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Register a new userA fitting formula for the merger timescale of galaxies in hierarchical clustering
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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.
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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.
The alpha element to iron peak element ratio, for example [Mg/Fe], is a commonly applied indicator of the galaxy star formation timescale (SFT) since the two groups of elements are mainly produced by different types of supernovae that explode over different timescales. However, it is insufficient to consider only [Mg/Fe] when estimating the SFT. The [Mg/Fe] yield of a stellar population depends on its metallicity. Therefore, it is possible for galaxies with different SFTs and at the same time different total metallicity to have the same [Mg/Fe]. This effect has not been properly taken into consideration in previous studies. In this study, we assume the galaxy-wide stellar initial mass function (gwIMF) to be canonical and invariant. We demonstrate that our computation code reproduces the SFT estimations of previous studies where only the [Mg/Fe] observational constraint is applied. We then demonstrate that once both metallicity and [Mg/Fe] observations are considered, a more severe downsizing relation is required. This means that either low-mass ellipticals have longer SFTs (> 4 Gyr for galaxies with mass below $10^{10}$ M$_odot$) or massive ellipticals have shorter SFTs ($approx 200$ Myr for galaxies more massive than $10^{11}$ M$_odot$) than previously thought. This modification increases the difficulty in reconciling such SFTs with other observational constraints. We show that applying different stellar yield modifications does not relieve this formation timescale problem. The quite unrealistically short SFT required by [Mg/Fe] and total metallicity would be prolonged if a variable stellar gwIMF were assumed. Since a systematically varying gwIMF has been suggested by various observations this could present a natural solution to this problem.
Accurate estimation of the merger timescale of galaxy clusters is important to understand the cluster merger process and further the formation and evolution of the large-scale structure of the universe. In this paper, we explore a baryonic effect on the merger timescale of galaxy clusters by using hydrodynamical simulations. We find that the baryons play an important role in accelerating the merger process. The merger timescale decreases with increasing the gas fraction of galaxy clusters. For example, the merger timescale is shortened by a factor of up to 3 for merging clusters with gas fractions 0.15, compared with the timescale obtained with zero gas fractions. The baryonic effect is significant for a wide range of merger parameters and especially more significant for nearly head-on mergers and high merging velocities. The baryonic effect on the merger timescale of galaxy clusters is expected to have impacts on the structure formation in the universe, such as the cluster mass function and massive substructures in galaxy clusters, and a bias of no-gas may exist in the results obtained from the dark matter-only cosmological simulations.
Recently, Hierarchical Clustering (HC) has been considered through the lens of optimization. In particular, two maximization objectives have been defined. Moseley and Wang defined the emph{Revenue} objective to handle similarity information given by a weighted graph on the data points (w.l.o.g., $[0,1]$ weights), while Cohen-Addad et al. defined the emph{Dissimilarity} objective to handle dissimilarity information. In this paper, we prove structural lemmas for both objectives allowing us to convert any HC tree to a tree with constant number of internal nodes while incurring an arbitrarily small loss in each objective. Although the best-known approximations are 0.585 and 0.667 respectively, using our lemmas we obtain approximations arbitrarily close to 1, if not all weights are small (i.e., there exist constants $epsilon, delta$ such that the fraction of weights smaller than $delta$, is at most $1 - epsilon$); such instances encompass many metric-based similarity instances, thereby improving upon prior work. Finally, we introduce Hierarchical Correlation Clustering (HCC) to handle instances that contain similarity and dissimilarity information simultaneously. For HCC, we provide an approximation of 0.4767 and for complementary similarity/dissimilarity weights (analogous to $+/-$ correlation clustering), we again present nearly-optimal approximations.
Recent works on Hierarchical Clustering (HC), a well-studied problem in exploratory data analysis, have focused on optimizing various objective functions for this problem under arbitrary similarity measures. In this paper we take the first step and give novel scalable algorithms for this problem tailored to Euclidean data in R^d and under vector-based similarity measures, a prevalent model in several typical machine learning applications. We focus primarily on the popular Gaussian kernel and other related measures, presenting our results through the lens of the objective introduced recently by Moseley and Wang [2017]. We show that the approximation factor in Moseley and Wang [2017] can be improved for Euclidean data. We further demonstrate both theoretically and experimentally that our algorithms scale to very high dimension d, while outperforming average-linkage and showing competitive results against other less scalable approaches.
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