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Related papers: A Local Dwarf Galaxy Search Using Machine Learning

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Countless low-surface brightness objects - including spiral galaxies, dwarf galaxies, and noise patterns - have been detected in recent large surveys. Classically, astronomers visually inspect those detections to distinguish between real…

Astrophysics of Galaxies · Physics 2021-03-25 Oliver Müller , Eva Schnider

We undertook a search for new dwarf galaxies in the vicinity of relatively isolated nearby galaxies with distances $D < 12$ Mpc and stellar masses in the $2\times10^{11}-3\times10^8~M_{\odot}$ interval, using the data from the DESI Legacy…

Astrophysics of Galaxies · Physics 2023-02-22 I. D. Karachentsev , E. I. Kaisina

This paper follows series of our works on the applicability of various machine learning methods to the morphological galaxy classification (Vavilova et al., 2021, 2022). We exploited the sample of 315776 SDSS DR9 galaxies with absolute…

We present a machine learning search for high-redshift ($5.0 < z < 6.5$) quasars using the combined photometric data from the DESI Imaging Legacy Surveys and the WISE survey. We explore the imputation of missing values for high-redshift…

Astrophysics of Galaxies · Physics 2024-09-05 Guangping Ye , Huanian Zhang , Qingwen Wu

Quickly growing computing facilities and an increasing number of extragalactic observations encourage the application of data-driven approaches to uncover hidden relations from astronomical data. In this work we raise the problem of…

Cosmology and Nongalactic Astrophysics · Physics 2020-03-25 A. Elyiv , O. Melnyk , I. Vavilova , D. Dobrycheva , V. Karachentseva

The morphological types of 5840 galaxies were classified by a visual inspection of color images using the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) to produce a morphology catalog of a representative sample of local galaxies with…

Astrophysics of Galaxies · Physics 2015-06-23 H. B. Ann , Mira Seo , D. K. Ha

Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…

Cosmology and Nongalactic Astrophysics · Physics 2025-04-03 Juntao Ma , Jie Wang , Tianxiang Mao , Hongxiang Chen , Yuxi Meng , Xiaohu Yang , Qingyang Li

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a…

We have conducted a search of a 9 deg$^{2}$ region of the CFHTLS around the Milky Way analog M101 (D$\sim$7 Mpc), in order to look for previously unknown low surface brightness galaxies. This search has uncovered 38 new low surface…

Astrophysics of Galaxies · Physics 2017-12-06 P. Bennet , D. J. Sand , D. Crnojević , K. Spekkens , D. Zaritsky , A. Karunakaran

We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…

Astrophysics of Galaxies · Physics 2020-07-15 A. O. Clarke , A. M. M. Scaife , R. Greenhalgh , V. Griguta

We discuss the approach of searching low mass dwarf galaxies, $\lesssim10^6\textrm{ M}_{\odot}$, in the general field, using integrated light surveys. By exploring the limiting surface brightness-spatial resolution…

Astrophysics of Galaxies · Physics 2018-04-11 Shany Danieli , Pieter van Dokkum , Charlie Conroy

We undertook a search for new nearby dwarf galaxies outside the known groups in the Local Volume using the data on DESI Legacy Imaging Surveys. In a wide sky area of $\sim$5000 square degrees directed toward the Local Void, we found only 12…

Astrophysics of Galaxies · Physics 2023-12-11 I. D. Karachentsev , V. E. Karachentseva , S. S. Kaisin , E. I. Kaisina

The results of morphological galaxy classifications performed by humans and by automated methods are compared. In particular, a comparison is made between the eyeball classifications of 454 galaxies in the Sloan Digital Sky Survey (SDSS)…

Astrophysics · Physics 2007-05-23 Nicholas M. Ball

Dwarf satellite galaxies are a key probe of dark matter and of galaxy formation on small scales and of the dark matter halo masses of their central galaxies. They have very low surface brightness, which makes it difficult to identify and…

Astrophysics of Galaxies · Physics 2014-06-11 Allison Merritt , Pieter van Dokkum , Roberto Abraham

Mass loss is a key aspect of stellar evolution, particularly in evolved massive stars, yet episodic mass loss remains poorly understood. To investigate this, we need evolved massive stellar populations across various galactic environments.…

Methods. We used different galaxy classification techniques: human labeling, multi-photometry diagrams, Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, k-Nearest Neighbors, and k-fold validation. Results. We present…

Astrophysics of Galaxies · Physics 2021-06-09 I. B. Vavilova , D. V. Dobrycheva , M. Yu. Vasylenko , A. A. Elyiv , O. V. Melnyk , V. Khramtsov

The detection of low surface brightness galaxies beyond the Local Group poses significant observational challenges, yet these faint systems are fundamental to our understanding of dark matter, hierarchical galaxy formation, and cosmic…

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…

Astrophysics of Galaxies · Physics 2022-08-04 I. B. Vavilova , V. Khramtsov , D. V. Dobrycheva , M. Yu. Vasylenko , A. A. Elyiv , O. V. Melnyk

We review observational data about a sample of Local Volume objects containing about 1000 galaxies within 11 Mpc of the Milky Way. Dwarf galaxies with stellar masses $M_*/M_{\odot} < 9$ dex make up 5/6 of the sample. Almost 40% of them have…

Astrophysics of Galaxies · Physics 2019-06-19 I. D. Karachentsev , E. I Kaisina
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