Energy-transport effects can alter the structure that develops as a supernova evolves into a supernova remnant. The Rayleigh–Taylor instability is thought to produce structure at the interface between the stellar ejecta and the circumstellar matter, based on simple models and hydrodynamic simulations. Here we report experimental results from the National Ignition Facility to explore how large energy fluxes, which are present in supernovae, affect this structure. We observed a reduction in Rayleigh–Taylor growth. In analyzing the comparison with supernova SN1993J, a Type II supernova, we found that the energy fluxes produced by heat conduction appear to be larger than the radiative energy fluxes, and large enough to have dramatic consequences. No reported astrophysical simulations have included radiation and heat conduction self-consistently in modeling supernova remnants and these dynamics should be noted in the understanding of young supernova remnants.

This is an electronic reprint of the original article published by the Institute of Mathematical Statistics in The Annals of Applied Statistics, 2015, Vol. 9, No. 3, 1141-1168. This reprint differs from the original in pagination and typographic detail. 1 2 R. B. GRAMACY ET AL.data observed in physical experiments to the extent possible, and accounting for any biases in predictions based on new simulations. Here, we are interested in computer model calibration for experiments on radiative shocks. These are challenging to simulate because both hydrodynamic and radiation transport elements are required to describe the physics. The University of Michigan's Center for Radiative Shock Hydrodynamics (CRASH) is tasked with modeling a particular high-energy laser radiative shock system. The CRASH team developed a code outputting a space-time field that describes the evolution of a shock given specified initial conditions (the inputs), and has collected outputs for almost 27,000 such cases. The code has two inputs involved in addressing known deficiencies in the mathematical model, but which don't directly correspond to physical conditions. Our goal is to find values for these inputs, by calibrating the simulator to a limited amount of field data available from an earlier study, while simultaneously learning relationships governing the signal shared between simulated and field processes in order to make predictions under novel physical regimes. Kennedy and O'Hagan (2001) were the first to propose a statistical framework for such situations: a hierarchical model linking noisy field measurements from the physical system to the potentially biased output of a computer model run with the "true" (but unknown) value of any calibration parameters not controlled in the field. The backbone of the framework is a pair of coupled Gaussian process (GP) priors for (a) simulator output and (b) bias. The hierarchical nature of the model, paired with Bayesian posterior inference, allows both data sources (simulated and field) to contribute to joint estimation of all unknowns.The GP is a popular prior for deterministic computer model output [Sacks et al. (1989)]. In that context, GP predictors are known as surrogate models or emulators, and they have many desirable accuracy and coverage properties. However, their computational burden severely limits the size of training data sets-to as few as 1000 input-output pairs in many common setupsand that burden is compounded when emulators are nested inside larger frameworks, as in computer model calibration. Consequently, new methodology is required when there are moderate to large numbers of computer model trials, which is increasingly common in the simulation literature [e.g., Kaufman et al. (2011), Paciorek et al. (2013].Calibrating the radiative shock experiment requires a thriftier apparatus along several dimensions: to accommodate large simulation data, but also to recognize and exploit a massive discrepancy between the relative sizes of computer and field data sets. First, we modularize th...

[1] A statistical survey of dayside photoelectrons in the Mars upper ionosphere is presented and discussed. A methodology for isolating photoelectron spectra on strong crustal field lines is developed and used to obtain a database of over 280,000 distributions from the Mars Global Surveyor (MGS) electron reflectometer instrument. The relationship of these electron fluxes to various controlling factors is explored and presented. It is found that much of the flux variation can be explained by a linear fit with the EUV solar radiation proxy, adjusted for the Sun-Mars distance and local solar zenith angle. The state of the lower atmosphere seems to also play a critical role in the photoelectron flux intensity. An interval with a global dust storm shows an increased flux and steepened slope for the relationship with EUV radiation. This implies that the dust storm is altering the density, composition, and/or temperature of the photoelectron source region within the thermosphere and perhaps even the characteristics of the Mars exosphere above the MGS orbit. Other parameters considered had either no influence or a small perturbation compared to this dominant trend.

[1] A recent survey of the Mars Global Surveyor (MGS) electron data for dayside photoelectron observations over regions of strong crustal fields revealed an unusual bimodal solar flux dependence. The elevated-flux population was associated with the timing of a large global dust storm in late 2001. The results of a systematic study parameterizing the photoelectron flux intensities against a solar flux proxy and MGS-observed atmospheric dust opacity are presented here. Instantaneous dust opacities were used as well as time-history averages and maximal values. The result is a functional form for the photoelectron fluxes against these parameters. The inclusion of instantaneous dust opacity values in the function do not improve the correlation, but a time-history window significantly enhances the correlation and explains the bimodal distribution in the electron fluxes. The best relationship was obtained with 7-Earth-month time-history dust opacity variables included in the function. The most likely explanation for this long-lived influence of dust storms is a composition and/or density change in the upper atmosphere.

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