The luminosity function of ringed galaxies

Smirnov, D. V.; Решетников, В. П.

doi:10.1093/mnras/stac2549

Cited by 5 publications

(4 citation statements)

References 77 publications

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“…The occurrence rate of polar-ring galaxies in Stripe 82 was estimated using the approach described in Smirnov & Reshetnikov (2022). Initially, the completeness of the PRG and the entire Stripe 82 samples was assessed using the V/V max method (Huchra & Sargent 1973), resulting in correction factors of 2.32 and 1.41, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Reshetnikov et al (2011) estimated that 0.17% of nearby galaxies with absolute magnitudes in the range of M B = −17 to −22 mag may have polar rings; however, if projection effects are taken into account, this fraction amounts to ∼0.4%. Recently, Smirnov & Reshetnikov (2022) investigated the luminosity functions of 103 'best' candidates for PRGs selected from Whitmore et al (1990), the SPRC, and Reshetnikov & Mosenkov (2019). They concluded that only ∼0.01% of nearby galaxies with absolute magnitudes in the range M r = −17 to −22 mag have polar rings, but their frequency of occurrence increases significantly with redshift up to z ∼ 1.…”

Section: Introductionmentioning

confidence: 99%

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The occurrence rate of galaxies with polar structures may be significantly underestimated

Mosenkov,

Bahr,

Reshetnikov

et al. 2024

A&A

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Polar-ring galaxies are photometrically and kinematically decoupled systems that are highly inclined to the major axis of the host. These galaxies have been explored since the 1970s, but the rarity of these systems has made such systematic studies difficult. However, over 250 good candidates have been identified. In this work, we examine a sample of over 18,000 galaxies from the Sloan Digital Sky Survey (SDSS) Stripe\,82 for the presence of galaxies with polar structures. Using deep SDSS Stripe\,82, DESI Legacy Imaging Surveys, and Hyper Suprime-Cam Subaru Strategic Program, we selected 53 good candidate galaxies with photometrically decoupled polar rings, 9 galaxies with polar halos, 6 galaxies with polar bulges, and 34 possibly forming polar-ring galaxies, versus 13 polar-ring candidates previously selected in Stripe\,82. Our results suggest that the occurrence rate of galaxies with polar structures may be significantly underestimated, as revealed by the deep observations, and may amount to 1-3<!PCT!> of non-dwarf galaxies.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The occurrence rate of galaxies with polar structures may be significantly underestimated

Mosenkov,

Bahr,

Reshetnikov

et al. 2024

A&A

Self Cite

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Section: Notes On Problem Points Of Cnn Image-based Galaxy Classifica...mentioning

confidence: 99%

“…The creation of the representative catalog of galaxies with ring(s) could be very useful ( [130,131]). For example, Smirnov and Reshetnikov [131] collected the samples of polar-and collision-ring galaxies from all the published data in several deep fields. Doing this painstaking preliminary search, they constructed the luminosity function for the ringed galaxies and confirmed the increase in their volume density with redshift: up to z ~1 their density grows as (1 + z) m , where m  5.…”

Section: Notes On Problem Points Of Cnn Image-based Galaxy Classifica...mentioning

confidence: 99%

Machine learning technique for morphological classification of galaxies from the SDSS. III. The CNN image-based inference of detailed features

Khramtsov¹,

Vavilova²,

Dobrycheva³

et al. 2022

Kosm. nauka tehnol.

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This paper follows a series of our works on the applicability of various machine learning methods to morphological galaxy classification (Vavilova et al., 2021, 2022). We exploited the sample of ~315800 low-redshift SDSS DR9 galaxies with absolute stellar magnitudes of −24m < Mr < −19.4m at 0.003 < z < 0.1 redshifts as a target data set for the CNN classifier. Because it is tightly overlapped with the Galaxy Zoo 2 (GZ2) sample, we use these annotated data as the training data set to classify galaxies into 34 detailed features. In the presence of a pronounced difference in visual parameters between galaxies from the GZ2 training data set and galaxies without known morphological parameters, we applied novel procedures, which allowed us for the first time to get rid of this difference for smaller and fainter SDSS galaxies with mr < 17.7. We describe in detail the adversarial validation technique as well as how we managed the optimal train-test split of galaxies from the training data set to verify our CNN model based on the DenseNet-201 realistically. We have also found optimal galaxy image transformations, which help increase the classifier’s generalization ability. We demonstrate for the first time that implication of the CNN model with a train-test split of data sets and size-changing function simulating a decrease in magnitude and size (data augmentation) significantly improves the classification of smaller and fainter SDSS galaxies. It can be considered as another way to improve the human bias for those galaxy images that had a poor vote classification in the GZ project. Such an approach, like autoimmunization, when the CNN classifier, trained on very good galaxy images, is able to retrain bad images from the same homogeneous sample, can be considered co-planar to other methods of combating such a human bias. The most promising result is related to the CNN prediction probability in the classification of detailed features. The accuracy of the CNN classifier is in the range of 83.3—99.4 % depending on 32 features (exception is for “disturbed” (68.55 %) and “arms winding medium” (77.39 %) features). As a result, for the first time, we assigned the detailed morphological classification for more than 140000 low-redshift galaxies, especially at the fainter end. A visual inspection of the samples of galaxies with certain morphological features allowed us to reveal typical problem points of galaxy image classification by shape and features from the astronomical point of view. The morphological catalogs of low-redshift SDSS galaxies with the most interesting features are available through the UkrVO website (http://ukr-vo.org/galaxies/) and VizieR.

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Revealing ringed galaxies in group environments

Fernandez,

Alonso,

Mesa

et al. 2024

A&A

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We explore galaxies with ringed structures inhabiting poor and rich groups with the aim of assessing the effects of local density environments on ringed galaxy properties. We identified galaxies with inner, outer, nuclear, inner+outer (inner and outer rings combined), and partial rings that reside in groups by cross-correlating a sample of ringed galaxies with a group catalog obtained from Sloan Digital Sky Survey (SDSS). The resulting sample was divided based on group richness, with groups having $3 rich 10$ members classified as poor, while groups having $11 rich 50$ were classified as rich. To quantify the effects of rings and the role of local density environment on galaxy properties, we constructed a suitable control sample for each catalog of ringed galaxies in poor and rich groups, consisting of non-ringed galaxies with similar values for the redshift, magnitude, morphology, group masses, and environmental density distributions as those of ringed ones. We explored the occurrence of ringed galaxies in poor and rich groups and analyzed several galaxy properties, such as star formation activity, stellar populations, and colors, with respect to the corresponding comparison samples. We obtained a sample of 637 ringed galaxies residing in groups. We found that about 76<!PCT!> of these galaxies inhabit poor groups, whereas only about 24<!PCT!> are present in rich groups. Inner rings are prevalent in both rich and poor groups, while nuclear rings are the least common in both groups. Regarding the control sample, about 81<!PCT!> galaxies are found in poor groups and about 19<!PCT!> in rich ones. We find that the percentages of ringed galaxies with bar structures are similar, regardless of whether the group is rich or poor. In addition, ringed galaxies inhabiting groups display a reduction in their star formation activity and aged stellar populations, compared to non-ringed ones in the corresponding control samples. However, the star formation rate is higher for nuclear rings in poor groups than for other types. This disparity may stem from the environmental influence on the internal processes of galaxies, either enhancing or diminishing star formation. Ringed galaxies also show an excess of red colors and tend to populate the green valley and the red sequence of color-magnitude and color-color diagrams, with a surplus of galaxies in the red sequence, while non-ringed galaxies are found in the green valley and the blue region. These trends are more significant in galaxies with ringed structures residing in rich groups. Our findings provide valuable insights into the relationship between ringed structures and their surrounding environments, paving the way for further explorations in this area of study.

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The luminosity function of ringed galaxies

Cited by 5 publications

References 77 publications

The occurrence rate of galaxies with polar structures may be significantly underestimated

The occurrence rate of galaxies with polar structures may be significantly underestimated

Machine learning technique for morphological classification of galaxies from the SDSS. III. The CNN image-based inference of detailed features

Revealing ringed galaxies in group environments

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