Super-MeV Compton Imaging and 3D Gamma-Ray Imaging Using Pixelated CdZnTe

The dissertation presents work in gamma-ray imaging in the MeV range, 3D Compton imaging, and time encoded imaging. The first thrust in high energy gamma-ray imaging begins with analyzing the artifacts produced. These factors include the increase in pair-production events, incorrect event sequencing, and charge sharing due to the larger electron clouds. They all result in shift-variant artifacts that degrade the signal-to-noise ratio as well as create artifacts that might be mistaken for a hot spot. The degradation from artifacts is discussed and possible mitigation techniques are presented to allow for recovery of the Compton image. One of the presented mitigation techniques proposes a new sequencing algorithm for 3-or-more interaction events, called FIL-MSD. Missequencing presents one of the more dominant artifacts and by fixing the first interaction to be the largest deposited energy, the sequencing efficiency has increased by 20% in simulated data. Experimental results show an almost twofold increase in the signal to noise ratio (SNR) for simple backprojection images of a 22Na (1.7 MeV) source. The image resolution using filtered backprojection (FBP) was improved on by developing an analytical point spread function model for high energy 3-interaction events. Previous models did not account for missequencing effects in the model. Adding these effects into the model improved the resolution of the image, but at a cost of increased artifact production. In addition, the Wiener filter was formalized for spherical harmonics, which could be used for any number of interaction given an appropriate point spread function model. Next, demonstration of a 3D Compton imaging system is accomplished via sensor fusion of a foot-mounted odometer and a CdZnTe detector. A comparison between 3D Compton imaging and inverse-square image-reconstruction algorithms for certain measurement conditions is presented. The experiments demonstrate the advantage of 3D Compton imaging over traditional localization techniques in those scenarios. Improvements in time encoded imaging (TEI) were also made with advancements in the reconstruction algorithms and was done so in three thrusts: use of subpixel sensing, depth of interaction correction, and 3D imaging of extended sources. Complex 3D objects was accomplished via the use of magnification-parallax effects which allowed for the estimation of a source in distance away from the detector. Both the 3D Compton imaging and TEI techniques were explored at the Idaho National Laboratory.PHDNuclear ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155099/1/shyd_1.pd

Cone carving for surface reconstruction

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The Angular Momentum of the Circumgalactic Medium in the TNG100 Simulation

We present an analysis of the angular momentum content of the circumgalactic medium (CGM) using TNG100, one of the flagship runs of the IllustrisTNG project. We focus on Milky Way-mass halos ($\sim 10^{12} \; M_{\odot}$) at $z=0$ but also analyze other masses and redshifts up to $z=5$. We find that the CGM angular momentum properties are strongly correlated with the stellar angular momentum of the corresponding galaxy: the CGM surrounding high-angular momentum galaxies has a systematically higher angular momentum and is better aligned to the rotational axis of the galaxy itself than the CGM surrounding low-angular momentum galaxies. Both the hot and cold phases of the CGM show this dichotomy, though it is stronger for colder gas. The CGM of high-angular momentum galaxies is characterized by a large wedge of cold gas with rotational velocities at least $\sim1/2$ of the halo's virial velocity, extending out to $\sim 1/2$ of the virial radius, and by biconical polar regions dominated by radial velocities suggestive of galactic fountains; both of these features are absent from the CGM of low-angular momentum galaxies. These conclusions are general to halo masses $\lesssim 10^{12} \; M_{\odot}$ and for $z \lesssim 2$, but they do not apply for more massive halos or at the highest redshift studied. By comparing simulations run with alterations to the fiducial feedback model, we identify the better alignment of the CGM to high-angular momentum galaxies as a feedback-independent effect and the galactic winds as a dominant influence on the CGM's angular momentum.Comment: Accepted to ApJ. 16 pages, 12 figure

Inpainting hydrodynamical maps with deep learning

From 1,000 hydrodynamic simulations of the CAMELS project, each with a different value of the cosmological and astrophysical parameters, we generate 15,000 gas temperature maps. We use a state-of-the-art deep convolutional neural network to recover missing data from those maps. We mimic the missing data by applying regular and irregular binary masks that cover either $15\%$ or $30\%$ of the area of each map. We quantify the reliability of our results using two summary statistics: 1) the distance between the probability density functions (pdf), estimated using the Kolmogorov-Smirnov (KS) test, and 2) the 2D power spectrum. We find an excellent agreement between the model prediction and the unmasked maps when using the power spectrum: better than $1\%$ for $k<20 h/$Mpc for any irregular mask. For regular masks, we observe a systematic offset of $\sim5\%$ when covering $15\%$ of the maps while the results become unreliable when $30\%$ of the data is missing. The observed KS-test p-values favor the null hypothesis that the reconstructed and the ground-truth maps are drawn from the same underlying distribution when irregular masks are used. For regular-shaped masks on the other hand, we find a strong evidence that the two distributions do not match each other. Finally, we use the model, trained on gas temperature maps, to perform inpainting on maps from completely different fields such as gas mass, gas pressure, and electron density and also for gas temperature maps from simulations run with other codes. We find that visually, our model is able to reconstruct the missing pixels from the maps of those fields with great accuracy, although its performance using summary statistics depends strongly on the considered field.Comment: 14 pages, 6 figures, Submitted to AP

Modeling Galactic Conformity with the Color-Halo Age Relation in the Illustris Simulation

Comparisons between observational surveys and galaxy formation models find that the mass of dark matter haloes can largely explain galaxies' stellar mass. However, it remains uncertain whether additional environmental variables, generally referred to as assembly bias, are necessary to explain other galaxy properties. We use the Illustris Simulation to investigate the role of assembly bias in producing galactic conformity by considering 18,000 galaxies with $M_{stellar}$ > $2 \times 10^9$ $M_{\odot}$. We find a significant signal of galactic conformity: out to distances of about 10 Mpc, the mean red fraction of galaxies around redder galaxies is higher than around bluer galaxies at fixed stellar mass. Dark matter haloes exhibit an analogous conformity signal, in which the fraction of haloes formed at earlier times (old haloes) is higher around old haloes than around younger ones at fixed halo mass. A plausible interpretation of galactic conformity can be given as a combination of the halo conformity signal with the galaxy color-halo age relation: at fixed stellar mass, particularly toward the low-mass end, Illustris' galaxy colors correlate with halo age, with the reddest galaxies (often satellites) being preferentially found in the oldest haloes. In fact, we can explain the galactic conformity effect with a simple semi-empirical model, by assigning stellar mass based on halo mass (abundance matching) and by assigning galaxy color based on halo age (age matching). We investigate other interpretations for the galactic conformity, particularly its dependence on the isolation criterion and on the central-satellite information. Regarding comparison to observations, we conclude that the adopted selection/isolation criteria, projection effects, and stacking techniques can have a significant impact on the measured amplitude of the conformity signal.Comment: 15 pages, 8 figures; accepted for publication in MNRAS (minor revisions to match accepted version

Radiation Damage of 2×2×1 cm32 \times 2 \times 1 \ \mathrm{cm}^3 Pixelated CdZnTe Due to High-Energy Protons

Pixelated CdZnTe detectors are a promising imaging-spectrometer for gamma-ray astrophysics due to their combination of relatively high energy resolution with room temperature operation negating the need for cryogenic cooling. This reduces the size, weight, and power requirements for telescope-based radiation detectors. Nevertheless, operating CdZnTe in orbit will expose it to the harsh radiation environment of space. This work, therefore, studies the effects of $61 \ \mathrm{MeV}$ protons on $2 \times 2 \times 1 \ \mathrm{cm}^3$ pixelated CdZnTe and quantifies proton-induced radiation damage of fluences up to $2.6 \times 10^8 \ \mathrm{p/cm^2}$. In addition, we studied the effects of irradiation on two separate instruments: one was biased and operational during irradiation while the other remained unbiased. Following final irradiation, the $662 \ \mathrm{keV}$ centroid and nominal $1\%$ resolution of the detectors were degraded to $642.7 \ \mathrm{keV}, 4.9 \% \ ( \mathrm{FWHM})$ and $653.8 \ \mathrm{keV}, 1.75 \% \ (\mathrm{FWHM})$for the biased and unbiased systems respectively. We therefore observe a possible bias dependency on proton-induced radiation damage in CdZnTe. This work also reports on the resulting activation and recovery of the instrument following room temperature and$60^{\circ}\mathrm{C}$ annealing

The CAMELS multifield data set: Learning the universe’s fundamental parameters with artificial intelligence

We present the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) Multifield Data set (CMD), a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from more than 2000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span ∼100 million light-years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine-learning models, CMD is the largest data set of its kind containing more than 70 TB of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community