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Register a new userExtracting Galaxy Merger Timescales II: A new fitting formula
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Predicting the merger timescale ($tau_{rm merge}$) of merging dark matter halos, based on their orbital parameters and the structural properties of their hosts, is a fundamental problem in gravitational dynamics that has important consequences for our understanding of cosmological structure formation and galaxy formation. Previous models predicting $tau_{rm merge}$ have shown varying degrees of success when compared to the results of cosmological $N$-body simulations. We build on this previous work and propose a new model for $tau_{rm merge}$ that draws on insights derived from these simulations. We find that published predictions can provide reasonable estimates for $tau_{rm merge}$ based on orbital properties at infall, but tend to underpredict $tau_{rm merge}$ inside the host virial radius ($R_{200}$) because tidal stripping is neglected, and overpredict it outside $R_{200}$ because the host mass is underestimated. Furthermore, we find that models that account for orbital angular momentum via the circular radius $R_{rm circ}$ underpredict (overpredict) $tau_{rm merge}$ for bound (unbound) systems. By fitting for the dependence of $tau_{rm merge}$ on various orbital and host halo properties,we derive an improved model for $tau_{rm merge}$ that can be applied to a merging halo at any point in its orbit. Finally, we discuss briefly the implications of our new model for $tau_{rm merge}$ for semi-analytical galaxy formation modelling.
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Hierarchical models of structure formation predict that dark matter halo assembly histories are characterised by episodic mergers and interactions with other haloes. An accurate description of this process will provide insights into the dynamical evolution of haloes and the galaxies that reside in them. Using large cosmological N-body simulations, we characterise halo orbits to study the interactions between substructure haloes and their hosts, and how different evolutionary histories map to different classes of orbits. We use two new software tools - WhereWolf, which uses halo group catalogues and merger trees to ensure that haloes are tracked accurately in dense environments, and OrbWeaver, which quantifies each halos orbital parameters. We demonstrate how WhereWolf improves the accuracy of halo merger trees, and we use OrbWeaver to quantify orbits of haloes. We assess how well analytical prescriptions for the merger timescale from the literature compare to measured merger timescales from our simulations and find that existing prescriptions perform well, provided the ratio of substructure-to-host mass is not too small. In the limit of small substructure-to-host mass ratio, we find that the prescriptions can overestimate the merger timescales substantially, such that haloes are predicted to survive well beyond the end of the simulation. This work highlights the need for a revised analytical prescription for the merger timescale that more accurately accounts for processes such as catastrophic tidal disruption.
The Sersic law (SL) offers a versatile functional form for the structural characterization of galaxies near and far. Whereas applying it to galaxies with a genuine SL luminosity distribution yields a robust determination of the Sersic exponent eta and effective surface brightness $mu_{rm eff}$, this is not necessarily the case for galaxies whose surface brightness profiles (SBPs) appreciably deviate from the SL (eg, early-type galaxies with a depleted core and nucleated dwarf ellipticals, or most late-type galaxies-LTGs). In this general case of imperfect SL profiles, the best-fitting solution may significantly depend on the radius (or surface brightness) interval fit and corrections for point spread function (PSF) convolution effects. Such uncertainties may then affect, in a non-easily predictable manner, automated structural studies of galaxies. We present a fitting concept (iFIT) that permits a robust determination of the equivalent SL model for the general case of galaxies with imperfect SL profiles. iFIT has been extensively tested on synthetic data with a Sersic index 0.3<${eta}$<4.2 and an effective radius 1<$rm{R}_{eff}$ (arcs)<20. Applied to non PSF-convolved data, iFIT can infer the Sersic exponent eta with an absolute error of <0.2 even for shallow SBPs. As for PSF-degraded data, iFIT can recover the input SL model parameters with a satisfactorily accuracy almost over the entire considered parameter space as long as FWHM(PSF)<$rm{R}_{eff}$. Tests indicate that iFIT shows little sensitivity on PSF corrections and the SBP limiting surface brightness, and that subtraction of the best-fitting SL model in two different bands yields a good match to the observed radial color profile. The publicly available iFIT offers an efficient tool for the non-supervised structural characterization of large galaxy samples, as those expected to become available with Euclid and LSST.
Merging is potentially the dominate process in galaxy formation, yet there is still debate about its history over cosmic time. To address this we classify major mergers and measure galaxy merger rates up to z $sim$ 3 in all five CANDELS fields (UDS, EGS, GOODS-S, GOODS-N, COSMOS) using deep learning convolutional neural networks (CNNs) trained with simulated galaxies from the IllustrisTNG cosmological simulation. The deep learning architecture used is objectively selected by a Bayesian Optmization process over the range of possible hyperparameters. We show that our model can achieve 90% accuracy when classifying mergers from the simulation, and has the additional feature of separating mergers before the infall of stellar masses from post mergers. We compare our machine learning classifications on CANDELS galaxies and compare with visual merger classifications from Kartaltepe et al. (2015), and show that they are broadly consistent. We finish by demonstrating that our model is capable of measuring galaxy merger rates, $mathcal{R}$, that are consistent with results found for CANDELS galaxies using close pairs statistics, with $mathcal{R}(z) = 0.02 pm 0.004 times (1 +z) ^ {2.76 pm 0.21}$. This is the first general agreement between major mergers measured using pairs and structure at z < 3.
We study galaxy mergers using a high-resolution cosmological hydro/N-body simulation with star formation, and compare the measured merger timescales with theoretical predictions based on the Chandrasekhar formula. In contrast to Navarro et al., our numerical results indicate, that the commonly used equation for the merger timescale given by Lacey and Cole, systematically underestimates the merger timescales for minor mergers and overestimates those for major mergers. This behavior is partly explained by the poor performance of their expression for the Coulomb logarithm, ln (m_pri/m_sat). The two alternative forms ln (1+m_pri/m_sat) and 1/2ln [1+(m_pri/m_sat)^2] for the Coulomb logarithm can account for the mass dependence of merger timescale successfully, but both of them underestimate the merger time scale by a factor 2. Since ln (1+m_pri/m_sat) represents the mass dependence slightly better we adopt this expression for the Coulomb logarithm. Furthermore, we find that the dependence of the merger timescale on the circularity parameter epsilon is much weaker than the widely adopted power-law epsilon^{0.78}, whereas 0.94*{epsilon}^{0.60}+0.60 provides a good match to the data. Based on these findings, we present an accurate and convenient fitting formula for the merger timescale of galaxies in cold dark matter models.
The cosmic web plays a major role in the formation and evolution of galaxies and defines, to a large extent, their properties. However, the relation between galaxies and environment is still not well understood. Here we present a machine learning approach to study imprints of environmental effects on the mass assembly of haloes. We present a galaxy-LSS machine learning classifier based on galaxy properties sensitive to the environment. We then use the classifier to assess the relevance of each property. Correlations between galaxy properties and their cosmic environment can be used to predict galaxy membership to void/wall or filament/cluster with an accuracy of $93%$. Our study unveils environmental information encoded in properties of haloes not normally considered directly dependent on the cosmic environment such as merger history and complexity. Understanding the physical mechanism by which the cosmic web is imprinted in a halo can lead to significant improvements in galaxy formation models. This is accomplished by extracting features from galaxy properties and merger trees, computing feature scores for each feature and then applying support vector machine to different feature sets. To this end, we have discovered that the shape and depth of the merger tree, formation time and density of the galaxy are strongly associated with the cosmic environment. We describe a significant improvement in the original classification algorithm by performing LU decomposition of the distance matrix computed by the feature vectors and then using the output of the decomposition as input vectors for support vector machine.
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