Publications

Probing the qcd critical point with relativistic heavy-ion collisions

We utilize an event-by-event relativistic hydrodynamic calculation performed at a number of different incident beam energies to investigate the creation of hot and dense QCD matter near the critical point. Using state-of-the-art analysis and visualization tools we demonstrate that each collision event probes QCD matter characterized by a wide range of temperatures and baryo-chemical potentials, making a dynamical response of the system to the vicinity of the critical point very difficult to isolate above the background.

S. Bass, H. Petersen, C. Quammen, H. Canary, C. Healey, and I. Taylor RussellM., “Probing the qcd critical point with relativistic heavy-ion collisions,” Central european journal of physics, vol. 10, pp. 1278-1281, 2012.

Exploring ensemble visualization

An ensemble is a collection of related datasets. Each dataset, or member, of an ensemble is normally large, multidimensional, and spatio-temporal. Ensembles are used extensively by scientists and mathematicians, for example, by executing a simulation repeatedly with slightly different input parameters and saving the results in an ensemble to see how parameter choices affect the simulation. To draw inferences from an ensemble, scientists need to compare data both within and between ensemble members. We propose two techniques to support ensemble exploration and comparison: a pairwise sequential animation method that visualizes locally neighboring members simultaneously, and a screen door tinting method that visualizes subsets of members using screen space subdivision. We demonstrate the capabilities of both techniques, first using synthetic data, then with simulation data of heavy ion collisions in high-energy physics. Results show that both techniques are capable of supporting meaningful comparisons of ensemble data.

M. N. Phadke, L. Pinto, O. Alabi, J. Harter, R. M. Taylor II, X. Wu, H. Petersen, S. A. Bass, and C. G. Healey, “Exploring ensemble visualization,” in Society of photo-optical instrumentation engineers (spie) conference series, 2012, p. 82940B-82940B-12.

Increasing the perceptual salience of relationships in parallel coordinate plots

We present three extensions to parallel coordinates that increase the perceptual salience of relationships between axes in multivariate data sets: (1) luminance modulation maintains the ability to preattentively detect patterns in the presence of overplotting, (2) adding a one-vs.-all variable display highlights relationships between one variable and all others, and (3) adding a scatter plot within the parallel-coordinates display preattentively highlights clusters and spatial layouts without strongly interfering with the parallel-coordinates display. These techniques can be combined with one another and with existing extensions to parallel coordinates, and two of them generalize beyond cases with known-important axes. We applied these techniques to two real-world data sets (relativistic heavy-ion collision hydrodynamics and weather observations with statistical principal component analysis) as well as the popular car data set. We present relationships discovered in the data sets using these methods.

J. M. Harter, X. Wu, O. S. Alabi, M. Phadke, L. Pinto, D. Dougherty, H. Petersen, S. Bass, and R. M. Taylor II, “Increasing the perceptual salience of relationships in parallel coordinate plots,” in Society of photo-optical instrumentation engineers (spie) conference series, 2012, p. 82940T-82940T-12.

Comparative visualization of ensembles using ensemble surface slicing

By definition, an ensemble is a set of surfaces or volumes derived from a series of simulations or experiments. Sometimes the series is run with different initial conditions for one parameter to determine parameter sensitivity. The understanding and identification of visual similarities and differences among the shapes of members of an ensemble is an acute and growing challenge for researchers across the physical sciences. More specifically, the task of gaining spatial understanding and identifying similarities and differences between multiple complex geometric data sets simultaneously has proved challenging. This paper proposes a comparison and visualization technique to support the visual study of parameter sensitivity. We present a novel single-image view and sampling technique which we call Ensemble Surface Slicing (ESS). ESS produces a single image that is useful for determining differences and similarities between surfaces simultaneously from several data sets. We demonstrate the usefulness of ESS on two real-world data sets from our collaborators.

O. S. Alabi, X. Wu, J. M. Harter, M. Phadke, L. Pinto, H. Petersen, S. Bass, M. Keifer, S. Zhong, C. Healey, and R. M. Taylor II, “Comparative visualization of ensembles using ensemble surface slicing,” in Society of photo-optical instrumentation engineers (spie) conference series, 2012, p. 82940U-82940U-12.

Classification of initial state granularity via 2d Fourier Expansion

A new method to quantify fluctuations in the initial state of heavy ion collisions is presented. The initial state energy distribution is decomposed with a set of orthogonal basis functions which include both angular and radial variation. The resulting two dimensional Fourier coefficients provide additional information about the nature of the initial state fluctuations compared to a purely angular decomposition. We apply this method to ensembles of initial states generated by both Glauber and Color Glass Condensate Monte-Carlo codes. In addition initial state configurations with varying amounts of fluctuations generated by a dynamic transport approach are analysed to test the sensitivity of the procedure. The results allow for a full characterization of the initial state structures that is useful to discriminate the different initial state models currently in use.

C. E. Coleman-Smith, H. Petersen, and R. L. Wolpert, “Classification of initial state granularity via 2d Fourier Expansion,” , 2012.

Characterizing the formation history of Milky Way-like stellar haloes with model emulators

We use the semi-analytic model ChemTreeN, coupled to cosmological N-body simulations, to explore how different galaxy formation histories can affect observational properties of Milky Way-like galaxies’ stellar haloes and their satellite populations. Gaussian processes are used to generate model emulators that allow one to statistically estimate a desired set of model outputs at any location of a p-dimensional input parameter space. This enables one to explore the full input parameter space orders of magnitude faster than could be done otherwise. Using mock observational data sets generated by ChemTreeN itself, we show that it is possible to successfully recover the input parameter vectors used to generate the mock observables if the merger history of the host halo is known. However, our results indicate that for a given observational data set the determination of ‘best fit’ parameters is highly susceptible to the particular merger history of the host. Very different halo merger histories can reproduce the same observational dataset, if the ‘best fit’ parameters are allowed to vary from history to history. Thus, attempts to characterize the formation history of the Milky Way using these kind of techniques must be performed statistically, analyzing large samples of high resolution N-body simulations.

F. A. Gomez, C. E. Coleman-Smith, B. W. O’Shea, J. Tumlinson, and R. Wolpert, “Characterizing the formation history of Milky Way-like stellar haloes with model emulators,” Astrophys.j., vol. 760, p. 112, 2012.

Constraining the initial state granularity with bulk observables in Au+Au collisions at $\sqrt{s_{\rm NN}}=200$ GeV

In this paper we conduct a systematic study of the granularity of the initial state of hot and dense QCD matter produced in ultra-relativistic heavy-ion collisions and its influence on bulk observables like particle yields, $m_T$ spectra and elliptic flow. For our investigation we use a hybrid transport model, based on (3+1)d hydrodynamics and a microscopic Boltzmann transport approach. The initial conditions are generated by a non-equilibrium hadronic transport approach and the size of their fluctuations can be adjusted by defining a Gaussian smoothing parameter $\sigma$. The dependence of the hydrodynamic evolution on the choices of $\sigma$ and $t_{start}$ is explored by means of a Gaussian emulator. To generate particle yields and elliptic flow that are compatible with experimental data the initial state parameters are constrained to be $\sigma=1$ fm and $t_{\rm start}=0.5$ fm. In addition, the influence of changes in the equation of state is studied and the results of our event-by-event calculations are compared to a calculation with averaged initial conditions. We conclude that even though the initial state parameters can be constrained by yields and elliptic flow, the granularity needs to be constrained by other correlation and fluctuation observables.

H. Petersen, C. Coleman-Smith, S. A. Bass, and R. Wolpert, “Constraining the initial state granularity with bulk observables in Au+Au collisions at $\sqrt{s_{\rm NN}}=200$ GeV,” J.phys., vol. G38, p. 45102, 2011.