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Register a new userThe Hubble Sequence at $zsim0$ in the IllustrisTNG simulation with deep learning
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Added by
Marc Huertas-Company
Publication date
2019
fields
Physics
and research's language is
English
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We analyze the optical morphologies of galaxies in the IllustrisTNG simulation at $zsim0$ with a Convolutional Neural Network trained on visual morphologies in the Sloan Digital Sky Survey. We generate mock SDSS images of a mass complete sample of $sim12,000$ galaxies in the simulation using the radiative transfer code SKIRT and include PSF and noise to match the SDSS r-band properties. The images are then processed through the exact same neural network used to estimate SDSS morphologies to classify simulated galaxies in four morphological classes (E, S0/a, Sab, Scd). The CNN model finds that $sim95%$ of the simulated galaxies fall in one the four main classes with high confidence. The mass-size relations of the simulated galaxies divided by morphological type also reproduce well the slope and the normalization of observed relations which confirms the realism of optical morphologies in the TNG suite. However, the Stellar Mass Functions decomposed into different morphologies still show significant discrepancies with observations both at the low and high mass end. We find that the high mass end of the SMF is dominated in TNG by massive disk galaxies while early-type galaxies dominate in the observations according to the CNN classifications. The present work highlights the importance of detailed comparisons between observations and simulations in comparable conditions.
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We study the structural evolution of isolated star-forming galaxies in the Illustris TNG100-1 hydrodynamical simulation, with a focus on investigating the growth of the central core density within 2 kpc ($Sigma_{*,2kpc}$) in relation to total stellar mass ($M_*$) at z < 0.5. First, we show that several observational trends in the $Sigma_{*,2kpc}$-$M_*$ plane are qualitatively reproduced in IllustrisTNG, including the distributions of AGN, star forming galaxies, quiescent galaxies, and radial profiles of stellar age, sSFR, and metallicity. We find that galaxies with dense cores evolve parallel to the $Sigma_{*,2kpc}$-$M_*$ relation, while galaxies with diffuse cores evolve along shallower trajectories. We investigate possible drivers of rapid growth in $Sigma_{*,2kpc}$ compared to $M_*$. Both the current sSFR gradient and the BH accretion rate are indicators of past core growth, but are not predictors of future core growth. Major mergers (although rare in our sample; $sim$10%) cause steeper core growth, except for high mass ($M_*$ >$sim$ $10^{10} M_{odot}$) mergers, which are mostly dry. Disc instabilities, as measured by the fraction of mass with Toomre Q < 2, are not predictive of rapid core growth. Instead, rapid core growth results in more stable discs. The cumulative black hole feedback history sets the maximum rate of core growth, preventing rapid growth in high-mass galaxies ($M_*$ >$sim$ $10^{9.5} M_{odot}$). For massive galaxies the total specific angular momentum of accreting gas is the most important predictor of future core growth. Our results suggest that the angular momentum of accreting gas controls the slope, width and zero-point evolution of the $Sigma_{*,2kpc}$-$M_*$ relation.
The Hubble sequence is a common classification scheme for the structure of galaxies. Despite the tremendous usefulness of this diagnostic, we still do not fully understand when, where, and how this morphological ordering was put in place. Here, we investigate the morphological evolution of a sample of 22 high redshift ($zgeq3$) galaxies extracted from the Argo simulation. Argo is a cosmological zoom-in simulation of a group-sized halo and its environment. It adopts the same high resolution ($sim10^4$ M$_odot$, $sim100$ pc) and sub-grid physical model that was used in the Eris simulation but probes a sub-volume almost ten times bigger with as many as 45 million gas and star particles in the zoom-in region. Argo follows the early assembly of galaxies with a broad range of stellar masses ($log M_{star}/{rm M}_{odot}sim8-11$ at $zsimeq3$), while resolving properly their structural properties. We recover a diversity of morphologies, including late-type/irregular disc galaxies with flat rotation curves, spheroid dominated early-type discs, and a massive elliptical galaxy, already established at $zsim3$. We identify major mergers as the main trigger for the formation of bulges and the steepening of the circular velocity curves. Minor mergers and non-axisymmetric perturbations (stellar bars) drive the bulge growth in some cases. The specific angular momenta of the simulated disc components fairly match the values inferred from nearby galaxies of similar $M_{star}$ once the expected redshift evolution of disc sizes is accounted for. We conclude that morphological transformations of high redshift galaxies of intermediate mass are likely triggered by processes similar to those at low redshift and result in an early build-up of the Hubble sequence.
We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantised variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that includes: (1) consideration of the clustering performance simultaneously when learning features from images; (2) allowing for various distance thresholds within the HC algorithm; (3) using the galaxy orientation to determine the number of clusters. This setup provides 27 clusters created with this unsupervised learning which we show are well separated based on galaxy shape and structure (e.g., Sersic index, concentration, asymmetry, Gini coefficient). These resulting clusters also correlate well with physical properties such as the colour-magnitude diagram, and span the range of scaling-relations such as mass vs. size amongst the different machine-defined clusters. When we merge these multiple clusters into two large preliminary clusters to provide a binary classification, an accuracy of $sim87%$ is reached using an imbalanced dataset, matching real galaxy distributions, which includes 22.7% early-type galaxies and 77.3% late-type galaxies. Comparing the given clusters with classic Hubble types (ellipticals, lenticulars, early spirals, late spirals, and irregulars), we show that there is an intrinsic vagueness in visual classification systems, in particular galaxies with transitional features such as lenticulars and early spirals. Based on this, the main result in this work is not how well our unsupervised method matches visual classifications and physical properties, but that the method provides an independent classification that may be more physically meaningful than any visually based ones.
We investigate the formation history of massive disk galaxies in hydro-dynamical simulation--the IllustrisTNG, to study why massive disk galaxies survive through cosmic time. 83 galaxies in the simulation are selected with M$_{*,z=0}$ $>8times10^{10}$ M$_odot$ and kinematic bulge-to-total ratio less than $0.3$. We find that 8.4 percent of these massive disk galaxies have quiet merger histories and preserve disk morphology since formed. 54.2 percent have a significant increase in bulge components in history, then become disks again till present time. The rest 37.3 percent experience prominent mergers but survive to remain disky. While mergers and even major mergers do not always turn disk galaxies into ellipticals, we study the relations between various properties of mergers and the morphology of merger remnants. We find a strong dependence of remnant morphology on the orbit type of major mergers. Specifically, major mergers with a spiral-in falling orbit mostly lead to disk-dominant remnants, and major mergers of head-on galaxy-galaxy collision mostly form ellipticals. This dependence of remnant morphology on orbit type is much stronger than the dependence on cold gas fraction or orbital configuration of merger system as previously studied.
Starburst galaxies are often found to be the result of galaxy mergers. As a result, galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlation between stellar mass and star formation rate. Here, we aim to test this claim. Deep learning techniques are applied to images from the Sloan Digital Sky Survey to provide visual-like classifications for over 340 000 objects between redshifts of 0.005 and 0.1. The aim of this classification is to split the galaxy population into merger and non-merger systems and we are currently achieving an accuracy of 91.5%. Stellar masses and star formation rates are also estimated using panchromatic data for the entire galaxy population. With these preliminary data, the mergers are placed onto the full galaxy main sequence, where we find that merging systems lie across the entire star formation rate - stellar mass plane.
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