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Register a new userSPIDER IX - Classifying Galaxy Groups according to their Velocity Distribution
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Reinado de Carvalho R
Publication date
2013
fields
Physics
and research's language is
English
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We introduce a new method to study the velocity distribution of galaxy systems, the Hellinger Distance (HD) - designed for detecting departures from a Gaussian velocity distribution. We define a relaxed galactic system as the one with unimodal velocity distribution and a normality deviation below a critical value (HD<0.05). In this work, we study the gaussian nature of the velocity distribution of the Berlind group sample, and of the FoF groups from the Millennium simulation. For the Berlind group sample (z<0.1), 67% of the systems are classified as relaxed, while for the Millennium sample we find 63% (z=0). We verify that in multimodal groups the average mass of modes in high multiplicity (N >= 20) systems are significantly larger than in low multiplicity ones (N<20), suggesting that groups experience a mass growth at an increasing virialization rate towards z=0, with larger systems accreting more massive subunits. We also investigate the connection between galaxy properties ([Fe/H], Age, eClass, g-r, R_petro and <mu_petro>) and the gaussianity of the velocity distribution of the groups. Bright galaxies (M_r <=-20.7) residing in the inner and outer regions of groups, do not show significant differences in the listed quantities regardless if the group has a Gaussian (G) or a Non-Gaussian (NG) velocity distribution. However, the situation is significantly different when we examine the faint galaxies (-20.7<M_r<=-17.9). In G groups, there is a remarkable difference between the galaxy properties of the inner and outer galaxy populations, testifying how the environment is affecting the galaxies. Instead, in NG groups there is no segregation between the properties of galaxies in the inner and outer regions, showing that the properties of these galaxies still reflect the physical processes prevailing in the environment where they were found earlier.
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We use machine learning to classify galaxies according to their HI content, based on both their optical photometry and environmental properties. The data used for our analyses are the outputs in the range $z = 0-1$ from MUFASA cosmological hydrodynamic simulation. In our previous paper, where we predicted the galaxy HI content using the same input features, HI rich galaxies were only selected for the training. In order for the predictions on real observation data to be more accurate, the classifiers built in this study will first establish if a galaxy is HI rich ($rm{log(M_{HI}/M_{*})} > -2 $) before estimating its neutral hydrogen content using the regressors developed in the first paper. We resort to various machine learning algorithms and assess their performance with various metrics such as accuracy for instance. The performance of the classifiers gets better with increasing redshift and reaches their peak performance around $z = 1$. Random Forest method, the most robust among the classifiers when considering only the mock data for both training and test in this study, reaches an accuracy above $98.6 %$ at $z = 0$ and above $99.0 %$ at $z = 1$. We test our algorithms, trained with simulation data, on classification of the galaxies in RESOLVE, ALFALFA and GASS surveys. Interestingly, SVM algorithm, the best classifier for the tests, achieves a precision, the relevant metric for the tests, above $87.60%$ and a specificity above $71.4%$ with all the tests, indicating that the classifier is capable of learning from the simulated data to classify HI rich/HI poor galaxies from the real observation data. With the advent of large HI 21 cm surveys such as the SKA, this set of classifiers, together with the regressors developed in the first paper, will be part of a pipeline, a very useful tool, which is aimed at predicting HI content of galaxies.
We analyse the dependence of the luminosity function of galaxies in groups (LF) on group dynamical state. We use the Gaussianity of the velocity distribution of galaxy members as a measurement of the dynamical equilibrium of groups identified in the SDSS Data Release 7 by Zandivarez & Martinez. We apply the Anderson-Darling goodness-of-fit test to distinguish between groups according to whether they have Gaussian or Non-Gaussian velocity distributions, i.e., whether they are relaxed or not. For these two subsamples, we compute the $^{0.1}r-$band LF as a function of group virial mass and group total luminosity. For massive groups, ${mathcal M}>5 times 10^{13} M_{odot} h^{-1}$, we find statistically significant differences between the LF of the two subsamples: the LF of groups that have Gaussian velocity distributions have a brighter characteristic absolute magnitude ($sim0.3$ mag) and a steeper faint end slope ($sim0.25$). We detect a similar effect when comparing the LF of bright ($M^{group}_{^{0.1}r}-5log(h)<-23.5$) Gaussian and Non-Gaussian groups. Our results indicate that, for massive/luminous groups, the dynamical state of the system is directly related with the luminosity of its galaxy members.
We search for the presence of substructure, a non-Gaussian, asymmetrical velocity distribution of galaxies, and large peculiar velocities of the main galaxies in galaxy clusters with at least 50 member galaxies, drawn from the SDSS DR8. We employ a number of 3D, 2D, and 1D tests to analyse the distribution of galaxies in clusters: 3D normal mixture modelling, the Dressler-Shectman test, the Anderson-Darling and Shapiro-Wilk tests and others. We find the peculiar velocities of the main galaxies, and use principal component analysis to characterise our results. More than 80% of the clusters in our sample have substructure according to 3D normal mixture modelling, the Dressler-Shectman (DS) test shows substructure in about 70% of the clusters. The median value of the peculiar velocities of the main galaxies in clusters is 206 km/s (41% of the rms velocity). The velocities of galaxies in more than 20% of the clusters show significant non-Gaussianity. While multidimensional normal mixture modelling is more sensitive than the DS test in resolving substructure in the sky distribution of cluster galaxies, the DS test determines better substructure expressed as tails in the velocity distribution of galaxies. Richer, larger, and more luminous clusters have larger amount of substructure and larger (compared to the rms velocity) peculiar velocities of the main galaxies. Principal component analysis of both the substructure indicators and the physical parameters of clusters shows that galaxy clusters are complicated objects, the properties of which cannot be explained with a small number of parameters or delimited by one single test. The presence of substructure, the non-Gaussian velocity distributions, as well as the large peculiar velocities of the main galaxies, shows that most of the clusters in our sample are dynamically young.
We present a new approach for quantifying the abundance of galaxy clusters and constraining cosmological parameters using dynamical measurements. In the standard method, galaxy line-of-sight (LOS) velocities, $v$, or velocity dispersions are used to infer cluster masses, $M$, in order to quantify the halo mass function (HMF), $dn(M)/dlog(M)$, which is strongly affected by mass measurement errors. In our new method, the probability distribution of velocities for each cluster in the sample are summed to create a new statistic called the velocity distribution function (VDF), $dn(v)/dv$. The VDF can be measured more directly and precisely than the HMF and it can also be robustly predicted with cosmological simulations which capture the dynamics of subhalos or galaxies. We apply these two methods to mock cluster catalogs and forecast the bias and constraints on the matter density parameter $Omega_m$ and the amplitude of matter fluctuations $sigma_8$ in flat $Lambda$CDM cosmologies. For an example observation of 200 massive clusters, the VDF with (without) velocity errors constrains the parameter combination $sigma_8Omega_m^{0.29 (0.29)} = 0.587 pm 0.011 (0.583 pm 0.011)$ and shows only minor bias. However, the HMF with dynamical mass errors is biased to low $Omega_m$ and high $sigma_8$ and the fiducial model lies well outside of the forecast constraints, prior to accounting for Eddington bias. When the VDF is combined with constraints from the cosmic microwave background (CMB), the degeneracy between cosmological parameters can be significantly reduced. Upcoming spectroscopic surveys that probe larger volumes and fainter magnitudes will provide a larger number of clusters for applying the VDF as a cosmological probe.
Fossil groups (FGs) are galaxy aggregates with an extended and luminous X-ray halo, which are dominated by a very massive early-type galaxy and lack of L* objects. FGs are indeed characterized by a large magnitude gap between their central and surrounding galaxies. This is explained by either speculating that FGs are failed groups that formed without bright satellite galaxies and did not suffer any major merger, or by suggesting that FGs are very old systems that had enough time to exhaust their bright satellite galaxies through multiple major mergers. Since major mergers leave signatures in the stellar populations of the resulting galaxy, we study the stellar population parameters of the brightest central galaxies (BCGs) of FGs as a benchmark against which the formation and evolution scenarios of FGs can be compared. We present long-slit spectroscopic observations along different axes of NGC 6482 and NGC 7556, which are the BCGs of two nearby FGs. The measurements include spatially resolved stellar kinematics and radial profiles of line-strength indices, which we converted into stellar population parameters using single stellar-population models. NGC 6482 and NGC 7556 are very massive and large galaxies and host a centrally concentrated stellar population, which is significantly younger and more metal rich than the rest of the galaxy. The age gradients of both galaxies are somewhat larger than those of the other FG BCGs studied so far, whereas their metallicity gradients are similarly negative and shallow. They have negligible gradients of alpha-element abundance ratio. The measured metallicity gradients are less steep than those predicted for massive galaxies that formed monolithically and evolved without experiencing any major merger. We conclude that the observed FGs formed through major mergers rather than being failed groups that lacked bright satellite galaxies from the beginning.
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