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Register a new userDynamical Modeling of Galaxy Mergers using Identikit
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We present dynamical models of four interacting systems: NGC 5257/8, The Mice, the Antennae, and NGC 2623. The parameter space of the encounters are constrained using the Identikit model-matching and visualization tool. Identikit utilizes hybrid N-body and test particle simulations to enable rapid exploration of the parameter space of galaxy mergers. The Identikit-derived matches of these systems are reproduced with self-consistent collisionless simulations which show very similar results. The models generally reproduce the observed morphology and HI kinematics of the tidal tails in these systems with reasonable properties inferred for the progenitor galaxies. The models presented here are the first to appear in the literature for NGC 5257/8 and NGC 2623, and The Mice and the Antennae are compared with previously published models. Based on the assumed mass model and our derived initial conditions, the models indicate the four systems are currently being viewed 175-260 Myr after first passage and cover a wide range of merger stages. In some instances there are mismatches between the models and the data (e.g., in the length of a tail); these are likely due to our adoption of a single mass model for all galaxies. Despite the use of a single mass model, these results demonstrate the utility of Identikit in constraining the parameter space for galaxy mergers when applied to real data.
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We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning (ML) algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach. We create two mock catalogs from Multidarks publicly available $N$-body MDPL1 simulation, one with perfect galaxy cluster membership information and the other where a simple cylindrical cut around the cluster center allows interlopers to contaminate the clusters. In the standard approach, we use a power-law scaling relation to infer cluster mass from galaxy line-of-sight (LOS) velocity dispersion. Assuming perfect membership knowledge, this unrealistic case produces a wide fractional mass error distribution, with a width of $Deltaepsilonapprox0.87$. Interlopers introduce additional scatter, significantly widening the error distribution further ($Deltaepsilonapprox2.13$). We employ the support distribution machine (SDM) class of algorithms to learn from distributions of data to predict single values. Applied to distributions of galaxy observables such as LOS velocity and projected distance from the cluster center, SDM yields better than a factor-of-two improvement ($Deltaepsilonapprox0.67$) for the contaminated case. Remarkably, SDM applied to contaminated clusters is better able to recover masses than even the scaling relation approach applied to uncontaminated clusters. We show that the SDM method more accurately reproduces the cluster mass function, making it a valuable tool for employing cluster observations to evaluate cosmological models.
We introduce a method for modeling disk galaxies designed to take full advantage of data from integral field spectroscopy (IFS). The method fits equilibrium models to simultaneously reproduce the surface brightness, rotation and velocity dispersion profiles of a galaxy. The models are fully self-consistent 6D distribution functions for a galaxy with a Sersic-profile stellar bulge, exponential disk and parametric dark matter halo, generated by an updated version of GalactICS. By creating realistic flux-weighted maps of the kinematic moments (flux, mean velocity and dispersion), we simultaneously fit photometric and spectroscopic data using both maximum-likelihood and Bayesian (MCMC) techniques. We apply the method to a GAMA spiral galaxy (G79635) with kinematics from the SAMI Galaxy Survey and deep $g$- and $r$-band photometry from the VST-KiDS survey, comparing parameter constraints with those from traditional 2D bulge-disk decomposition. Our method returns broadly consistent results for shared parameters, while constraining the mass-to-light ratios of stellar components and reproducing the HI-inferred circular velocity well beyond the limits of the SAMI data. While the method is tailored for fitting integral field kinematic data, it can use other dynamical constraints like central fibre dispersions and HI circular velocities, and is well-suited for modelling galaxies with a combination of deep imaging and HI and/or optical spectra (resolved or otherwise). Our implementation (MagRite) is computationally efficient and can generate well-resolved models and kinematic maps in under a minute on modern processors.
We present a series of hundreds of collisionless simulations of galaxy group mergers. These simulations are designed to test whether the properties of elliptical galaxies - including the key fundamental plane scaling relation, morphology and kinematics - can be simultaneously reproduced by dry multiple mergers in galaxy groups. Preliminary results indicate that galaxy group mergers can produce elliptical remnants lying on a tilted fundamental plane, even without a central dissipational component from a starburst. This suggests that multiple mergers in groups are an alternate avenue for the formation of elliptical galaxies which could well dominate for luminous ellipticals.
We present hydrodynamic simulations of a major merger of disk galaxies, and study the ISM dynamics and star formation properties. High spatial and mass resolutions of 12pc and 4x10^4 M_sol allow to resolve cold and turbulent gas clouds embedded in a warmer diffuse phase. We compare to lower resolution models, where the multiphase ISM is not resolved and is modeled as a relatively homogeneous and stable medium. While merger-driven bursts of star formation are generally attributed to large-scale gas inflows towards the nuclear regions, we show that once a realistic ISM is resolved, the dominant process is actually gas fragmentation into massive and dense clouds and rapid star formation therein. As a consequence, star formation is more efficient by a factor of up to 10 and is also somewhat more extended, while the gas density probability distribution function (PDF) rapidly evolves towards very high densities. We thus propose that the actual mechanism of starburst triggering in galaxy collisions can only be captured at high spatial resolution and when the cooling of gas is modeled down to less than 10^3 K. Not only does our model reproduce the properties of the Antennae system, but it also explains the ``starburst mode revealed recently in high-redshift mergers compared to quiescent disks.
This lecture reviews the fundamental physical processes involved in star formation in galaxy interactions and mergers. Interactions and mergers often drive intense starbursts, but the link between interstellar gas physics, large scale interactions, and active star formation is complex and not fully understood yet. Two processes can drive starbursts: radial inflows of gas can fuel nuclear starbursts, triggered gas turbulence and fragmentation can drive more extended starbursts in massive star clusters with high fractions of dense gas. Both modes are certainly required to account for the observed properties of starbursting mergers. A particular consequence is that star formation scaling laws are not universal, but vary from quiescent disks to starbursting mergers. High-resolution hydrodynamic simulations are used to illustrate the lectures.
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