ORCID Profile
0000-0002-1871-4154
Current Organisation
Trent University
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Publisher: Oxford University Press (OUP)
Date: 08-10-2020
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 erse s les of interacting systems – coupled with multiwavelength c aigns aimed to capture their internal ISM structure.
Publisher: American Astronomical Society
Date: 11-2022
Abstract: Observations and simulations of interacting galaxies and mergers in the local universe have shown that interactions can significantly enhance the star formation rates (SFRs) and fueling of active galactic nuclei (AGN). However, at higher redshift, some simulations suggest that the level of star formation enhancement induced by interactions is lower due to the higher gas fractions and already increased SFRs in these galaxies. To test this, we measure the SFR enhancement in a total of 2351 (1327) massive ( M * 10 10 M ⊙ ) major (1 M 1 / M 2 4) spectroscopic galaxy pairs at 0.5 z 3.0 with Δ V 5000 km s −1 (1000 km s −1 ) and projected separation kpc selected from the extensive spectroscopic coverage in the COSMOS and CANDELS fields. We find that the highest level of SFR enhancement is a factor of 1.23 − 0.09 + 0.08 in the closest projected separation bin ( kpc) relative to a stellar mass-, redshift-, and environment-matched control s le of isolated galaxies. We find that the level of SFR enhancement is a factor of ∼1.5 higher at 0.5 z 1 than at 1 z 3 in the closest projected separation bin. Among a s le of visually identified mergers, we find an enhancement of a factor of 1.86 − 0.18 + 0.29 (∼3 σ ) for coalesced systems. For this visually identified s le, we see a clear trend of increased SFR enhancement with decreasing projected separation (2.40 − 0.37 + 0.62 versus 1.58 − 0.20 + 0.29 for 0.5 z 1.6 and 1.6 z 3.0, respectively). The SFR enhancements seen in our interactions and mergers are all lower than the level seen in local s les at the same separation, suggesting that the level of interaction-induced star formation evolves significantly over this time period.
Publisher: Oxford University Press (OUP)
Date: 24-12-2021
Abstract: One of the central challenges to establishing the role of mergers in galaxy evolution is the selection of pure and complete merger s les in observations. In particular, while large and reasonably pure interacting galaxy pair s les can be obtained with relative ease via spectroscopic criteria, automated selection of post-coalescence merger remnants is restricted to the physical characteristics of remnants alone. Furthermore, such selection has predominantly focused on imaging data – whereas kinematic data may offer a complimentary basis for identifying merger remnants. Therefore, we examine the theoretical utility of both the morphological and kinematic features of merger remnants in distinguishing galaxy merger remnants from other galaxies. Deep classification models are calibrated and evaluated using idealized synthetic images and line-of-sight stellar velocity maps of a heterogeneous population of galaxies and merger remnants from the TNG100 cosmological hydrodynamical simulation. We show that even idealized stellar kinematic data have limited utility compared to imaging and underperforms by $2.1 \\pm 0.5 {{\\ \\rm per\\ cent}}$ in completeness and $4.7 \\pm 0.4 {{\\ \\rm per\\ cent}}$ in purity for our fiducial model architecture. Combining imaging and stellar kinematics offers a small boost in completeness (by $1.8 \\pm 0.4 {{\\ \\rm per\\ cent}}$, compared to $92.7 \\pm 0.2 {{\\ \\rm per\\ cent}}$ from imaging alone) but no change in purity ($0.1\\pm 0.3 {{\\ \\rm per\\ cent}}$ improvement compared to $92.7 \\pm 0.2 {{\\ \\rm per\\ cent}}$, evaluated with equal numbers of merger remnant and non-remnant control galaxies). Classification accuracy of all models is particularly sensitive to physical companions at separations ≲ 40 kpc and to time-since-coalescence. Taken together, our results show that the stellar kinematic data have little to offer in compliment to imaging for merger remnant identification in a heterogeneous galaxy population.
Publisher: Oxford University Press (OUP)
Date: 02-06-2022
Abstract: The importance of the post-merger epoch in galaxy evolution has been well documented, but post-mergers are notoriously difficult to identify. While the features induced by mergers can sometimes be distinctive, they are frequently missed by visual inspection. In addition, visual classification efforts are highly inefficient because of the inherent rarity of post-mergers (~1 per cent in the low-redshift Universe), and non-parametric statistical merger selection methods do not account for the ersity of post-mergers or the environments in which they appear. To address these issues, we deploy a convolutional neural network (CNN) that has been trained and evaluated on realistic mock observations of simulated galaxies from the IllustrisTNG simulations, to galaxy images from the Canada France Imaging Survey, which is part of the Ultraviolet Near Infrared Optical Northern Survey. We present the characteristics of the galaxies with the highest CNN-predicted post-merger certainties, as well as a visually confirmed subset of 699 post-mergers. We find that post-mergers with high CNN merger probabilities [p(x) & 0.8] have an average star formation rate that is 0.1 dex higher than a mass- and redshift-matched control s le. The SFR enhancement is even greater in the visually confirmed post-merger s le, a factor of 2 higher than the control s le.
No related grants have been discovered for David Patton.