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MITIGATION of BACKGROUNDS for the LARGE UNDERGROUND XENON DARK MATTER EXPERIMENT
Doctor of Philosophy, Case Western Reserve University, 2015, Physics
While the existence of particle dark matter is widely accepted through multitude of astrophysics evidence, its exact nature remains mysterious. It is expected to comprise the local galactic halo, and one of the most favored candidates, weakly interacting massive particle (WIMP), is hypothesized to interact with baryonic matter. Such an interaction can be detected in a radio-quiet low-threshold detector such as the large underground xenon (LUX) detector. The LUX is a dual-phase xenon time projection chamber (TPC), and it operates at Sanford Underground Research Facility in Lead, SD. Analysis of the first science data with a 86.3 days live-time from LUX yielded the best spin-independent WIMP-nucleon cross-section exclusion limit to date, with the lower limit of $7.6\times10^{-46}$~cm$^2$ at 33~GeV/c$^2$ with a 90\% confidence level. This thesis consists the following chapters. The case for cold dark matter from the current cosmological observations is reviewed. The natures of the expected WIMP-nucleon scattering signal and the techniques to discriminate the background events are discussed. Principles of the dual-phase TPC are explained, with details of the LUX hardware. The original works for this thesis follows. A campaign to remove radioactive noble impurities from the target xenon is described in depth. A position reconstruction algorithm based on comparison of observed data to simulation is developed. Background events from the detector's internal walls are studied and modeled for the profile likelihood ratio test of the second analysis. Finally, the first published results are reviewed in detail.

Committee:

Thomas Shutt (Committee Chair); Daniel Akerib (Advisor); Corbin Covault (Committee Member); Stacy McGaugh (Committee Member); Harsh Mathur (Committee Member)

Subjects:

Astrophysics; Chemical Engineering; Particle Physics; Physics; Radiation

Keywords:

Dark matter, direct detection, liquid xenon, gas chromatography, position reconstruction, dual-phase time projection chamber, radon progeny, profile likelihood ratio test, WIMP, WIMP-nucleon cross-section

LUX Thermosyphon Cryogenics and Radon-Related Backgrounds for the First WIMP Result
Doctor of Philosophy, Case Western Reserve University, 2014, Physics
The cold, non-baryonic dark matter hypothesis describes the motions of galaxies and clusters, as well as the character of the large scale structure of the universe. Particle freeze- out arguments suggest a weakly interacting massive particle (WIMP) plays the dark matter role. Decades of experiments have sought direct interaction with normal matter and this thesis describes the efforts of the Large Underground Xenon (LUX) experiment to directly detect WIMP-xenon recoils. The LUX detector is a 370-kg two-phase time projection chamber (TPC) deployed at the Davis Campus of the 4850’ Level of the Sanford Underground Research Facility. It col- lects scintillation and ionization signals from particle interactions in the liquid xenon (LXe) to measure deposited energy and recoil type. The xenon is purified by circulating through a heated zirconium getter, condensed and evaporated with heat exchangers, and calibrated with internal and external sources to characterize the detector response to gammas, betas, neutrons, and alphas in a keV-MeV energy range. Long, stable operation of the detector at 175 K is accomplished with the thermosyphon cryogenic system, which is a passive, gravity-assisted closed cooling loop. The phase change of a fixed amount of nitrogen transports heat from the detector to a thermal bath of liquid nitrogen (LN) above the cryostat. It delivers 1000 W of cooling power through four loops whose transport tubing fits within a 6-inch diameter tube to cool the cryostat in a water tank 6.5 m below the LN bath. Low-power heaters provide temperature control to within 0.1 K. This system provided months of stable operation underground at 25-slpm xenon circulation with a 5-W heat load. The first WIMP hunt consisted of 85 live-days of data acquisition. Alpha decays from radon were observed and studied to characterize their background contributions to the WIMP analysis. The (a,n) rate and electron recoils from beta decays in the 222Rn chain were found to be sub-dominant to other irreducible backgrounds. All backgrounds were carefully measured to create a model used in the profile likelihood ratio test statistic for the first LUX result that produced the most sensitive WIMP limit to date.

Committee:

Tom Shutt, PhD (Advisor); Dan Akerib, PhD (Advisor); John Ruhl, PhD (Committee Member); Harsh Mathur, PhD (Committee Member); Stacy McGaugh, PhD (Committee Member)

Subjects:

Astrophysics; Particle Physics; Physics

Keywords:

dark matter; particle astrophysics; radon; thermosyphon; WIMP; LUX; Homestake; SURF;

On the Borel complexity of some classes of Banach spaces
PHD, Kent State University, 2013, College of Arts and Sciences / Department of Mathematical Science
In this dissertation I mainly study several classes of Banach spaces and I try to compute, of at least to obtain a lower/upper bound, to its Borel complexity. Also, using those results, we show some non-universality results for some of those classes of Banach spaces.

Mathematics

Keywords:

Effros-Borel structure, Banach spaces, Banach-Saks property, Radon-Nikodym property, complete continuous property, weak compact operators, unconditional converging operators, local structure

Correction model based ANN modeling approach for the estimation of Radon concentrations in Ohio
Master of Science in Electrical Engineering, University of Toledo, 2012, College of Engineering
According to National Cancer Institute, radon is one of the major causes for lung cancer related deaths after smoking in the United States. In order to prevent deaths due to radon inhalation there is a need to determine the level of radon concentration in each locality, e.g., zip-codes. However, factors like inapproachability hinder the process of estimating radon concentration in some places. In such places the radon concentrations could be estimated using several interpolation techniques. In this thesis, a new approach that improves the accuracy of the neural model with the help of sensitivity based correction model for modeling and estimating radon concentrations in Ohio is proposed. The results are compared with commonly used techniques such as kriging, radial basis function (RBF), inverse distance weighting (IDW), global polynomial interpolation (GPI), local polynomial interpolation (LPI) and the recently developed conventional ANN modeling approach. Further, model accuracies of all the above interpolation schemes are evaluated based on the ranked performance measures criteria with emphasis on the extreme-end (peak-end, low-end), and mid-range radon concentrations. The results demonstrate the effectiveness of the proposed approach in estimating the radon concentrations. The prediction accuracy of the proposed approach is found to be improved by 70-80% compared to the other techniques.

Subjects:

Electrical Engineering

Keywords:

Artificial neural networks; Correction Model; Indoor Air Quality Measures; Interpolation; Ohio; Radon; Zip code

Development of Artificial Neural Networks Based Interpolation Techniques for the Modeling and Estimation of Radon Concentrations in Ohio
Master of Science, University of Toledo, 2010, Engineering (Computer Science)

Radon is a chemically inert, naturally occurring radioactive gas. It is one of the main causes of lung cancer second to smoking, and accounts for about 25,000 deaths every year in the US alone according to the National Cancer Institute. In order to initiate preventative measures to reduce the deaths caused by radon inhalation, it is helpful to have radon concentration data for each locality, e.g. zip code. However, such data are not available for every zip code in Ohio, owing to several reasons including inapproachability. In places where data is unavailable, radon concentrations must be estimated using interpolation techniques to take appropriate preventive measures against cancer.

This thesis proposes new interpolation techniques based on Artificial Neural Networks utilizing the available knowledge in terms of Radon concentration data and Uranium concentration data for modeling and predicting Radon concentrations in Ohio, US. Several models were first trained and then validated using available data to identify the best model for each technique. Model accuracies using the proposed approaches were proven to be significantly better in comparison to conventional interpolation techniques such as Kriging and Radial Basis Functions.

Committee:

Vijay Devabhaktuni, PhD (Advisor); Ashok Kumar, PhD (Advisor); Mohammed Niamat, PhD (Committee Member)

Subjects:

Environmental Engineering

Keywords:

Artificial neural networks; Interpolation; Modeling; Ohio; Zip code; Radon; Uranium; Knowledge Based Neural Network; Source Difference Method; Prior Knowledge Input, Space Mapped Neural Network

Estimation of Unmeasured Radon Concentrations in Ohio Using Quantile Regression Forest
Master of Science, University of Toledo, 2014, College of Engineering

Committee:

Vijay Devabhaktuni (Committee Chair); Ashok Kumar (Committee Member); Mansoor Alam (Committee Member)

Subjects:

Applied Mathematics; Electrical Engineering; Mathematics

Keywords:

Radon; Kriging; Local Polynomial Interpolation; Global Polynomial Interpolation; Radial Basis Function; Artificial Neural Networks; Random Forest Regression; Quantile Regression Forest; operational performance measures

REPORT ON AN INTERNSHIP WITH THE FORT WAYNE-ALLEN COUNTY DEPARTMENT OF HEALTH, FORT WAYNE, INDIANA
Master of Environmental Science, Miami University, 2004, Environmental Sciences
As a requirement of earning a Master of Environmental Science degree, I interned with the Environmental/General Services division of the Fort Wayne-Allen County Department of Health in Fort Wayne, Indiana from December 10, 2001 to June 10, 2002. During my internship I was responsible for determining the environmental source of poisoning for children poisoned with lead as well providing parents and landowners with suggestions to eliminate the found sources. In addition, I was designated as the Indoor Air Quality Specialist, requiring me to provide education and complaint investigation of indoor air quality issues. I was also responsible for providing GIS and GPS map production for the entire department. Other duties included assisting during investigations of adult and child neglect cases, inspecting local tattoo and body piercing establishments, presenting a bi-annual blood-borne pathogen course to local tattoo and body piercing artists, and weekly sampling of public pools.

Subjects:

Environmental Sciences

Keywords:

Data acquisition and reconstruction techniques for improved electron paramagnetic resonance (EPR) imaging
Doctor of Philosophy, The Ohio State University, 2007, Electrical Engineering
Electron paramagnetic resonance imaging (EPRI) is capable of measuring both endogenous and introduced free radical distributions in variety of biological systems. Despite the inherent potential, the broad use of EPRI is hampered by slow acquisition which can be the bottleneck for many biological applications where conditions may change over time. The objective of this work is to reduce the data acquisition time without degrading the reconstruction quality. First, we have modeled the data acquisition process for both spatial and spectral-spatial imaging in the form of Radon transform. Efficient-to-program expressions for Radon and inverse Radon transform for 2D, 3D, and 4D EPRI are derived. Second, we have proposed a method to uniformly distribute the data for both 3D and 4D EPRI which, from fewer projections, can generate reconstruction results which are better than those based on the existing nonuniform or partially uniform sampling techniques. The expected savings in the acquisition time offered by the suggested uniform sampling are 30% and 50% for 3D and 4D, respectively. In addition, we have also discussed existing uniform sampling methods and compared their performance with the suggested method using simulation and experimental data. Third, we have suggested a single-stage filtered backprojection reconstruction for 3D and 4D EPRI using the partial Radon transform for 4-fold acceleration. This substantial speed up further motivated us to reconsider the iterative reconstruction methods such as algebraic reconstruction which, despite having the superior performance in terms of reconstruction quality, have not been routinely used for 3D and 4D reconstructions due to their slow speeds. With the use of partial Radon transform, along with proper choice of interpolation type, the 3D iterative reconstruction time is reduced by more than 80%, which implies that a 64×64×64 image can be reconstructed from 150 projections using 100 iterations in approximately 10 minutes with ordinary contemporary computing resources. Further, the ability of the iterative methods to model EPR related constraints and to incorporate any other a priori information, such as nonnegativity, has also been demonstrated. Again, both simulation and experimental data are used to evaluate and compare the suggested reconstruction procedures.

Keywords:

EPR; EPRI; Oximetry; 4D Radon Transform; Filtered Backprojection; Fekete; ART; MART

A Comparison of Various Interpolation Techniques for Modeling and Estimation of Radon Concentrations in Ohio
Master of Science in Engineering, University of Toledo, 2013, Engineering (Computer Science)
Radon-222 and its parent Radium-226 are naturally occurring radioactive decay products of Uranium-238. The US Environmental Protection Agency (USEPA) attributes about 10 percent of lung cancer cases that is `around 21,000 deaths per year’ in the United States, caused due to indoor radon. The USEPA has categorized Ohio as a Zone 1 state (i.e. the average indoor radon screening level greater than 4 picocuries per liter). In order to implement preventive measures, it is necessary to know radon concentration levels in all the zip codes of a geographic area. However, it is not possible to survey all the zip codes, owing to reasons such as inapproachability. In such places where radon data are unavailable, several interpolation techniques are used to estimate the radon concentrations. This thesis presents a comparison between recently developed interpolation techniques to new techniques such as Support Vector Regression (SVR), and Random Forest Regression (RFR). Recently developed interpolation techniques include Artificial Neural Network (ANN), Knowledge Based Neural Networks (KBNN), Correction-Based Artificial Neural Networks (CBNN) and the conventional interpolation techniques such as Kriging, Local Polynomial Interpolation (LPI), Global Polynomial Interpolation (GPI) and Radial Basis Function (RBF) using the K-fold cross validation method.

Committee:

William Acosta (Committee Chair); Vijay Devabhaktuni (Committee Co-Chair); Ashok Kumar (Committee Member); Rob Green (Committee Member)

Computer Science

Keywords:

artificial neural networks; cross-validation; correction based artificial neural networks; prior knowledge input; source difference; space-mapped neural networks; support vector regression; radon; random forest regression

TRANSPORT OF RADON IN STILL WATER
PhD, University of Cincinnati, 2005, Engineering : Nuclear and Radiological Engineering

Keywords:

radon; 222 Rn; water; diffusion; diffusion coefficient

Evaluation of Spatial Interpolation Techniques Built in the Geostatistical Analyst Using Indoor Radon Data for Ohio,USA
Master of Science in Civil Engineering, University of Toledo, 2012, Civil Engineering

According to the United States Environmental Protection Agency, radon is the number one cause of lung cancer among non-smokers, and it is responsible for about 21,000 lung cancer deaths every year in the United States. In the State of Ohio, 14% of lung cancer deaths are caused due to radon. It is essential to have the radon concentration data for every location (i.e., zip codes) so that necessary preventive measures can be taken up. Measuring the radon concentration across the entire State of Ohio will be very expensive and time consuming. This research focuses on the application of six geographical information system (GIS) based interpolation techniques to estimate the radon concentration in the unmeasured zip codes in the State of Ohio. The radon concentration in homes has been obtained by The University of Toledo researchers from various commercial testing services, university researchers, and county health departments. The data are divided into two sets. The first set uses 80% of the data for training different interpolation schemes, and the second data set includes 20% of the data to evaluate the interpolation techniques. Statistical performance measures such as coefficient of correlation (r), Spearman correlation coefficient (¿¿), slope of the regression line (m), ratio of the intercept of the regression line to the average observed concentrations (b/Co), fractional variance (FV), fraction of prediction within a factor of two of the observations (FA2), model comparison measure (MCM2), geometric mean bias (MG), geometric mean variance (VG), normalized mean square error (NMSE), fractional bias (FB), revised index of agreement (IOAr), accuracy for paired peak (Ap), maximum ratio (Rmax), scatter plots, quantile – quantile (QQ) plots and bootstrap 95% confidence interval estimates based on extreme-end concentrations (i.e., peak-end/low-end), and the mid-range concentrations of indoor air quality (IAQ) models are performed on the predicted data points to evaluate the best interpolation technique.

Considering the statistical indicators for peak-end, low-end and mid-range estimates, it has been found that cokriging is a suitable technique for peak-end estimates, and the radial basis function (RBF) technique meets all the acceptable criteria for low-end and mid-range estimates. After considering the closeness of the greater number of measures to their respective ideal values, graphical representations of the scatter plots and QQ plots, the RBF technique surpasses the other six interpolation techniques. Again, the summary of the bootstrap confidence interval estimates among the techniques indicate that the RBF technique is not significantly different from the other five interpolation techniques under all situations. Therefore, the RBF technique may not be the best technique always when applied to similar sets of dataset from other states and countries. The RBF technique is tentatively suggested in this thesis to perform the interpolation of radon concentration for the unmeasured zip codes in the State of Ohio. This technique is used to understand the extent of radon problems in Ohio. This approach provides a complete picture of radon distribution in the state. It has been found from the zip code based analysis that the number of zip codes exceeding 2.7 pCi/l (World Health Organization (WHO) recommended limit), 4 pCi/l (US Environmental Protection Agency (EPA) action limit), 8 pCi/l and 20 pCi/l are 1300, 693, 28, and 2, respectively after prediction using the RBF technique.

Committee:

Ashok Kumar, PhD (Committee Chair); Brian W. Randolph, PhD (Committee Member); Matthew Franchetti, PhD (Committee Member)

Subjects:

Environmental Engineering

Keywords:

Radon; GIS; kriging; cokriging; radial basis function (RBF); Inverse Distance Weighting (IDW); Local Polynomial Interpolation (LPI); Global PolynomiaI Interpolation (GPI); interpolation; spatial interpolation

STUDY OF SPATIAL/TEMPORAL PATTERNS OF RADON RELEASES FROM THE K-65 SILOS, USING DISPERSION MODELING AND GIS: A CASE STUDY AT THE DEPARTMENT OF ENERGY'S FERNALD ENVIRONMENTAL MANAGEMENT PROJECT, CINCINNATI, OHIO
MA, University of Cincinnati, 2001, Arts and Sciences : Geography

Geography

Keywords:

RADON; DISPERSION MODELING; GEOGRAPHIC INFORMATION SYSTEMS (GIS); INDUSTRIAL SOURCE COMPLEX 3 (ISC3) MODEL

Quantitative description of successive transformations in atmospheric samples /
Doctor of Philosophy, The Ohio State University, 1953, Graduate School

Committee:

Not Provided (Other)

Physics

Keywords:

AUTOMATED CURVED HAIR DETECTION AND REMOVAL IN SKIN IMAGES TO SUPPORT AUTOMATED MELANOMA DETECTION
Master of Sciences, Case Western Reserve University, 2013, EECS - Electrical Engineering
If detected early, skin cancer has a 95-100% successful treatment rate; therefore early detection is crucial and several computer-aided methods have been developed to assist dermatologists. In skin images removing hairs without altering the lesion is important to effectively apply detection algorithms. This thesis focuses on the use of image processing techniques to remove hairs by identifying hair pixels contained within a binary image mask using the Generalized Radon Transform. The Radon Transform was adapted to find quadratic curves characterized by rotational angle and scaling. The method detects curved hairs in the image mask for removal and replacement through pixel interpolation. Implementing this technique in MATLAB gives the ability to perform tests rapidly on both simulated and actual images. The quadratic Radon transform performs well in curve detection; however, the research points out the need for better algorithms to improve hair masking, peak detection, and interpolation replacement.

Committee:

Marc Buchner, PhD (Advisor); Kenneth Loparo, PhD (Committee Member); Vira Chankong, PhD (Committee Member)

Subjects:

Electrical Engineering

Keywords:

Image Processing Hair Curve Detection Skin Lesion Cancer Radon Transform Quadratic Automated Melanoma