The evolution of barred galaxies in the EAGLE simulations

Cavanagh, Mitchell K; Bekki, Kenji; Groves, Brent; Pfeffer, Joel

doi:10.1093/mnras/stab3786

Cited by 24 publications

(15 citation statements)

References 166 publications

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“…Almost all the previous studies done in classification of galaxies are done to classify galaxies into at most 5 classes (Walmsley et al 2020) (Mittal et al 2020) (Jiménez et al 2020) (Eassa et al 2021) (Bom et al 2021) (Cavanagh et al 2021). But considering the development in theoretical aspects, we found that there was need of classifying it into more classes.…”

Section: Resultsmentioning

confidence: 81%

“…spiral and elliptical. (Cavanagh et al 2021) have proposed 4 different models for classifying data into 2 (elliptical and spiral), 3 (elliptical, spiral and lenticular)and 4 (elliptical, spiral, lenticular and others) classes respectively. Due to limitation of data, the authors have rotated and flipped the images to generate additional data images.…”

Section: Related Workmentioning

confidence: 99%

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Galaxy classification: a deep learning approach for classifying Sloan Digital Sky Survey images

Gharat

Dandawate

2022

Monthly Notices of the Royal Astronomical Society

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In recent decade, large scale sky surveys such as Sloan Digital Sky Survey (SDSS) has resulted in generation of tremendous amount of data. The classification of this enormous amount of data by astronomers is time consuming. To simplify this process, in 2007 a volunteer based citizen science project called ‘Galaxy Zoo’ was introduced which has reduced the time for classification by a good extent. However, in this modern era of deep learning, automating this classification task is highly beneficial as it reduces the time for classification. Since last few years, many algorithms have been proposed which happen to do a phenomenal job in classifying galaxies into multiple classes. But all these algorithms tend to classify galaxies into less than 6 classes. However after considering the minute information which we know about galaxies, it is necessary to classify galaxies into more than 8 classes. In this study, a neural network model is proposed so as to classify SDSS data into 10 classes from an extended Hubble Tuning Fork. Great care is given to disk edge and disk face galaxies, distinguishing between a variety of substructures and minute features which are associated to each class. The proposed model consists of convolution layers to extract features making this method fully automatic. The achieved test accuracy is 84.73 per cent which happens to be promising after considering such minute details in classes. Along with convolution layers, the proposed model has 3 more layers responsible for classification which makes the algorithm consume less time.

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Section: Resultsmentioning

confidence: 81%

Section: Related Workmentioning

confidence: 99%

Galaxy classification: a deep learning approach for classifying Sloan Digital Sky Survey images

Gharat

Dandawate

2022

Monthly Notices of the Royal Astronomical Society

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“…C1). Given that these models are for bulge-dominated S0s, these results indicates that bar formation due to spontaneous bar instability and tidal interaction is unlikely in S0s thus provides a physical reason why the bar fraction in S0s is small both in observational studies and in cosmological simulations (e.g., Cavanagh et al 2022 for recent discussion on this topic). It can be seen clearly, however, that the orbital eccentricities of stars (e) in the tidal model become significantly higher: local APS do not work to form seed bars though.…”

Section: Appendix C: ωPre − E Maps In Other Modelsmentioning

confidence: 86%

“…In this paper, we do not discuss other key issues related to bar formation and evolution, such as dynamical interaction between bars and dark matter halos (e.g., Weinberg 1985;Debattista & Sellwood 2000). and redshift evolution of bars (e.g., Abraham et al 1999;Jogee et al 2004;Sheth et al 2008;Cavanagh et al 2022).…”

Section: Introductionmentioning

confidence: 99%

A mechanism of bar formation in disk galaxies: synchronization of apsidal precession

Bekki¹

2022

Preprint

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We discuss the mechanism(s) of bar formation in isolated and tidally interacting disk galaxies using the results of idealized collisionless Nbody simulations of the galaxies. In order to better understand the mechanism, we investigate orbital eccentricities (e), epochs of apocenter passages (t a ), azimuthal angles at t a (ϕ a ), precession rates (Ω pre ), for individual stars, as well as bar strengths represented by relative m = 2 Fourier amplitude (A 2 ) and bar pattern speeds (Ω bar ). The main results are as follows. A significant fraction of stars with initially different ϕ a and Ω pre in an isolated disk galaxy can have similar values within several dynamical timescales. This synchronization of ϕ a and Ω pre , which is referred to as apsidal precession synchronization ("APS") in the present study, is caused by the enhanced strength of the tangential component of gravitational force. A weak seed bar (A 2 < 0.1) is first formed through APS in local regions of a disk, then the bar grows due to APS. In the bar growth phase (0.1 < A 2 < 0.4), APS can proceed more efficiently due to stronger tangential force from the bar so that it can enhance the bar strength further. This positive feedback loop in APS is the key physical mechanism of bar growth in isolated stellar disks. Bar formation can be severely suppressed in disks with lower disk mass fractions and/or higher Q parameters due to much less efficient APS. APS proceeds more rapidly and more efficiently due to strong tidal perturbation in the formation of tidal bars compared to spontaneous bar formation.

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“…In any case, this component emerges out of our analysis even if we assume a more restrictive criteria to exclude barred galaxies. However, the fact that the galaxies in our sample do not have a bar now does not necessarily imply they never had one, as it is still an open question whether bars are transient features (Bournaud & Combes 2002;Peschken & Łokas 2018;Cavanagh et al 2022) or not (Athanassoula 2005;Athanassoula et al 2013;Kim et al 2016). To clarify the nature of the spheroids in these EAGLE galaxies, we have to constrain how these components formed, an analysis which we leave for future work.…”

Section: Summary and Discussionmentioning

confidence: 97%

Milky Way-like galaxies: stellar population properties of dynamically defined disks, bulges and stellar halos

Sara¹,

Obreja²,

Domínguez-Tenreiro³

et al. 2022

Preprint

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The formation of galaxies can be understood in terms of the assembly patterns of each type of galactic component. To perform this kind of analysis, is necessary to define some criteria to separate those components. Decomposition methods based on dynamical properties are more physically motivated than photometry-based ones. We use the unsupervised Gaussian Mixture model of galactic structure finder to extract the components of a sub-sample of galaxies with Milky Way-like masses from the EAGLE simulations. A clustering in the space of first and second order dynamical moments of all identified substructures reveals five types of galaxy components: thin and thick disks, stellar halos, bulges and spheroids. We analyse the dynamical, morphological and stellar population properties of these five component types, exploring to what extent these properties correlate with each other, and how much they depend on the total galaxy stellar and dark matter halo masses. All galaxies contain a bulge, a stellar halo and a disk. 60% of objects host two disks (thin and thick), and 68% host also a spheroid. The dynamical diskto-total ratio does not depend on stellar mass, but the median rotational velocities of the two disks do. Thin disks are well separated in stellar ages, [Fe/H] and 𝛼-enhancement from the three dispersion-dominated components, while thick disks are in between. Except for thin disks, all components show correlations among their stellar population properties: older ages mean lower metallicities and larger 𝛼-enhancement. Finally, we quantify the weak dependence of stellar population properties on each component's dynamics.

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The evolution of barred galaxies in the EAGLE simulations

Cited by 24 publications

References 166 publications

Galaxy classification: a deep learning approach for classifying Sloan Digital Sky Survey images

Galaxy classification: a deep learning approach for classifying Sloan Digital Sky Survey images

A mechanism of bar formation in disk galaxies: synchronization of apsidal precession

Milky Way-like galaxies: stellar population properties of dynamically defined disks, bulges and stellar halos

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