Brandon Michael Medina

We present a method to estimate distances to asymptotic giant branch (AGB) stars in the Galaxy, using spectral energy distributions (SEDs) in the near- and mid-infrared. By assuming that a given set of source properties (initial mass, stellar temperature, composition, and evolutionary stage) will provide a typical SED shape and brightness, sources are color matched to a distance-calibrated template and thereafter scaled to extract the distance. The method is tested by comparing the distances obtained to those estimated from very long baseline interferometry or Gaia parallax measurements, yielding a strong correlation in both cases. Additional templates are formed by constructing a source sample likely to be close to the Galactic center, and thus with a common, typical distance for calibration of the templates. These first results provide statistical distance estimates to a set of almost 15,000 Milky Way AGB stars belonging to the Bulge Asymmetries and Dynamical Evolution (BAaDE) survey, with typical distance errors of +/- 35%. With these statistical distances, a map of the intermediate-age population of stars traced by AGBs is formed, and a clear bar structure can be discerned, consistent with the previously reported inclination angle of 30 degrees to the GC-Sun direction vector. These results motivate deeper studies of the AGB population to tease out the intermediate-age stellar distribution throughout the Galaxy, as well as determining statistical properties of the AGB population luminosity and mass-loss-rate distributions.

Verification and benchmark problems allow code developers to assess numerical accuracy and increase confidence that specific sets of model physics were implemented correctly in the code for application to real world problems. In this work, we compare a benchmark test of a relativistic (500keV) electron beam propagating through a pressurized (0.1 mbar) Ar gas cell and present the results between the general plasma code EMPIRE and the hybrid code GAZEL. EMPIRE can be run as a fully kinetic Particle-In-Cell (PIC) problem with Direct Simulation Monte Carlo (DSMC) collisions or as a hybrid problem with both fluid and PIC charged species which collide with a background neutral fluid via fluid-fluid rate-based interactions and PIC-fluid Monte Carlo Collisions (MCC) that produce a charged fluid of low-energy secondary plasma. GAZEL is a hybrid code that utilizes deterministic collisions between the fluid and beam particles via a pseudo-fluid, which is generated from spatial averages of the beam computational particles. We compare both fully kinetic EMPIRE results with hybrid EMPIRE and hybrid GAZEL results and find reasonably good agreement until late times when presently numerical instabilities result in the beam breaking up in the EMPIRE simulations.

Laser Doppler velocimetry, also known as photonic Doppler velocimetry (PDV), is a diagnostic commonly used in shock compression experiments. To quantitatively compare experimental results to computational results, the uncertainty in the velocimetry measurements must be well characterized. That is, we must know which features in the experimental data are statistically significant and which are not. In this contribution, we will present recent work on understanding the resolution, precision, and accuracy of PDV measurements. We will discuss how the uncertainty is a function of signal strength, noise, and signal digitization, and how the uncertainty is influenced by the analysis method. We will show how knowledge of uncertainty can be used to rigorously compare experiment with computational predictions.