Using a large galaxy group catalogue constructed from the Sloan Digital Sky Survey Data Release 2, we investigate the correlation between various galaxy properties and halo mass. We split the population of galaxies in early-types, late-types and intermediate-types, based on their colour and specific star formation rate. At fixed luminosity, the late- (early-)type fraction of galaxies increases (decreases) with decreasing halo mass. Most importantly, this mass dependence is smooth and persists over the entire mass range probed, without any break or feature at any mass-scale. We argue that the previous claim of a characteristic feature on galaxy group scales is an artefact of the environment estimators used. At fixed halo mass, the luminosity dependence of the type fractions is surprisingly weak, especially over the range 0.25? L/L*? 2.5: galaxy type depends more strongly on halo mass than on luminosity. In agreement with previous studies, the late- (early-)type fraction increases (decreases) with increasing halocentric radius. However, we find that this radial dependence is present in haloes of all masses probed (down to 10 12 h -1 M ⊙ ), while previous studies did not find any radial dependence in haloes with M? 10 13.5 h -1 M ⊙ . We argue that this discrepancy owes to the fact that we have excluded central galaxies from our analysis. We also find that the properties of satellite galaxies are strongly correlated with those of their central galaxy. In particular, the early-type fraction of satellites is significantly higher in a halo with an early-type central galaxy than in a halo of the same mass but with a late-type central galaxy. This phenomenon, which we call 'galactic conformity', is present in haloes of all masses and for satellites of all luminosities. Finally, the fraction of intermediate-type galaxies is always ∼20 per cent, independent of luminosity, independent of halo mass, independent of halocentric radius, and independent of whether the galaxy is a central galaxy or a satellite galaxy. We discuss the implicat ns of all these findings for galaxy formation and evolution.

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Properties of galaxy groups in the Sloan Digital Sky Survey – I. The dependence of colour, star formation and morphology on halo mass

We analyse the observed correlation between galaxy environment and Halpha emission-line strength, using volume-limited samples and group catalogues of 24 968 galaxies at 0.05 < z < 0.095, drawn from the 2dF Galaxy Redshift Survey (M-bJ < -19.5) and the Sloan Digital Sky Survey (M-r < -20.6). We characterize the environment by: (1) Sigma(5), the surface number density of galaxies determined by the projected distance to the fifth nearest neighbour; and (2) rho(1.1) and rho(5.5), three-dimensional density estimates obtained by convolving the galaxy distribution with Gaussian kernels of dispersion 1.1 and 5.5 Mpc, respectively. We find that star-forming and quiescent galaxies form two distinct populations, as characterized by their H equivalent width, W-0(Halpha). The relative numbers of star-forming and quiescent galaxies vary strongly and continuously with local density. However, the distribution of W-0(Halpha) amongst the star-forming population is independent of environment. The fraction of star-forming galaxies shows strong sensitivity to the density on large scales, rho(5.5), which is likely independent of the trend with local density, rho(1.1). We use two differently selected group catalogues to demonstrate that the correlation with galaxy density is approximately independent of group velocity dispersion, for sigma = 200-1000 km s(-1). Even in the lowest-density environments, no more than similar to70 per cent of galaxies show significant Halpha emission. Based on these results, we conclude that the present-day correlation between star formation rate and environment is a result of short-time-scale mechanisms that take place preferentially at high redshift, such as starbursts induced by galaxy-galaxy interactions.

https://researchbank.swinburne.edu.au/file/1326e4cd-120c-4c0f-8947-aeb005b6f371/1/PDF (Accepted manuscript).pdf

Galaxy ecology: groups and low-density environments in the SDSS and 2dFGRS

In almost no other field of computer science, the idea of using bio-inspired search paradigms has been so useful as in solving multiobjective optimization problems. The idea of using a population of search agents that collectively approximate the Pareto front resonates well with processes in natural evolution, immune systems, and swarm intelligence. Methods such as NSGA-II, SPEA2, SMS-EMOA, MOPSO, and MOEA/D became standard solvers when it comes to solving multiobjective optimization problems. This tutorial will review some of the most important fundamentals in multiobjective optimization and then introduce representative algorithms, illustrate their working principles, and discuss their application scope. In addition, the tutorial will discuss statistical performance assessment. Finally, it highlights recent important trends and closely related research fields. The tutorial is intended for readers, who want to acquire basic knowledge on the mathematical foundations of multiobjective optimization and state-of-the-art methods in evolutionary multiobjective optimization. The aim is to provide a starting point for researching in this active area, and it should also help the advanced reader to identify open research topics.

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A tutorial on multiobjective optimization: fundamentals and evolutionary methods

THE best adjective to describe this work is \"sweep11 ing.\" The range of subject matter is so broad that it can almost be described as containing everything except fuzzy set theory. Included are explicit discussions of the basics of probability (relegated to an appendix); the practice of problem solving; testing hypotheses about statistical parameters; calculating and interpreting confidence limits; tolerance limits and prediction limits; setting up and interpreting control charts; design of experiments; analysis of variance; line and surface fitting; and maximum likelihood procedures. If you can think of something that is not in this list, then it probably means I have overlooked it.

Experiments: Planning, Analysis, and Parameter Design Optimization

Metamodeling is becoming a rather popular means to approximate the expensive simulations in today’s complex engineering design problems since accurate metamodels can bring in a lot of benefits. The metamodel accuracy, however, heavily depends on the locations of the observed points. Adaptive sampling, as its name suggests, places more points in regions of interest by learning the information from previous data and metamodels. Consequently, compared to traditional space-filling sampling approaches, adaptive sampling has great potential to build more accurate metamodels with fewer points (simulations), thereby gaining increasing attention and interest by both practitioners and academicians in various fields. Noticing that there is a lack of reviews on adaptive sampling for global metamodeling in the literature, which is needed, this article categorizes, reviews, and analyzes the state-of-the-art single−/multi-response adaptive sampling approaches for global metamodeling in support of simulation-based engineering design. In addition, we also review and discuss some important issues that affect the success of an adaptive sampling approach as well as providing brief remarks on adaptive sampling for other purposes. Last, challenges and future research directions are provided and discussed.

A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design

Complexity in solving real-world multicriteria optimization problems often stems from the fact that complex, expensive, and/or time-consuming simulation tools or physical experiments are used to evaluate solutions to a problem. In such settings, it is common to use efficient computational models, often known as surrogates or metamodels, to approximate the outcome (objective or constraint function value) of a simulation or physical experiment. The presence of multiple objective functions poses an additional layer of complexity for surrogate-assisted optimization. For example, complexities may relate to the appropriate selection of metamodels for the individual objective functions, extensive training time of surrogate models, or the optimal use of many-core computers to approximate efficiently multiple objectives simultaneously. Thinking out of the box, complexity can also be shifted from approximating the individual objective functions to approximating the entire Pareto front. This leads to further complexities, namely, how to validate statistically and apply the techniques developed to real-world problems. In this paper, we discuss emerging complexity-related topics in surrogate-assisted multicriteria optimization that may not be prevalent in nonsurrogate-assisted single-objective optimization. These complexities are motivated using several real-world problems in which the authors were involved. We then discuss several promising future research directions and prospective solutions to tackle emerging complexities in surrogate-assisted multicriteria optimization. Finally, we provide insights from an industrial point of view into how surrogate-assisted multicriteria optimization techniques can be developed and applied within a collaborative business environment to tackle real-world problems.

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Surrogate-Assisted Multicriteria Optimization: Complexities, Prospective Solutions, and Business Case

This paper aims to demonstrate that the initial population plays an important role in the convergence of genetic algorithms independently from the algorithm and the problem. Using a well-distributed sampling increases the robustness and avoids premature convergence. The observation is proved using MOGA-II and NSGA-II with different sampling methods. This result is particularly important whenever the optimization involves time-consuming functions.

The Effect of Initial Population Sampling on the Convergence of Multi-Objective Genetic Algorithms

The NBI-NLPQLP optimization method is tested on several multi-objective optimization problems. Its performance is compared to that of MOGA-II: since NBI-NLPQLP is based on the classical gradient- based NLPQLP, it is fast and accurate, but not as robust, in comparison with the genetic algorithm. Furthermore a discontinuous Pareto frontier can give rise to problems in the NBI's convergence. In order to over- come this problem, a hybridization technique coupled with a partitioning method is proposed.

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NBI and MOGA-II, two complementary algorithms for Multi-Objective optimizations

We study morphology and luminosity segregation of galaxies in loose groups. We analyze the two catalogs of groups identified in the Nearby Optical Galaxy (NOG) sample, by means of hierarchical and percolation “friends-of-friends” methods (HG and PG catalogs, respectively). In the first part of our analysis we consider 387 and 436 groups of HG and PG and compare morphology- (luminosity-) weighted to unweighted group properties: velocity dispersion, mean pairwise distance, and mean groupcentric distance of member galaxies. The second part of our analysis is based on two ensemble systems, one for each catalog, built by suitably combining together galaxies of all groups (1584 and 1882 galaxies for HG and PG groups). We find that earlier-type (brighter) galaxies are more clustered and lie closer to the group centers, both in position and in velocity, than later-type (fainter) galaxies. Spatial segregations are stronger than kinematical segregations. These effects are generally detected at the $\mathrel{\hbox{\rlap{\hbox{\lower4pt\hbox{~}}}\hbox{$>$}}}$3-sigma level. Luminosity segregation is shown to be independent of morphology segregation. Our main conclusions are strengthened by the detection of segregation in both hierarchical and percolation catalogs. Our results agree with a continuum of segregation properties of galaxies in systems, from low-mass groups to massive clusters.

/pdf/morphology-and-luminosity-segregation-of-galaxies-in-nearby-4bxf59krbz.pdf

Morphology and luminosity segregation of galaxies in nearby loose groups

We study morphology and luminosity segregation of galaxies in loose groups. We analyze the two catalogs of groups which have been identified in the Nearby Optical Galaxy (NOG) sample, by means of hierarchical and percolation ``friends-of-friends'' methods (HG and PG catalogs, respectively). In the first part of our analysis we consider 387 and 436 groups of HG and PG, respectively, and compare morphology- (luminosity-) weighted to unweighted group properties: velocity dispersion, mean pairwise distance, and mean groupcentric distance of member galaxies. The second part of our analysis is based on two ensemble systems, one for each catalog, built by suitably combining together galaxies of all groups (1584 and 1882 galaxies for HG and PG groups, respectively). We find that earlier-type (brighter) galaxies are more clustered and lie closer to the group centers, both in position and in velocity, than later-type (fainter) galaxies. Spatial segregations are stronger than kinematical segregations. These effects are generally detected at the larger than 3-sigma level. Luminosity segregation is shown to be independent of morphology segregation. Our main conclusions are strengthened by the detection of segregation in both hierarchical and percolation catalogs. Our results agree with a continuum of segregation properties of galaxies in systems, from low-mass groups to massive clusters.

https://arxiv.org/pdf/astro-ph/0305237.pdf

Enrico Rigoni

Papers

Surrogate-Assisted Multicriteria Optimization: Complexities, Prospective Solutions, and Business Case

The Effect of Initial Population Sampling on the Convergence of Multi-Objective Genetic Algorithms

NBI and MOGA-II, two complementary algorithms for Multi-Objective optimizations

Morphology and luminosity segregation of galaxies in nearby loose groups

Morphology and luminosity segregation of galaxies in nearby loose groups