Dark matter makes up 85% of the mass in the universe. It does not reflect or produce any form of electromagnetic radiation, like light, which makes it difficult to detect. We know of its existence through its gravitational effects on visible matter. Recent research suggests that the Large Magellanic Cloud (LMC), which is the largest satellite galaxy of the Milky Way (MW), is moving quickly towards the MW's dark matter halo, causing an imbalance in it. As the LMC moves, it deflects the MW's dark matter particles, creating patterns called "dark matter wakes" behind it, similar to the wake created by a duck in water. We can observe this effect from Earth because our solar system is located in one of the MW's spiral arms.
This document discusses an algorithm for identifying and characterizing dark matter wakes in galaxy simulations. Our method involves analyzing the changes in energy and angular momentum of MK DM halo interacting with the Large Magellanic Cloud, simulated by ( Garavito-Camargo et al., 2019). We use data from a simulation with no disturbances to identify particles with low momentum, which then we use as a filter in a simulation pertubed by LMC. We study how the density and velocity of the selected particles change as they cluster behind the LMC galaxy.
Contour plots of DM particle’s position show strong asymmetrical overdensities as particles compress near the LMC's orbit with higher density contrast than in the unperturbed halo. The velocity field changed significantly after interacting with the LMC galaxy. Initially, it was isotropic. However, afterwards, it aligned with the LMC orbit, and the divergence was positive. The standard deviation of angles and circular variance were much lower compared to the unperturbed halo. Identifying dark matter wake regions in the phase diagram ould be used for comparing them to observed stellar density in those regions, without requiring observational data before interaction as needed by the selection by angular momentum increments method alone. To improve selection, geometrical metrics can be explored to compare MW DM halo's response in different simulation snapshots. Combining kinematic intuition with machine automation tools could make it easier to identify substructures in the energy-momentum diagram.
In the future, this algorithm could be used to predict where dark matter wakes can be found and applied to other simulations, such as the FIRE project. Automating this task could create synthetic catalogs for studying dark matter wake phenomena and validating predictions with observational data from surveys like Gaia, LSST, and DESI. This data could provide evidence of halos' response to dark matter wakes and serve as a probe for test dark matter models.
DM does not emit electromagnetic radiation and interacts with ordinary matter solely through gravity. Gravity is the primary force associated with large distances in the universe, and studying how massive bodies such as galaxies interact could improve our understanding of the physics of DM particles.
There is significant evidence supporting the existence of dark matter (DM). It was originally proposed to account for the missing mass in galaxy rotation curves and is approximately five times more abundant than visible matter based on stellar observations. Since then, dark matter has become necessary to model the evolution of galaxies and the large-scale structure of the Universe with our current understanding of physics. Additional evidence has been found through observations of gravitational lensing (Madhavacheril), which revealed large concentrations of dark matter that bend space-time. The Cosmic Microwave Background also revealed anisotropy patterns and provided information on the distribution and amount of matter in the early Universe. Its presence is necessary to account for the early stages of the Universe, when baryonic matter alone was too hot to form self-bound objects by gravity.
Galaxies form inside a DM halo that envelops their stellar disk. (Wechsler), However, much remains unknown about the physics governing DM halos and their constituent particles. To understand the effects of dark matter models on the evolution of the universe, one can compare observational data with numerical simulations. N-body simulations are particularly useful for this task since they can provide high-resolution information on halo mass distributions for both small and large structures. These simulations involve studying a system of DM particles that represent galaxies, stars, or dark matter particles through numerical integration. The positions and velocities of these particles are updated over a specified time by solving equations of motion. Astrometry data, which includes position coordinates, radial velocity, and proper motions, provides useful information in understanding the internal dynamics of galaxies.
Currently, there are numerous ongoing efforts to search for dark matter. Direct approaches include experimental particle physics and superconducting detectors, while indirect approaches study the sun and observe cosmic rays. Despite the many candidates and models for DM particles, testing them is difficult because they do not emit electromagnetic radiation. However, as dark matter interacts gravitationally and is more abundant than ordinary matter, the DM halo particles could influence their stellar counterparts when perturbations, such as a passing satellite, are introduced.
Analyzing the velocities and distribution of dark matter particles can provide insights into the morphology of dark matter halos and the characteristics of dark matter. Different DM models can result in different perturbations in DM halos, making such analyses useful for distinguishing between models.
The easiest natural laboratory to access is our own galaxy, the Milky Way (MW). We can measure the 3D velocities and positions of stars, as well as their chemical information. This multidimensional view of the MW galaxy is not possible in external galaxies. By observing these measures, we can learn about dynamical interactions, as well as the behavior of dark matter and galaxy evolution. Our location in one of the spiral arms of the MW galaxy allows us to see this response.
The Large Magellanic Cloud (LMC) is the most massive satellite galaxy of the Milky Way (MW), accounting for approximately one-tenth of the host's mass. Currently, it is nearly 50 kpc away, just past the pericenter, and appears to be moving at a speed of 327 km/s. Some studies suggest that the LMC is making its first passage, which could potentially dislodge the MW halo from its galactic center, resulting in a system that is out of equilibrium. These events have been observed in (Petersen 2020, Erkal 2020). They influence the structure of the Milky Way as critical factors in its evolution.
The interaction between a satellite galaxy's orbital motion and the natural frequency of dark matter particles in the halo can result in resonances. The orbital frequency of the satellite is the rate at which the LMC orbits around the MW, and it is determined by mutual gravitational forces. When the frequencies align, the particles become trapped in resonant orbits. They share similar velocities and orbital characteristics, which leads to the formation of gravitational density dark matter wakes trailing the satellite. This eventually causes the satellite's orbit to decay via a process called dynamical friction(See Chandrasekhar ).
Dynamical friction therefore is a force associated with the direct gravitational scattering of particles. It slows down particles in the direction of movement, generating losses of angular momentum and energy. (Saleh, Alladin) When the satellite galaxy loses kinetic energy, it moves toward the center of the galaxy given that its orbit loss eccentricity by a process named tidal circularization.
The drag effect is stronger for massive objects as particles in the MW’s DM halo are attracted gravitationally by the LMC and, after it moves, they concentrate behind it, exerting a collective gravitational force on the LMC (Tremaine), forming trails in a predominant direction related to the satellite's orbit. It resembles a density wave that interacts gravitationally. By exploring these clusters and delimiting the selection of these particles by parameters such as changes in angular momentum, regions where dark matter wakes are likely to form can be identified.
In a recent research Garavito-Camargo et al., 2019) used N-body simulations to investigate the interaction between the dark matter halos of galaxies, specifically the MW-LMC system, over the past 2 Gyrs, a time scale chosen to minimize changes in the LMC's orbit caused by uncertainties in the MW mass . They also determined the appropriate number of particles needed to produce perturbations and performed convergence tests on the initial conditions of the LMC's orbit, mass values, and properties of the dynamical response. These tests affected the amplitude of the dark matter wake but not its morphology. In this system, it has been shown that angular momentum and energy are transferred as the orbital barycenter of the MW moves due to the gravitational pull produced by the LMC. They showed that the Large Magellanic Cloud's passage through the dark matter halo induced kinematic imprints, as dynamical friction effect is relevant for close equal mass-ratio 1: 10. including transient and collective responses.