Tunable Wire Metamaterials for an Axion Haloscope

Metamaterials based on regular two-dimensional arrays of thin wires have attracted renewed attention in light of a recently proposed strategy to search for dark matter axions. When placed in the external magnetic field, such metamaterials facilitate resonant conversion of axions into plasmons near their plasma frequency. Since the axion mass is not known a priori, a practical way to tune the plasma frequency of metamaterial is required. In this work, we have studied a system of two interpenetrating rectangular wire lattices where their relative position is varied. The plasma frequency as a function of their relative position in two dimensions has been mapped out experimentally, and compared with both a semi-analytic theory of wire-array metamaterials and numerical simulations. Theory and simulation yield essentially identical results, which in turn are in excellent agreement with experimental data. Over the range of translations studied, the plasma frequency can be tuned over a range of 16%

Searching For Dark Matter with Plasma Haloscopes

We summarise the recent progress of the Axion Longitudinal Plasma HAloscope (ALPHA) Consortium, a new experimental collaboration to build a plasma haloscope to search for axions and dark photons. The plasma haloscope is a novel method for the detection of the resonant conversion of light dark matter to photons. ALPHA will be sensitive to QCD axions over almost a decade of parameter space, potentially discovering dark matter and resolving the Strong CP problem. Unlike traditional cavity haloscopes, which are generally limited in volume by the Compton wavelength of the dark matter, plasma haloscopes use a wire metamaterial to create a tuneable artificial plasma frequency, decoupling the wavelength of light from the Compton wavelength and allowing for much stronger signals. We develop the theoretical foundations of plasma haloscopes and discuss recent experimental progress. Finally, we outline a baseline design for ALPHA and show that a full-scale experiment could discover QCD axions over almost a decade of parameter space.Comment: Endorsers: Jens Dilling, Michael Febbraro, Stefan Knirck, and Claire Marvinney. 26 pages, 17 figures, version accepted in Physical Review

Long-term Radiation Monitoring Strategies after Nuclear Power Plant Accidents

Since the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident in 2011, radiation measurements and monitoring have been conducted continuously. Radiation air dose rate datasets have been archived extensively in this area. There are several different types of measurements: fixed-point measurements, walk surveys, car surveys, airborne surveys, and monitoring posts. They have different spatial coverage, footprints, and uncertainty. Currently, the monitoring program is expected to transition to long-term monitoring after ten years’ monitoring. The challenge of long-term monitoring is to build a cost effective and sustainable strategy for minimizing the cost associated with the number of monitoring locations or sampling, while maximizing the ability to meet the objectives of long-term monitoring. This study aims to develop the long-term radiation monitoring strategies after the FDNPP accident. In this dissertation, we tackle three key challenges: (1) multiscale spatial data integration, (2) monitoring optimization, and (3) spatiotemporal data integration. First, we developed an efficient algorithm for integrating the multiscale data sets; the algorithm is based on Kriging to estimate the dose rates for unobserved locations. Secondly, we developed a strategy and an algorithm to optimize the monitoring post placement and their number. This strategy is designed in order to reduce the number of sensors while capturing spatial heterogeneity. The algorithm is based on Gaussian process model to capture and estimate the heterogeneity of air-dose rates across the domain. Lastly, we built a Kalman-filter based algorithm combined with Gaussian Process Model to predict the spatial–temporal distribution of radiation dose rates. We expect that these methods will have valuable contributions for the long-term monitoring in the Fukushima region, but also for the preparation for the future nuclear accidents

The Inverse Optimization of an Optical Lithographic Source with a Hybrid Genetic Algorithm

As an effective resolution enhancement technology, source optimization (SO) is considered key for significantly improving the image quality of optical lithography at advanced nodes. To solve the problem of unsatisfactory SO performance, it is necessary to combine it with optimization algorithms. In this study, an SO method based on a hybrid genetic algorithm is proposed to achieve an acceptable source shape in the imaging process for optical lithography. To overcome the problems of local optima and the small search scope, an update strategy that uses particle swarm optimization and the tabu list method from the tabu search algorithm are utilized to enhance the optimization performance. Meanwhile, different feature patterns were employed as the input of the optimization model. These simulation results show that the proposed SO method exhibits dominant optimization performance for SO in optical lithography

Spatial and temporal prediction of radiation dose rates near Fukushima Daiichi Nuclear Power Plant

In this paper, we have developed a methodology to estimate the spatiotemporal distribution of radiation air dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP). In our exploratory data analysis, we found that (1) the temporal evolution of dose rates is composed of a log-linear decay trend and fluctuations of air dose rates that are spatially correlated among adjacent monitoring posts; and (2) the slope of the log-linear environmental decay trend can be represented as a function of the apparent initial dose rates, coordinate position, land-use type, and soil type. From these observations, we first estimated the log-linear decay trend at each location based on these predictors, using the random forest method. We then developed a modified Kalman filter coupled with a Gaussian process model to estimate the dose-rate time series at a given location and time. We applied this method to the Fukushima evacuation zone (as of March 2017), which included 17 monitoring post locations (with monitoring datasets collected between 2014 and 2018) and generated a time series of dose-rate maps. Our results show that this approach allows us to produce accurate spatial and temporal predictions of radiation dose-rate maps using limited spatiotemporal measurements

Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm

Radishes with black hearts will lose edible value and cause food safety problems, so it is important to detect and remove the defective ones before processing and consumption. A hyperspectral transmittance imaging system with 420 wavelengths was developed to capture images from white radishes. A successive-projections algorithm (SPA) was applied with 10 wavelengths selected to distinguish defective radishes with black hearts from normal samples. Pearson linear correlation coefficients were calculated to further refine the set of wavelengths with 4 wavelengths determined. Four chemometric classifiers were developed for classification of normal and defective radishes, using 420, 10 and 4 wavelengths as input variables. The overall classifying accuracy based on the four classifiers were 95.6%–100%. The highest classification with 100% was obtained with a back propagation artificial neural network (BPANN) for both calibration and prediction using 420 and 10 wavelengths. Overall accuracies of 98.4% and 97.8% were obtained for calibration and prediction, respectively, with Fisher's linear discriminant analysis (FLDA) based on 4 wavelengths, and was better than the other three classifiers. This indicated that the developed hyperspectral transmittance imaging was suitable for black heart detection in white radishes with the optimal wavelengths, which has potential for fast on-line discrimination before food processing or reaching storage shelves

Optimizing long-term monitoring of radiation air-dose rates after the Fukushima Daiichi Nuclear Power Plant.

Radiation air dose rates near the Fukushima Daiichi Nuclear Power Plant (FDNPP) have been steadily decreasing over the past eight years since the release of radioactive elements in March 2011. Currently, the radiation monitoring program is expected to transition to long-term monitoring after most of the remediation activities are completed. The main long-term monitoring objectives are to (1) confirm the continuing reduction of contaminant and hazard levels, (2) provide assurance for the public, (3) accumulate the basic datasets for scientific knowledge and future preparation, and (4) detect changes or anomalies in contaminant mobility (if they occur), or any unexpected processes or events. In this work, we have developed a methodology for optimizing the monitoring locations of radiation air dose-rate monitoring. Our approach consists of three steps in order to determine monitoring locations in a systematic manner: (1) prioritizing the critical locations, such as schools or regulatory requirement locations, (2) diversifying locations that cover the key environmental controls that are known to influence contaminant mobility and distributions, and (3) capturing the heterogeneity of radiation air-dose rates across the domain. For the second step, we use a Gaussian mixture model to identify the representative locations among multiple environmental variables, such as elevation and land-cover types. For the third step, we use a Gaussian process model to capture and estimate the heterogeneity of air-dose rates across the domain. Employing an integrated dose-rate map derived from Bayesian geostatistical methods as a reference map, we distribute the monitoring locations in such a way as to capture the heterogeneity of the reference map. Our results have shown that this approach allows us to select monitoring locations in a systematic manner such that the heterogeneity of air dose rates is captured by the minimal number of monitoring locations