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Register a new userEMERGE: Empirical predictions of galaxy merger rates since $zsim6$
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We explore the galaxy-galaxy merger rate with the empirical model for galaxy formation, Emerge. On average, we find that between $2$ per cent and $20$ per cent of massive galaxies ($log_{10}(m_{*}/M_{odot}) geq 10.3$) will experience a major merger per Gyr. Our model predicts galaxy merger rates that do not scale as a power-law with redshift when selected by descendant stellar mass, and exhibit a clear stellar mass and mass-ratio dependence. Specifically, major mergers are more frequent at high masses and at low redshift. We show mergers are significant for the stellar mass growth of galaxies $log_{10}(m_{*}/M_{odot}) gtrsim 11.0$. For the most massive galaxies major mergers dominate the accreted mass fraction, contributing as much as $90$ per cent of the total accreted stellar mass. We reinforce that these phenomena are a direct result of the stellar-to-halo mass relation, which results in massive galaxies having a higher likelihood of experiencing major mergers than low mass galaxies. Our model produces a galaxy pair fraction consistent with recent observations, exhibiting a form best described by a power-law exponential function. Translating these pair fractions into merger rates results in an inaccurate prediction compared to the model intrinsic values when using published observation timescales. We find the pair fraction can be well mapped to the intrinsic merger rate by adopting an observation timescale that decreases linearly with redshift as $T_{mathrm{obs}} = -0.36(1+z)+2.39$ [Gyr], assuming all observed pairs merge by $z=0$.
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We present EMERGE, an Empirical ModEl for the foRmation of GalaxiEs, describing the evolution of individual galaxies in large volumes from $zsim10$ to the present day. We assign a star formation rate to each dark matter halo based on its growth rate, which specifies how much baryonic material becomes available, and the instantaneous baryon conversion efficiency, which determines how efficiently this material is converted to stars, thereby capturing the baryonic physics. Satellites are quenched following the delayed-then-rapid model, and they are tidally disrupted once their subhalo has lost a significant fraction of its mass. The model is constrained with observed data extending out to high redshift. The empirical relations are very flexible, and the model complexity is increased only if required by the data, assessed by several model selection statistics. We find that for the same final halo mass galaxies can have very different star formation histories. Nevertheless, the average star formation and accretion rates are in good agreement with models following an abundance matching strategy. Galaxies that are quenched at $z=0$ typically have a higher peak star formation rate compared to their star-forming counterparts. The accretion of stars can dominate the total mass of massive galaxies, but is insignificant for low-mass systems, independent of star-formation activity. EMERGE predicts stellar-to-halo mass ratios for individual galaxies and introduces scatter self-consistently. We find that at fixed halo mass, passive galaxies have a higher stellar mass on average. The intra-cluster-mass in massive haloes can be up to 8 times larger than the mass of the central galaxy. Clustering for star-forming and quenched galaxies is in good agreement with observational constraints, indicating a realistic assignment of galaxies to haloes.
The concentration parameter is a key characteristic of a dark matter halo that conveniently connects the halos present-day structure with its assembly history. Using Dark Sky, a suite of cosmological $N$-body simulations, we investigate how halo concentration evolves with time and emerges from the mass assembly history. We also explore the origin of the scatter in the relation between concentration and assembly history. We show that the evolution of halo concentration has two primary modes: (1) smooth increase due to pseudo-evolution; and (2) intense responses to physical merger events. Merger events induce lasting and substantial changes in halo structures, and we observe a universal response in the concentration parameter. We argue that merger events are a major contributor to the uncertainty in halo concentration at fixed halo mass and formation time. In fact, even haloes that are typically classified as having quiescent formation histories experience multiple minor mergers. These minor mergers drive small deviations from pseudo-evolution, which cause fluctuations in the concentration parameters and result in effectively irreducible scatter in the relation between concentration and assembly history. Hence, caution should be taken when using present-day halo concentration parameter as a proxy for the halo assembly history, especially if the recent merger history is unknown.
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 observa
tions 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 (semi-realistic images), (3) insertion into a real sky image (fully realistic images). We find that networks trained on either idealized or semi-real images have poor performance when applied to survey-realistic images. In contrast, networks trained on fully realistic images achieve 87.1% 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% compared to 79.6% 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% with r-only compared to 87.1% 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.
We present results of a statistical study of the cosmic evolution of the mass dependent major-merger rate since z=1. A stellar mass limited sample of close major-merger pairs (the CPAIR sample) was selected from the archive of the COSMOS survey. Pair fractions at different redshifts derived using the CPAIR sample and a local K-band selected pair sample show no significant variations with stellar mass. The pair fraction exhibits moderately strong cosmic evolution, with the best-fitting evolutionary index m=2.2+-0.2. The best-fitting function for the merger rate implies that galaxies with stellar mass between 1E+10 -- 3E+11 M_sun have undergone 0.5 -- 1.5 major-mergers since z=1. Our results show that, for massive galaxies at z<1, major mergers involving star forming galaxies (i.e. wet and mixed mergers) can account for the formation of both ellipticals and red quiescent galaxies (RQGs). On the other hand, major mergers cannot be responsible for the formation of most low mass ellipticals and RQGs. Our quantitative estimates indicate that major mergers have significant impact on the stellar mass assembly of the most massive galaxies, but for less massive galaxies the stellar mass assembly is dominated by the star formation. Comparison with the mass dependent (U)LIRG rates suggests that the frequency of major-merger events is comparable to or higher than that of (U)LIRGs.
We provide, for the first time, robust observational constraints on the galaxy major merger fraction up to $zapprox 6$ using spectroscopic close pair counts. Deep Multi Unit Spectroscopic Explorer (MUSE) observations in the Hubble Ultra Deep Field (HUDF) and Hubble Deep Field South (HDF-S) are used to identify 113 secure close pairs of galaxies among a parent sample of 1801 galaxies spread over a large redshift range ($0.2<z<6$) and stellar masses ($10^7-10^{11} M_odot$), thus probing about 12 Gyr of galaxy evolution. Stellar masses are estimated from spectral energy distribution (SED) fitting over the extensive UV-to-NIR HST photometry available in these deep Hubble fields, adding Spitzer IRAC bands to better constrain masses for high-redshift ($zgeqslant 3$) galaxies. These stellar masses are used to isolate a sample of 54 major close pairs with a galaxy mass ratio limit of 1:6. Among this sample, 23 pairs are identified at high redshift ($zgeqslant 3$) through their Ly$alpha$ emission. The sample of major close pairs is divided into five redshift intervals in order to probe the evolution of the merger fraction with cosmic time. Our estimates are in very good agreement with previous close pair counts with a constant increase of the merger fraction up to $zapprox 3$ where it reaches a maximum of 20%. At higher redshift, we show that the fraction slowly decreases down to about 10% at $zapprox6$. The sample is further divided into two ranges of stellar masses using either a constant separation limit of $10^{9.5} M_odot$ or the median value of stellar mass computed in each redshift bin. Overall, the major close pair fraction for low-mass and massive galaxies follows the same trend. These new, homogeneous, and robust estimates of the major merger fraction since $zapprox6$ are in good agreement with recent predictions of cosmological numerical simulations.
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