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Galaxy mergers are crucial to understanding galaxy evolution, therefore we must determine their observational signatures to select them from large IFU galaxy samples such as MUSE and SAMI. We employ 24 high-resolution idealized hydrodynamical galaxy merger simulations based on the ‘Feedback In Realistic Environment’ (FIRE-2) model to determine the observability of mergers to various configurations and stages using synthetic images and velocity maps. Our mergers cover a range of orbital configurations at fixed 1:2.5 stellar mass ratio for two gas rich spirals at low redshift. Morphological and kinematic asymmetries are computed for synthetic images and velocity maps spanning each interaction. We divide the interaction sequence into three: (1) the pair phase; (2) the merging phase; and (3) the post-coalescence phase. We correctly identify mergers between first pericentre passage and 500 Myr after coalescence using kinematic asymmetry with 66 per cent completeness, depending upon merger phase and the field of view of the observation. We detect fewer mergers in the pair phase (40 per cent) and many more in the merging and post-coalescence phases (97 per cent). We find that merger detectability decreases with field of view, except in retrograde mergers, where centrally concentrated asymmetric kinematic features enhances their detectability. Using a cut-off derived from a combination of photometric and kinematic asymmetry, we increase these detections to 89 per cent overall, 79 per cent in pairs, and close to 100 per cent in the merging and post-coalescent phases. By using this combined asymmetry cut-off we mitigate some of the effects caused by smaller fields of view subtended by massively multiplexed integral field spectroscopy programmes.

 

Recent observations have revealed remarkable insights into the gas reservoir in the circumgalactic medium (CGM) of galaxy haloes. In this paper, we characterize the gas in the vicinity of Milky Way and Andromeda analogues in the hestia (High resolution Environmental Simulations of The Immediate Area) suite of constrained Local Group (LG) simulations. The hestia suite comprise of a set of three high-resolution arepo-based simulations of the LG, run using the Auriga galaxy formation model. For this paper, we focus only on the z = 0 simulation data sets and generate mock skymaps along with a power spectrum analysis to show that the distributions of ions tracing low-temperature gas (H i and Si iii) are more clumpy in comparison to warmer gas tracers (O vi, O vii, and O viii). We compare to the spectroscopic CGM observations of M31 and low-redshift galaxies. hestia underproduces the column densities of the M31 observations, but the simulations are consistent with the observations of low-redshift galaxies. A possible explanation for these findings is that the spectroscopic observations of M31 are contaminated by gas residing in the CGM of the Milky Way.

 

ABSTRACT We investigate the spatial structure and evolution of star formation and the interstellar medium (ISM) in interacting galaxies. We use an extensive suite of parsec-scale galaxy-merger simulations (stellar mass ratio = 2.5:1), which employs the ‘Feedback In Realistic Environments-2’ model (fire-2). This framework resolves star formation, feedback processes, and the multiphase structure of the ISM. We focus on the galaxy-pair stages of interaction. We find that close encounters substantially augment cool (H i) and cold-dense (H2) gas budgets, elevating the formation of new stars as a result. This enhancement is centrally concentrated for the secondary galaxy, and more radially extended for the primary. This behaviour is weakly dependent on orbital geometry. We also find that galaxies with elevated global star formation rate (SFR) experience intense nuclear SFR enhancement, driven by high levels of either star formation efficiency (SFE) or available cold-dense gas fuel. Galaxies with suppressed global SFR also contain a nuclear cold-dense gas reservoir, but low SFE levels diminish SFR in the central region. Concretely, in the majority of cases, SFR enhancement in the central kiloparsec is fuel-driven (55 per cent for the secondary, 71 per cent for the primary) – while central SFR suppression is efficiency-driven (91 per cent for the secondary, 97 per cent for the primary). Our numerical predictions underscore the need of substantially larger, and/or merger-dedicated, spatially resolved galaxy surveys – capable of examining vast and diverse samples of interacting systems – coupled with multiwavelength campaigns aimed to capture their internal ISM structure. 

ABSTRACT The correlation between galaxies’ integrated stellar masses and star formation rates (the ‘star formation main sequence’, SFMS) is a well-established scaling relation. Recently, surveys have found a relationship between the star formation rate (SFR) and stellar mass surface densities on kpc and sub-kpc scales (the ‘resolved SFMS’, rSFMS). In this work, we demonstrate that the rSFMS emerges naturally in Feedback In Realistic Environments 2 (FIRE-2) zoom-in simulations of Milky Way-mass galaxies. We make SFR and stellar mass maps of the simulated galaxies at a variety of spatial resolutions and star formation averaging time-scales and fit the rSFMS using multiple methods from the literature. While the absolute value of the SFMS slope (αMS) depends on the fitting method, the slope is steeper for longer star formation time-scales and lower spatial resolutions regardless of the fitting method employed. We present a toy model that quantitatively captures the dependence of the simulated galaxies’ αMS on spatial resolution and use it to illustrate how this dependence can be used to constrain the characteristic mass of star-forming clumps. 

Machine learning is becoming a popular tool to quantify galaxy morphologies and identify mergers. However, this technique relies on using an appropriate set of training data to be successful. By combining hydrodynamical simulations, synthetic observations, and convolutional neural networks (CNNs), we quantitatively assess how realistic simulated galaxy images must be in order to reliably classify mergers. Specifically, we compare the performance of CNNs trained with two types of galaxy images, stellar maps and dust-inclusive radiatively transferred images, each with three levels of observational realism: (1) no observational effects (idealized images), (2) realistic sky and point spread function (semirealistic images), and (3) insertion into a real sky image (fully realistic images). We find that networks trained on either idealized or semireal images have poor performance when applied to survey-realistic images. In contrast, networks trained on fully realistic images achieve 87.1 per cent classification performance. Importantly, the level of realism in the training images is much more important than whether the images included radiative transfer, or simply used the stellar maps ($87.1{{\ \rm per\ cent}}$ compared to $79.6{{\ \rm per\ cent}}$ accuracy, respectively). Therefore, one can avoid the large computational and storage cost of running radiative transfer with a relatively modest compromise in classification performance. Making photometry-based networks insensitive to colour incurs a very mild penalty to performance with survey-realistic data ($86.0{{\ \rm per\ cent}}$ with r-only compared to $87.1{{\ \rm per\ cent}}$ with gri). This result demonstrates that while colour can be exploited by colour-sensitive networks, it is not necessary to achieve high accuracy and so can be avoided if desired. We provide the public release of our statistical observational realism suite, RealSim, as a companion to this paper.