Jet SIFT-ing: a new scale-invariant jet clustering algorithm for the substructure era

We introduce a new jet clustering algorithm named SIFT (Scale-Invariant Filtered Tree) that maintains the resolution of substructure for collimated decay products at large boosts. The scale-invariant measure combines properties of kT and anti-kT by preferring early association of soft radiation with a resilient hard axis, while avoiding the specification of a fixed cone size. Integrated filtering and variable-radius isolation criteria block assimilation of soft wide-angle radiation and provide a halting condition. Mutually hard structures are preserved to the end of clustering, automatically generating a tree of subjet axis candidates. Excellent object identification and kinematic reconstruction for multi-pronged resonances are realized across more than an order of magnitude in transverse energy. The clustering measure history facilitates high-performance substructure tagging, which we quantify with the aid of supervised machine learning. These properties suggest that SIFT may prove to be a useful tool for the continuing study of jet substructure.Comment: 29 pages, 23 figures, 5 tables, and 5 films (ancillary files

Optimisation of DTV coverage and broadcasting antennas

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe increased use of the available radio frequency spectrum by many existing as well as new technologies brings up the need of more advanced and specialised antennas, designed for very specific purposes. A very good example is the withdrawal of analogue TV from the radio spectrum, which has given space to be used by newer technologies such as 4G and 5G. Optimising the design of an antenna for a specific purpose is a goal that becomes more and more necessary. This suggests that an investigation of methods to optimise an electromagnetic design with the best possible results at the best possible time is also necessary. Evolutionary Algorithms (EA) are a very well know method which exhibits solid results within a smallest possible time for electromagnetic problems (e.g. antenna design optimisation). EA are nature inspired algorithms, and some very popular examples, which are widely used in antenna optimisation are the Differential Evolution (DE), Particle Swarm Optimisation (PSO) and Invasive Weed Optimisation (IWO). This thesis researches a comparison between the aforementioned methods, while also proposing a novel method, which is a modified version of IWO and has proven to be very solid. To determine the efficacy of the proposed method, all the algorithms were compared on some of the most major test functions for such purposes. Some examples are Ackley’s, De Jong’s, Holder table, Rastrigin and Rosenbrock. By employing these test functions, it was possible to determine the optimum settings of the modified IWO version. These methods are compared for different antenna design optimisation simulations using software such as MATLAB and CST Microwave Studio, to determine which method yields the best results and to output novel optimised antenna designs for different purposes. Some of the novel antenna designs that were applied to EAs for optimisation are a collinear dipole array with a specifically shaped radiation pattern and a log-periodic dipole antenna (LPDA) with flat gain response across its operating spectrum for Digital TV (DTV) broadcasting purposes. Other novel designs include a planar elliptical dipole antenna for Ultra-Wideband (UWB) Electromagnetic Compatibility (EMC) applications such as EMC measurements and a pin-fed notched circular patch antenna with circular polarisation for satellite communications, in which cases the small size of the generated geometries was also a goal so that portability is achieved. The geometrical parameters of the best possible antenna design were in some cases fabricated and compared to the simulated results so that the latter is compared to real world applications

OPTIMIZING COMPLEX BIOECONOMIC SIMULATIONS USING AN EFFICIENT SEARCH HEURISTIC

For simulation to be truly useful for investigating many problems in agricultural economics, non-simplifying optimization techniques need to be employed. General methods for simulation optimization that do not inhibit system characterization or analysis are available, and they would appear to provide much of the mathematical and optimizing rigor demanded by economists. This paper describes the theory and algorithm of a robust and efficient simulation optimization approach, the Complex Method. An example of implementing the algorithm is illustrated using a pest management problem.simulation, optimization, Complex Method, hill-climbing, Research Methods/ Statistical Methods,

Dynamic modeling and parameter estimation for an ethlyene-propylene-diene polymerization process

New Page 1 A general dynamic model for continuous EPDM polymerization in which crosslinking and gel formation are attributable to reactions between pendant double bonds has been developed. A pseudo-kinetic rate constant method is introduced to construct a moment model for a pseudo-homopolymer that approximates the behavior of the actual terpolymer under long chain and quasi-steady state assumptions. The pseudo-homopolymer model is then used as the basis for application of the numerical fractionation method. The proposed dynamic model is capable of predicting polydispersities and molecular weight distributions near the gel point with as few as eleven generations, and in the post-gel region with as few as five. The overall molecular weight distribution (MWD) of the sol was constructed by assuming a Schulz two parameter distribution for each generation. A parameter selection procedure is proposed to determine the kinetic parameters that can be estimated from the limited plant data. The procedure is based on the steady-state parameter output sensitivity matrix. The overall effect of each parameter on the measured outputs is determined using Principal Component Analysis (PCA). The angles between the sensitivity vectors are used as a measure of collinearity between parameters. A simple algorithm which provides a tradeoff between overall parameter effect on key outputs and collinearity yields a ranking of parameters by ease of estimation, independent of the available data. Its nonlinear and dynamic extensions are also developed and tested to address the nonlinearity and dynamics of the parameters\u27 effects on the outputs. The key kinetic parameters determined by the parameter selection procedure were estimated from steady-state data extracted from dynamic plant data, using a newly developed steady state detection tool. A hierarchical extended Kalman filter (EKF) design is proposed to estimate unmeasured state variables and key kinetic parameters of the EPDM kinetic model. The estimator design is based on decomposing the dynamic model into two subsystems, by exploiting the triangular model structure and the different sampling frequencies of the on-line and laboratory measurements directly related to the state variables of each subsystem. Simulation tests show that the hierarchical EKF generates satisfactory predictions even in the presence of measurement noise and plant/model mismatch

Expected Performance of the ATLAS Experiment - Detector, Trigger and Physics

A detailed study is presented of the expected performance of the ATLAS detector. The reconstruction of tracks, leptons, photons, missing energy and jets is investigated, together with the performance of b-tagging and the trigger. The physics potential for a variety of interesting physics processes, within the Standard Model and beyond, is examined. The study comprises a series of notes based on simulations of the detector and physics processes, with particular emphasis given to the data expected from the first years of operation of the LHC at CERN