies in the local Universe exhibit spiral structure with a variety of forms. Many galaxies possess two prominent spiral arms, some have more, while others display a many-armed flocculent appearance. Spiral arms are…

We developed an algorithm to use Galaxy Zoo 3D spiral arm masks produced by citizen scientist volunteers to semi-automatically classify spiral galaxies as either multi-armed or grand design spirals. Our final sample consists of 299…

星系天体物理 · 物理学 2024-05-03 Beverly J. Smith , Matthew Watson , Mark L. Giroux , Curtis Struck

The classification of galaxies as spirals or ellipticals is a crucial task in understanding their formation and evolution. With the arrival of large-scale astronomical surveys, such as the Sloan Digital Sky Survey (SDSS), astronomers now…

计算机视觉与模式识别 · 计算机科学 2023-12-04 Anusha Guruprasad

Different spiral generation mechanisms are expected to produce different morphological and kinematic features. In this first paper in a series we carefully study the parameters of spiral structure in 155 face-on spiral galaxies, selected…

星系天体物理 · 物理学 2020-02-05 Sergey Savchenko , Alexander Marchuk , Aleksandr Mosenkov , Konstantin Grishunin

Numerous studies have demonstrated the ability of Convolutional Neural Networks (CNNs) to classify large numbers of galaxies in a manner which mimics the expertise of astronomers. Such classifications are not always physically motivated,…

星系天体物理 · 物理学 2019-04-24 J. D. Diaz , Kenji Bekki , Duncan A. Forbes , Warrick J. Couch , Michael J. Drinkwater , Simon Deeley

We applied the image-based approach with a convolutional neural network model to the sample of low-redshifts galaxies with $-24^{m}<M_{r}<-19.4^{m}$ from the SDSS DR9. We divided it into two subsamples, SDSS DR9 galaxy dataset and Galaxy…

星系天体物理 · 物理学 2022-08-04 I. B. Vavilova , V. Khramtsov , D. V. Dobrycheva , M. Yu. Vasylenko , A. A. Elyiv , O. V. Melnyk

We use classifications provided by citizen scientists though Galaxy Zoo to investigate the correlation between bulge size and arm winding in spiral galaxies. Whilst the traditional spiral sequence is based on a combination of both measures,…

Recent JWST observations have revealed the prevalence of spiral structures at $z > 1$. Unlike in the local Universe, the origin and the consequence of spirals at this epoch remain unexplored. We use public JWST/NIRCam data from the…

In this paper, a deep convolutional neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into main three categories Elliptical, Spiral, and Irregular. The proposed deep…

计算机视觉与模式识别 · 计算机科学 2017-09-08 Nour Eldeen M. Khalifa , Mohamed Hamed N. Taha , Aboul Ella Hassanien , I. M. Selim

Spiral arms are the defining features of broad morphological classes of disc galaxies, but their nature and influence on galaxy evolution is still under debate. A key diagnostic for their nature is the spiral arm pattern speed: the radial…

天体物理仪器与方法 · 物理学 2025-12-22 Robert J. J. Grand , Martin Roth

Deep neural networks (DNNs) with a step-by-step introduction of inputs, which is constructed by imitating the somatosensory system in human body, known as SpinalNet have been implemented in this work on a Galaxy Zoo dataset. The input…

机器学习 · 计算机科学 2023-05-04 Dim Shaiakhmetov , Remudin Reshid Mekuria , Ruslan Isaev , Fatma Unsal
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