Bars formed in galaxy merging and their classification with deep learning

Abstract

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.

Spatially resolved star formation and fuelling in galaxy interactions

“…Patton et al (2020) confirm this effect in cosmological simulations, and demonstrate that close encounters affect galaxy pairs out to separations of ∼280 kpc in 3D space. Each interaction and flyby (Moreno 2012;Sinha & Holley-Bockelmann 2012;L'Huillier, Park & Kim 2015;) is capable of inciting bar formation (Łokas et al 2016;Pettitt & Wadsley 2018;Łokas 2019;Cavanagh & Bekki 2020) and promoting bulge mass growth (Just et al 2010;Bekki & Couch 2011). But more importantly, the cumulative effect of multiple -frequently occurring and long-lived -galaxy encounters may ultimately stimulate the transformation of spirals into lenticulars in dense environments (Moore et al 1996;Boselli & Gavazzi 2006;Cappellari 2013;Joshi et al 2020).…”

Merger-induced galaxy transformations in the ARTEMIS simulations

“…While there is overwhelming evidence that the centre of the Milky Way hosts a bar (e.g., Queiroz et al 2020;Anders et al 2019), its origins are still poorly understood. Studies using N-body simulations (e.g., Cavanagh & Bekki 2020;Peirani et al 2009) have indicated that bar formation in disc galaxies can be triggered by minor mergers, with mass ratios of 0.1 being most conducive (Cavanagh & Bekki 2020). Furthermore, observations of the Milky Way's bulge (Surot et al 2019) have found that its stars are largely over 7.5 Gyr old, roughly corresponding to the time of the GS merger.…”

Morphological classification of galaxies with deep learning: comparing 3-way and 4-way CNNs

“…Each architecture is built using Keras (Chollet et al 2015), a high-level machine learning interface. The first CNN, referred to as C1, is based on our previous work (Cavanagh & Bekki 2020), and was originally designed for the binary classification of 50 × 50 imagery. This architecture consists of a single block of convolution and pooling layers (2 Conv2D plus a MaxPooling), a penultimate Dense layer with 256 nodes, and the output Dense layer which contains either 3 or 4 nodes for 3-way or 4-way classification.…”