Time-dependent Pattern Speeds in Barred Galaxies

Wu, Yu-Ting; Pfenniger, D.; Taam, Ronald E.

doi:10.3847/1538-4357/aac5e8

Cited by 12 publications

(36 citation statements)

References 32 publications

(40 reference statements)

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“…Pattern speed and amplitude fluctuations of the secondary bar in double-barred N-body galactic discs was also reported by Debattista & Shen (2007). Wu et al (2018) noted that in the region between the inner and outer bars, there was a disagreement among their three p determination methods, which they attributed to the absence of a well-defined steady pattern. They measured negative pattern speeds reaching values of up to p ∼ −200 km s −1 kpc −1 (their fig.…”

Section: Relation To Other Theoretical Studiessupporting

confidence: 55%

“…Here we employ three methods to determine the bar length 2 of Model1, two of which have been widely used in the literature (e.g. Athanassoula & Misiriotis 2002;Wegg et al 2015;Wu et al 2018) and a new approach introduced below. We use a cut off |z| < 1 kpc, where z is the distance from the disc mid-plane, but the results do not vary wildly for other reasonable values.…”

Section: E a S U R E M E N T S O F Ba R L E N G T Hmentioning

confidence: 99%

“…In addition to these intrinsic variations, biases in p can arise from a measurement in the bar-spiral overlap region. Wu et al (2018) used three different methods to measure the instantaneous p in their simulations, finding a monotonous decrease with radius from ∼28 to ∼12 km s −1 kpc −1 (their fig. 3), as the bar (∼7 kpc long) transitioned to the spiral.…”

Section: Intrinsic Versus Apparent Bar Parameter Fluctuationsmentioning

confidence: 99%

“…Interestingly, the TW method applied to the same simulation appeared to capture these variations. Wu et al (2018) studied variations in bar length, strength, and pattern speed in a simulation of a double-barred galactic disc, attributing the majority of effects to the inner bar. They concluded that the primary bar was longest when disconnected from the spiral structure.…”

Section: Relation To Other Theoretical Studiesmentioning

confidence: 99%

“…Quillen et al (2011) noted that in their N-body simulations, the bar length visibly fluctuates in R-φ density maps, resulting from the interaction with the inner disc spiral structure, as spirals connect and disconnect from the bar ends. Timedependent fluctuations in bar length, strength, and pattern speed were found in double-barred N-body models by Wu, Pfenniger & Taam (2018), interpreted as the interaction between the two bars moving with different pattern speed.…”

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confidence: 97%

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Fluctuations in galactic bar parameters due to bar–spiral interaction

Hilmi

Minchev

Buck

et al. 2020

Monthly Notices of the Royal Astronomical Society

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Abstract We study the late-time evolution of the central regions of two Milky Way-like simulations of galaxies formed in a cosmological context, one hosting a fast bar and the other a slow one. We find that bar length, Rb, measurements fluctuate on a dynamical timescale by up to 100%, depending on the spiral structure strength and measurement threshold. The bar amplitude oscillates by about 15%, correlating with Rb. The Tremaine-Weinberg-method estimates of the bars’ instantaneous pattern speeds show variations around the mean of up to $\sim 20\%$, typically anti-correlating with the bar length and strength. Through power spectrum analyses, we establish that these bar pulsations, with a period in the range ∼60 − 200 Myr, result from its interaction with multiple spiral modes, which are coupled with the bar. Because of the presence of odd spiral modes, the two bar halves typically do not connect at exactly the same time to a spiral arm, and their individual lengths can be significantly offset. We estimated that in about 50% of bar measurements in Milky Way-mass external galaxies, the bar lengths of SBab type galaxies are overestimated by $\sim 15\%$ and those of SBbc types by $\sim 55\%$. Consequently, bars longer than their corotation radius reported in the literature, dubbed “ultra-fast bars”, may simply correspond to the largest biases. Given that the Scutum-Centaurus arm is likely connected to the near half of the Milky Way bar, recent direct measurements may be overestimating its length by 1 − 1.5 kpc, while its present pattern speed may be 5 − 10 $\rm km\ s^{-1}\ kpc^{-1}$smaller than its time-averaged value.

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Section: Relation To Other Theoretical Studiessupporting

confidence: 55%

Section: E a S U R E M E N T S O F Ba R L E N G T Hmentioning

confidence: 99%

Section: Intrinsic Versus Apparent Bar Parameter Fluctuationsmentioning

confidence: 99%

Section: Relation To Other Theoretical Studiesmentioning

confidence: 99%

mentioning

confidence: 97%

See 3 more Smart Citations

Fluctuations in galactic bar parameters due to bar–spiral interaction

Hilmi

Minchev

Buck

et al. 2020

Monthly Notices of the Royal Astronomical Society

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Bars formed in galaxy merging and their classification with deep learning

Cavanagh

Bekki

2020

A&A

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Context. Stellar bars are a common morphological feature of spiral galaxies. While it is known that they can form in isolation, or be induced tidally, few studies have explored the production of stellar bars in galaxy merging. We look to investigate bar formation in galaxy merging using methods from deep learning to analyse our N-body simulations. Aims. The primary aim is to determine the constraints on the mass ratio and orientations of merging galaxies that are most conducive to bar formation. We further aim to explore whether it is possible to classify simulated barred spiral galaxies based on the mechanism of their formation. We test the feasibility of this new classification schema with simulated galaxies. Methods. Using a set of 29 400 images obtained from our simulations, we first trained a convolutional neural network to distinguish between barred and non-barred galaxies. We then tested the network on simulations with different mass ratios and spin angles. We adapted the core neural network architecture for use with our additional aims. Results. We find that a strong inverse relationship exists between the mass ratio and the number of bars produced. We also identify two distinct phases in the bar formation process; (1) the initial, tidally induced formation pre-merger and (2) the destruction and/or regeneration of the bar during and after the merger. Conclusions. Mergers with low mass ratios and closely-aligned orientations are considerably more conducive to bar formation compared to equal-mass mergers. We demonstrate the flexibility of our deep learning approach by showing it is feasible to classify bars based on their formation mechanism.

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Five methods for determining pattern speeds in galaxies

Pfenniger¹,

Saha²,

Wu³

2023

A&A

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Context. After matter distribution and kinematics, the bar or spiral pattern speeds are the next fundamental parameters determining the dynamics of a galaxy. Aims. New or refined methods for determining instantaneous scalar and vector pattern speeds from a restricted domain are developed for applications in N-body simulations or in galaxies such as the Milky Way, for which the stellar coordinates become increasingly better known. Methods. The general feature used throughout follows from the fact that the time derivative of a function of the coordinates is linearly proportional to its rotation rate and its particle velocities. Knowing these therefore allows retrieving the instantaneous pattern speed vector by linear optimization. Similarly, if an invariant function depends on the position and velocities, then its instantaneous rotation vectors in space can be retrieved. Knowing the accelerations also allows determining the pattern rotation of velocity space. Results. The first three methods are based on the assumed rotational invariance of functions at each point in space or velocity space: (1) the 6D invariant function method, measuring the pattern speed vectors in space and velocity space, (2) the differential/regional 3D Tremaine-Weinberg method, evaluated over regions with a high signal-to-noise ratio, (3) the 3D Jacobi integral method, yielding the potential pattern speed. Extensions to derive the rotation center position, speed, and acceleration are introduced in the first and third methods. The last two methods are based on the assumed invariance of average functions of the particle coordinates: (4) the 2D and 3D moment of inertia methods by using the derivative of the singular value decomposition, (5) the 2D Fourier method (3D for m = 2 mode), giving the mode rotation speeds. Pattern speed accelerations are also derived in the fourth and fifth methods. Conclusions. Depending on the available data in specific problems, the different methods provide a choice of approaches.

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Time-dependent Pattern Speeds in Barred Galaxies

Cited by 12 publications

References 32 publications

Fluctuations in galactic bar parameters due to bar–spiral interaction

Fluctuations in galactic bar parameters due to bar–spiral interaction

Bars formed in galaxy merging and their classification with deep learning

Five methods for determining pattern speeds in galaxies

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