Halo assembly bias and its effects on galaxy clustering

The clustering of dark halos depends not only on their mass but also on their assembly history, a dependence we term `assembly bias'. Using a galaxy formation model grafted onto the Millennium Simulation of the LCDM cosmogony, we study how assembly bias affects galaxy clustering. We compare the original simulation to `shuffled' versions where the galaxy populations are randomly swapped among halos of similar mass, thus isolating the effects of correlations between assembly history and environment at fixed mass. Such correlations are ignored in the halo occupation distribution models often used populate dark matter simulations with galaxies, but they are significant in our more realistic simulation. Assembly bias enhances 2-point correlations by 10% for galaxies with M_bJ-5logh brighter than -17, but suppresses them by a similar amount for galaxies brighter than -20. When such samples are split by colour, assembly bias is 5% stronger for red galaxies and 5% weaker for blue ones. Halo central galaxies are differently affected by assembly bias than are galaxies of all types. It almost doubles the correlation amplitude for faint red central galaxies. Shuffling galaxies among halos of fixed formation redshift or concentration in addition to fixed mass produces biases which are not much smaller than when mass alone is fixed. Assembly bias must reflect a correlation of environment with aspects of halo assembly which are not encoded in either of these parameters. It induces effects which could compromise precision measurements of cosmological parameters from large galaxy surveys.Comment: 8 pages, 4 figures, accepted for publication in MNRA

Lattice Bhatnagar-Gross-Krook studies of hydrodynamic and thermohydrodynamic internal pressure-driven flows.

In this thesis we develop applications of Lattice-Bhatnagar-Krook (LBGK) models to incompressible flow problems.We show that in geometries where flow is forced via application of a pressure difference, a modified Exactly Incompressible LBGK (EILBGK) scheme must be applied if significant pressure variations occur. We analyse the model's representation of the no-slip wall boundary condition for flow in a straight duct and recover a friction factor in excellent agreement with theory. Simulation of flow over a backward-facing step produces good agreement with other numerical techniques.We propose two new LBGK schemes, one directed towards the calculation of depth-averaged flow quantities and the other which focusses on thermal flows in the Boussinesq-Oberbeck limit. Depth-averaged flow facilitates the two-dimensional simulation of three-dimensional ducts of constant depth. The effect of the unmodelled dimension is accounted for by including momentum sinks in the momentum equations. We apply the scheme to flow in a bifurcating duct and results are again in good agreement with other numerical methods. We develop a thermal model in which energy is treated efficiently as a passively advected scalar quantity. This approach results in a model which is more simpleand robust than other previously reported LBGK thermal models. Our scheme is then validated by application to flow in a straight duct with constant heat flux applied at the walls. Excellent agreement with theoretical predictions is obtained for the calculated Nusselt number

SparkFlow : towards high-performance data analytics for Spark-based genome analysis

The recent advances in DNA sequencing technology triggered next-generation sequencing (NGS) research in full scale. Big Data (BD) is becoming the main driver in analyzing these large-scale bioinformatic data. However, this complicated process has become the system bottleneck, requiring an amalgamation of scalable approaches to deliver the needed performance and hide the deployment complexity. Utilizing cutting-edge scientific workflows can robustly address these challenges. This paper presents a Spark-based alignment workflow called SparkFlow for massive NGS analysis over singularity containers. SparkFlow is highly scalable, reproducible, and capable of parallelizing computation by utilizing data-level parallelism and load balancing techniques in HPC and Cloud environments. The proposed workflow capitalizes on benchmarking two state-of-art NGS workflows, i.e., BaseRecalibrator and ApplyBQSR. SparkFlow realizes the ability to accelerate large-scale cancer genomic analysis by scaling vertically (HyperThreading) and horizontally (provisions on-demand). Our result demonstrates a trade-off inevitably between the targeted applications and processor architecture. SparkFlow achieves a decisive improvement in NGS computation performance, throughput, and scalability while maintaining deployment complexity. The paper’s findings aim to pave the way for a wide range of revolutionary enhancements and future trends within the High-performance Data Analytics (HPDA) genome analysis realm.Postprin

The formation history of elliptical galaxies

We take advantage of the largest high-resolution simulation of cosmic structure growth ever carried out -- the Millennium Simulation of the concordance LambdaCDM cosmogony -- to study how the star formation histories, ages and metallicities of elliptical galaxies depend on environment and on stellar mass. We concentrate on a galaxy formation model which is tuned to fit the joint luminosity/colour/morphology distribution of low redshift galaxies. Massive ellipticals in this model have higher metal abundances, older luminosity-weighted ages, shorter star formation timescales, but lower assembly redshifts than less massive systems. Within clusters the typical masses, ages and metal abundances of ellipticals are predicted to decrease, on average, with increasing distance from the cluster centre. We also quantify the effective number of progenitors of ellipticals as a function of present stellar mass, finding typical numbers below 2 for M* < 10^{11} Msun, rising to about 5 for the most massive systems. These findings are consistent with recent observational results that suggest ``down-sizing'' or ``anti-hierarchical'' behaviour for the star formation history of the elliptical galaxy population, despite the fact that our model includes all the standard elements of hierarchical galaxy formation and is implemented on the standard, LambdaCDM cosmogony.Comment: 12 pages, 11 figures, minor revisions, MNRAS accepte

Red Galaxy Growth and the Halo Occupation Distribution

We have traced the past 7 Gyr of red galaxy stellar mass growth within dark matter halos. We have determined the halo occupation distribution, which describes how galaxies reside within dark matter halos, using the observed luminosity function and clustering of 40,696 0.2<z<1.0 red galaxies in Bootes. Half of 10^{11.9} Msun/h halos host a red central galaxy, and this fraction increases with increasing halo mass. We do not observe any evolution of the relationship between red galaxy stellar mass and host halo mass, although we expect both galaxy stellar masses and halo masses to evolve over cosmic time. We find that the stellar mass contained within the red population has doubled since z=1, with the stellar mass within red satellite galaxies tripling over this redshift range. In cluster mass halos most of the stellar mass resides within satellite galaxies and the intra-cluster light, with a minority of the stellar mass residing within central galaxies. The stellar masses of the most luminous red central galaxies are proportional to halo mass to the power of a third. We thus conclude that halo mergers do not always lead to rapid growth of central galaxies. While very massive halos often double in mass over the past 7 Gyr, the stellar masses of their central galaxies typically grow by only 30%.Comment: Accepted for publication in the ApJ. 34 pages, 22 Figures, 5 Table

Integrating pest population models with biophysical crop models to better represent the farming system

Farming systems frameworks such as the Agricultural Production Systems simulator (APSIM) represent fluxes through the soil, plant and atmosphere of the system well, but do not generally consider the biotic constraints that function within the system. We designed a method that allowed population models built in DYMEX to interact with APSIM. The simulator engine component of the DYMEX population-modelling platform was wrapped within an APSIM module allowing it to get and set variable values in other APSIM models running in the simulation. A rust model developed in DYMEX is used to demonstrate how the developing rust population reduces the crop's green leaf area. The success of the linking process is seen in the interaction of the two models and how changes in rust population on the crop's leaves feedback to the APSIM crop modifying the growth and development of the crop's leaf area. This linking of population models to simulate pest populations and biophysical models to simulate crop growth and development increases the complexity of the simulation, but provides a tool to investigate biotic constraints within farming systems and further moves APSIM towards being an agro-ecological framework

Integrating pest population models with biophysical crop models to better represent the farming system

Farming systems frameworks such as the Agricultural Production Systems simulator (APSIM) represent fluxes through the soil, plant and atmosphere of the system well, but do not generally consider the biotic constraints that function within the system. We designed a method that allowed population models built in DYMEX to interact with APSIM. The simulator engine component of the DYMEX population-modelling platform was wrapped within an APSIM module allowing it to get and set variable values in other APSIM models running in the simulation. A rust model developed in DYMEX is used to demonstrate how the developing rust population reduces the crop's green leaf area. The success of the linking process is seen in the interaction of the two models and how changes in rust population on the crop's leaves feedback to the APSIM crop modifying the growth and development of the crop's leaf area. This linking of population models to simulate pest populations and biophysical models to simulate crop growth and development increases the complexity of the simulation, but provides a tool to investigate biotic constraints within farming systems and further moves APSIM towards being an agro-ecological framework

The Social context of motorcycle riding and the key determinants influencing rider behavior: A qualitative investigation

Objective: Given the increasing popularity of motorcycle riding and heightened risk of injury or death associated with being a rider, this study explored rider behaviour as a determinant of rider safety and, in particular, key beliefs and motivations which influence such behaviour. To enhance the effectiveness of future education and training interventions, it is important to understand riders’ own views about what influences how they ride. Specifically, this study sought to identify key determinants of riders’ behaviour in relation to the social context of riding including social and identity-related influences relating to the group (group norms and group identity) as well as the self (moral/personal norm and self-identity). ----- ----- Method: Qualitative research was undertaken via group discussions with motorcycle riders (n = 41). Results: The findings revealed that those in the group with which one rides represent an important source of social influence. Also, the motorcyclist (group) identity was associated with a range of beliefs, expectations, and behaviours considered to be normative. Exploration of the construct of personal norm revealed that riders were most cognizant of the “wrong things to do” when riding; among those issues raised was the importance of protective clothing (albeit for the protection of others and, in particular, pillion passengers). Finally, self-identity as a motorcyclist appeared to be important to a rider’s self-concept and was likely to influence their on-road behaviour. ----- ----- Conclusion: Overall, the insight provided by the current study may facilitate the development of interventions including rider training as well as public education and mass media messages. The findings suggest that these interventions should incorporate factors associated with the social nature of riding in order to best align it with some of the key beliefs and motivations underpinning riders’ on-road behaviours