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Generating synthetic magnetic field intermittency using a Minimal Multiscale Lagrangian Mapping approach
The Minimal Multiscale Lagrangian Mapping procedure developed in the context of neutral fluid turbulence is a simple method for generating synthetic vector fields. Using a sequence of low-pass filtered fields, fluid particles are displaced at their rms speed for some scale-dependent time interval, and then interpolated back to a regular grid. Fields produced in this way are seen to possess certain properties of real turbulence. This paper extends the technique to plasmas by taking into account the coupling between the velocity and magnetic fields. We examine several possible applications to plasma systems. One use is as initial conditions for simulations, wherein these synthetic fields may efficiently produce a strongly intermittent cascade. The intermittency properties of the synthetic fields are also compared with those of the solar wind. Finally, studies of cosmic ray transport and modulation in the test particle approximation may benefit from improved realism in synthetic fields produced in this way.
Probing Heavy Majorana Neutrinos and the Weinberg Operator through Vector Boson Fusion Processes in Proton-Proton Collisions at s = 13 TeV
The first search exploiting the vector boson fusion process to probe heavy Majorana neutrinos and the Weinberg operator at the LHC is presented. The search is performed in the same-sign dimuon final state using a proton-proton collision dataset recorded at s = 13 TeV , collected with the CMS detector and corresponding to a total integrated luminosity of 138 fb -1 . The results are found to agree with the predictions of the standard model. For heavy Majorana neutrinos, constraints on the squared mixing element between the muon and the heavy neutrino are derived in the heavy neutrino mass range 50 GeV–25 TeV; for masses above 650 GeV these are the most stringent constraints from searches at the LHC to date. A first test of the Weinberg operator at colliders provides an observed upper limit at 95% confidence level on the effective $μμ$ Majorana neutrino mass of 10.8 GeV.
Spin Alignment of Vector Mesons in Heavy-Ion Collisions
Polarized quarks and antiquarks in high-energy heavy-ion collisions can lead to the spin alignment of vector mesons formed by quark coalescence. Here, using the relativistic spin Boltzmann equation for vector mesons derived from Kadanoff-Baym equations with an effective quark-meson model for strong interaction and quark coalescence model for hadronizaton, we calculate the spin density matrix element $ρ_{00}$ for Φ mesons and show that anisotropies of local field correlations with respect to the spin quantization direction lead to Φ meson's spin alignment. We propose that the local correlation or fluctuation of Φ fields is the dominant mechanism for the observed Φ meson's spin alignment and its strength can be extracted from experimental data as functions of collision energies. The calculated transverse momentum dependence of $ρ_{00}$ agrees with STAR's data. We further predict the azimuthal angle dependence of $ρ_{00}$ which can be tested in future experiments.
Streaming Compression of Scientific Data via Weak-SINDy
Here, in this paper, a streaming weak-SINDy algorithm is developed specifically for compressing streaming scientific data. The production of scientific data, either via simulation or experiments, is undergoing a stage of exponential growth, which makes data compression important and often necessary for storing and utilizing large scientific data sets. As opposed to classical “offline” compression algorithms that perform compression on a readily available data set, streaming compression algorithms compress data “online” while the data generated from simulation or experiments is still flowing through the system. This feature makes streaming compression algorithms well suited for scientific data compression, where storing the full data set offline is often infeasible. This work proposes a new streaming compression algorithm, streaming weak-SINDy, which takes advantage of the underlying data characteristics during compression. The streaming weak-SINDy algorithm constructs feature matrices and target vectors in the online stage via a streaming integration method in a memory efficient manner. The feature matrices and target vectors are then used in the offline stage to build a model through a regression process that aims to recover equations that govern the evolution of the data. For compressing high-dimensional streaming data, we adopt a streaming proper orthogonal decomposition (POD) process to reduce the data dimension and then use the streaming weak-SINDy algorithm to compress the temporal data of the POD expansion. We propose modifications to the streaming weak-SINDy algorithm to accommodate the dynamically updated POD basis. By combining the built model from the streaming weak-SINDy algorithm and a small amount of data samples, the full data flow could be reconstructed accurately at a low memory cost, as shown in the numerical tests.
Polarized fusion and potential in situ tests of fuel polarization survival in a tokamak plasma
Abstract The use of spin-polarized fusion fuels would provide a significant boost towards the ignition of a burning plasma. The cross section for D + T → α + n, would be increased by 1.5 if the fuels were injected with parallel polarization. Furthermore, our simulations demonstrate additional non-linear power gains in large-scale machines such as ITER, due to increased alpha heating. Such benefits require the survival of spin polarizations for periods comparable to the particle confinement time. During the 1980s, calculations predicted that polarizations could survive a plasma environment, although concerns persisted regarding the cumulative impacts of wall recycling. In that era, technical challenges prevented direct tests and left the large scale fueling of a power reactor beyond reach. Over the last decades, this situation has changed dramatically. Detailed simulations of ITER have predicted negligible wall recycling in a high-power reactor, and recent advances in laser-driven sources project the capability of producing large quantities of ∼100% polarized D and T. The remaining crucial step is an in-situ demonstration of polarization survival in a plasma. For this, we outline a measurement strategy using the isospin-mirror reaction, D + 3 He → α + p. Polarized 3 He avoids the complexities of handling tritium, while encompassing the same spin-physics. We evaluate two methods of delivering deuterium, using dynamically polarized Lithium-Deuteride (with vector polarization P V D of 70%) or frozen-spin Hydrogen-Deuteride (with P V D of 40%), together with a method of injecting optically-pumped 3 He (with 65% polarization). Pellets of these materials all have long polarization decay times (∼6 min for LiD at 2 K, ∼2 months for HD at 2 K, and ∼3 d for 3 He at 77 K), all far greater than a plasma shot in a research tokamak such as DIII-D (∼20 s). Both species can be propelled from a single cryogenic injection gun. We review plasma requirements and strategies for detecting polarization survival. Polarization alters both fusion yields and the angular distribution of fusion products, and each of these provides a potential signal. In this paper we simulate a selection of shots with similar characteristics in a future high-T ion H plasma, and find ratios of yields from shots with fuel spins parallel and antiparallel reaching 1.3 (HD + 3 He) to 1.6 (LiD + 3 He) over a wide range of poloidal angles. (A companion paper finds sensitivity to fusion product angular distributions as reflected in the pitch angles of protons and alphas reaching the plasma facing wall.)
Investigation of Portable Event-Based Monte Carlo Transport Using the NVIDIA Thrust Library
Power consumption considerations are driving future high performance computing platforms toward many-core computing architectures. Los Alamos National Laboratory's Trinity machine, available in 2016, will use both Intel Xeon Haswell processors and Intel Xeon Phi Knights Landing many integrated core (MIC) architecture coprocessors. Lawrence Livermore National Laboratory's Sierra machine, available in 2018, will use an IBM PowerPC architecture along with Nvidia graphics processing unit (GPU) architecture accelerators. These different advanced architectures make the computing landscape in upcoming years complex. Traditional approaches to Monte Carlo transport do not work efficiently on these new computing platforms. MIC architectures require vectorization to operate efficiently, and vectorization is difficult to achieve in Monte Carlo transport. GPU architectures require additional code to explicitly use the hardware, requiring significant code changes or hardware specific branches in the source code. A significant challenge for Monte Carlo transport projects is to simultaneously support within a single source code base efficient simulations for both the current generation of architectures and the different advanced computing architectures. In order to address these challenges, two important changes are typically required: a new algorithmic approach for solving Monte Carlo transport, and explicit use of hardware specific software. In this paper, we describe initial research investigations of an event-based Monte Carlo transport algorithm implemented using the Nvidia Thrust library on a GPU for a Monte Carlo test code. The event-based algorithm targets many-core architectures by increasing SIMD (single instruction multiple data) parallelism, while Thrust potentially provides portable performance by allowing one source code base to compile code targeted for both CPUs and GPUs. We described preliminary investigations of portable event-based Monte Carlo algorithms implemented using the Nvidia Thrust library in a research Monte Carlo test code. We found that an explicit CUDA implementation of an event-based Monte Carlo algorithm performed significantly more efficiently than a Thrust implementation on GPU platforms, most likely as a result of additional flexibility in access to different memory spaces on the GPU. Additionally, we showed that on GPU platforms and at large enough problem sizes the event-based implementations perform more efficiently than the serial history-based implementation running on the host CPU. While investigating this problem, we also discovered that the performance of the event-based algorithm is affected by what tallies are being used. A zonal scalar flux tally requires atomic operations that significantly impacted the performance of the code, in some cases producing slowdowns instead of speedups. We decided to remove the tally in order to focus on the effectiveness of the event-based algorithm. Future work will be required to research more effective ways of handling such tallies. Additionally, we would like to consider new ways for optimizing both the Thrust and CUDA versions, in order to see how much performance we have yet to achieve. The potential trade-off between portability and performance was demonstrated in this investigation. Thrust provides both CPU and GPU versions of the code in one code base, but it does so at a cost. We discovered approximately a factor of 2-5 performance difference between Thrust and CUDA on each GPU platform we tested. Thrust provides a tool to access the GPUs with less effort and specialization, but it does so by giving up fine grained control where extra performance can be found for this application. Future work will be required to determine whether this performance differential between Thrust and CUDA can be reduced for event-based Monte Carlo transport. (authors)
Model-Based Detection of Coordinated Attacks (DCA) in Distribution Systems
The fast-paced growth in digitization of smart grid components enhances system observability and remote-control capabilities through efficient communication. However, enhanced connectivity results in heightened system vulnerability towards cybersecurity risks in the cyber-physical power system. Coordinated cyber-attacks (CCA), when undetected, lead to system-wide impact in terms of large disturbances or widespread outages. Detecting CCA in the cyber layer is critical to thwart cyber-attacks in real-time before the attack impacts the physical system. The challenge of locating CCA stems from the complex grid dynamics, making it difficult to distinguish between normal operational variations and cyber-attack impact. CCA often employs multiple attack vectors targeting geographically distributed components, further complicating CCA identification. Existing research in intrusion detection is primarily focused on the transmission network and limited to detecting individual attacks. In this paper, a novel proactive DCA strategy is proposed for early detection of CCA by establishing correlations among distinct attack events through model-based reinforcement learning that utilizes abductive reasoning to conclude the attacker goal. The solution includes understanding the system model, learning the system dynamics, and correlating individual cyber-attacks to extract the attacker’s objective. The developed learning algorithm identifies the most probable attack path to reach the attacker’s objective by predicting the next attack steps. A DNP3-based cyber-physical co-simulation testbed is developed to test the proposed algorithm using the IEEE 13-node test feeder.
Naturalness confronts nature: searches for supersymmetry with the CMS detector in pp collisions at √s = 8 and $13$ TeV
In this thesis, we present two inclusive searches for supersymmetric particles at √s = 8 and 13 TeV using the razor variables and guided by the principle of naturalness. We build a framework to explore the natural supersymmetry parameter space of gluino and top squark masses and branching ratios, which is a unique attempt to cover this parameter space in a more complete way than ever before using LHC data. With this approach, the production of top squarks and gluinos are excluded below ~ 700 GeV and ~ 1.6 TeV, respectively, independent of the branching ratios, constituting one of the tightest constraints on natural supersymmetry from the LHC. Motivated by the need to mitigate the effects of multiple interactions per bunch crossing (pileup), an essential feature of present and future hadron colliders, in this thesis we also study the precision timing capabilities of a LYSO-based sampling calorimeter, and achieve a time resolution of ~ 30 ps in electron test beam measurements. The achieved resolution corresponds to the precision needed to significantly reduce the inclusion of pileup particles in the reconstruction of the event of interest. This study is foundational in building an R&D program on precision timing for the high-luminosity LHC and other future hadron colliders. We also propose alternative simplified models to study Higgs-plus-jets events at the LHC, and reinterpret an excess observed at 8 TeV in the context of these models. Finally, we discuss a search for narrow resonances in the dijet mass spectrum at 13 TeV using the data-scouting technique at CMS, which records a smaller event format to increase the maximum recordable rate. For the benchmark models with a vector or axial-vector mediator that couples to quarks and dark matter particles, the dijet search excludes mediator masses from 0.5 TeV up to ~ 2.7 TeV largely independent of the dark matter particle mass, which constitutes a larger exclusion than traditional mono-X searches at the LHC. In the plane of the dark matter-nucleon interaction cross section versus dark matter mass, the dijet search is also more sensitive than direct detection experiments for spin-dependent cross sections.
A New Repellent for Redbay Ambrosia Beetle (Coleoptera: Curculionidae: Scolytinae), Primary Vector of the Mycopathogen That Causes Laurel Wilt
The redbay ambrosia beetle, Xyleborus glabratus, was detected in Georgia, USA, in 2002 and has since spread to 11 additional states. This wood-boring weevil carries a symbiotic fungus, Harringtonia lauricola, that causes laurel wilt, a lethal disease of trees in the Lauraceae family. Native ambrosia beetles that breed in infected trees can acquire H. lauricola and contribute to the spread of laurel wilt. Since 2002, laurel wilt has devastated native Persea species in coastal forests and has killed an estimated 200,000 avocado trees in Florida. Since laurel wilt is difficult to manage once it has entered a susceptible agrosystem, this study evaluated piperitone as a candidate repellent to deter attacks by X. glabratus and other ambrosia beetles. Additionally, piperitone was compared to the known repellent verbenone as a potential cost-effective alternative. The repellent efficacy was determined by comparing captures in traps baited with commercial beetle lures containing α-copaene versus captures in traps baited with lures plus a repellent. In parallel 10-week field tests, the addition of piperitone reduced the captures of X. glabratus in α-copaene-baited traps by 90%; however, there was no significant reduction in the captures of native ambrosia beetles in ethanol-baited traps. In two replicate 10-week comparative tests, piperitone and verbenone both reduced X. glabratus captures by 68–90%, with longevity over the full 10 weeks. This study identifies piperitone as a new X. glabratus repellent with potential for pest management.
Omega-Limit Sets and Input-To-State Stability in Power Grids with Switching Equilibria
This paper studies a power transmission system with both conventional generators (CGs) and distributed energy assets (DEAs) providing frequency control. We consider an operating condition with demand aggregating two dynamic components: one that switches between different values on a finite set, and one that varies smoothly over time. Such dynamic operating conditions may result from protection scheme activations, external cyber-attacks, or due to the integration of dynamic loads, such as data centers. Mathematically, the dynamics of the resulting system are captured by a system that switches between a finite number of vector fields - or modes -, with each mode having a distinct equilibrium point induced by the demand aggregation. To analyze the stability properties of the resulting switching system, we leverage tools from hybrid dynamic inclusions and the concept of ..omega.. -limit sets from sets. Specifically, we characterize a compact set that is semi-globally practically asymptotically stable under the assumption that the switching frequency and load variation rate are sufficiently slow. For arbitrarily fast variations of the load, we use a level-set argument with multiple Lyapunov functions to establish input-to-state stability of a larger set and with respect to the rate of change of the loads. The theoretical results are illustrated via numerical simulations on the IEEE 39-bus test system.
An interface reconstruction method based on an analytical formula for 3D arbitrary convex cells
In this study, we are interested in an interface reconstruction method for 3D arbitrary convex cells that could be used in multi-material flow simulations for instance. We assume that the interface is represented by a plane whose normal vector is known and we focus on the volume-matching step that consists in finding the plane constant so that it splits the cell according to a given volume fraction. We follow the same approach as in the recent authors' publication for 2D arbitrary convex cells in planar and axisymmetrical geometries, namely we derive an analytical formula for the volume of the specific prismatoids obtained when decomposing the cell using the planes that are parallel to the interface and passing through all the cell nodes. This formula is used to bracket the interface plane constant such that the volume-matching problem is rewritten in a single prismatoid in which the same formula is used to find the final solution. Finally, the proposed method is tested against an important number of reproducible configurations and shown to be at least five times faster.
Observational Evidence of Magnetic Reconnection for Brightenings and Transition Region Arcades in IRIS Observations
By using a new method of forced-field extrapolation, we study the emerging flux region AR11850 observed by the Interface Region Imaging Spectrograph and Solar Dynamical Observatory . Our results suggest that the bright points (BPs) in this emerging region exhibit responses in lines formed from the upper photosphere to the transition region, which have relatively similar morphologies. They have an oscillation of several minutes according to the Atmospheric Imaging Assembly data at 1600 and 1700 Å. The ratio between the BP intensities measured in 1600 and 1700 Å filtergrams reveals that these BPs are heated differently. Our analysis of the Helioseismic and Magnetic Imager vector magnetic field and the corresponding topology in AR11850 indicates that the BPs are located at the polarity inversion line and most of them are related to magnetic reconnection or cancelation. The heating of the BPs might be different due to different magnetic topology. We find that the heating due to the magnetic cancelation would be stronger than the case of bald patch reconnection. The plasma density rather than the magnetic field strength could play a dominant role in this process. Based on physical conditions in the lower atmosphere, our forced-field extrapolation shows consistent results between the bright arcades visible in slit-jaw image 1400 Å and the extrapolated field lines that pass through the bald patches. It provides reliable observational evidence for testing the mechanism of magnetic reconnection for the BPs and arcades in the emerging flux region, as proposed in simulation studies.
Final report on accident tolerant fuel performance analysis of APMT-Steel Clad/UO₂ fuel and APMT-Steel Clad/UN-U₃Si₅ fuel concepts
In FY2014 our group completed and documented analysis of new Accident Tolerant Fuel (ATF) concepts using BISON. We have modeled the viability of moving from Zircaloy to stainless steel cladding in traditional light water reactors (LWRs). We have explored the reactivity penalty of this change using the MCNP-based burnup code Monteburns, while attempting to minimize this penalty by increasing the fuel pellet radius and decreasing the cladding thickness. Fuel performance simulations using BISON have also been performed to quantify changes to structural integrity resulting from thinner stainless steel claddings. We account for thermal and irradiation creep, fission gas swelling, thermal swelling and fuel relocation in the models for both Zircaloy and stainless steel claddings. Additional models that account for the lower oxidation stainless steel APMT are also invoked where available. Irradiation data for HT9 is used as a fallback in the absence of appropriate models. In this study the isotopic vectors within each natural element are varied to assess potential reactivity gains if advanced enrichment capabilities were levied towards cladding technologies. Recommendations on cladding thicknesses for a robust cladding as well as the constitutive components of a less penalizing composition are provided. In the first section (section 1-3), we present results accepted for publication in the 2014 TOPFUEL conference regarding the APMT/UO₂ ATF concept (J. Galloway & C. Unal, Accident Tolerant and Neutronically Favorable LWR Cladding, Proceedings of WRFPM 2014, Sendai, Japan, Paper No.1000050). Next we discuss our preliminary findings from the thermo-mechanical analysis of UN-U₃Si₅ fuel with APMT clad. In this analysis we used models developed from limited data that need to be updated when the irradiation data from ATF-1 test is available. Initial results indicate a swelling rate less than 1.5% is needed to prevent excessive clad stress.
Unraveling the Noise: An Investigation Plan for Signal Interference in Hearing Aids
Bluetooth Low Energy (BLE) has revolutionized the performance of hearing aids with functionalities like seamless audio streaming and enhanced auditory functions. However, BLE operates within the highly congested 2.4 GHz frequency band, making it susceptible to signal interference that can degrade performance, reduce audio quality, and impact user experience. This paper documents interference patterns in BLE communication and introduces practical mitigation techniques aimed at improving the reliability of hearing aids. Attack vectors associated with Bluetooth enabled hearing aids include communication jamming, the interception of data between target devices, and GATT handle exploitation. Attackers could also use the vulnerabilities to block communications or intercept sensitive audio streams, posing significant security and privacy risks. These threats compromise two critical components of the CIA triad: (1) availability, by causing persistent connectivity issues, and (2) integrity, by enabling unauthorized data modifications. It is necessary to deal with these problems to ensure hearing aids work well and safely. This study investigates the impact of BLE signal interference on hearing aids, using tools such as HackRF [1], a Python tool to simulate interference scenarios, and Ubertooth [2] to sniff Bluetooth traffic between hearing aids and the device with the application. This paper investigates testing of BLE traffic in search of specific interference patterns that would impact the functionality of hearing aids, including jamming and flooding. This research focuses on developing robust mitigation techniques with the aim of securing BLE-enabled hearing aids against those vulnerabilities.
MO-DE-207B-05: Predicting Gene Mutations in Renal Cell Carcinoma Based On CT Imaging Features: Validation Using TCGA-TCIA Datasets
Purpose: The goal of this work is to investigate the use of contrast enhanced computed tomographic (CT) features for the prediction of mutations of BAP1, PBRM1, and VHL genes in renal cell carcinoma (RCC). Methods: For this study, we used two patient databases with renal cell carcinoma (RCC). The first one consisted of 33 patients from our institution (UT Southwestern Medical Center, UTSW). The second one consisted of 24 patients from the Cancer Imaging Archive (TCIA), where each patient is connected by a unique identi?er to the tissue samples from the Cancer Genome Atlas (TCGA). From the contrast enhanced CT image of each patient, tumor contour was first delineated by a physician. Geometry, intensity, and texture features were extracted from the delineated tumor. Based on UTSW dataset, we completed feature selection and trained a support vector machine (SVM) classifier to predict mutations of BAP1, PBRM1 and VHL genes. We then used TCIA-TCGA dataset to validate the predictive model build upon UTSW dataset. Results: The prediction accuracy of gene expression of TCIA-TCGA patients was 0.83 (20 of 24), 0.83 (20 of 24), and 0.75 (18 of 24) for BAP1, PBRM1, and VHL respectively. For BAP1 gene, texture feature was the most prominent feature type. For PBRM1 gene, intensity feature was the most prominent. For VHL gene, geometry, intensity, and texture features were all important. Conclusion: Using our feature selection strategy and models, we achieved predictive accuracy over 0.75 for all three genes under the condition of using patient data from one institution for training and data from other institutions for testing. These results suggest that radiogenomics can be used to aid in prognosis and used as convenient surrogates for expensive and time consuming gene assay procedures.
Current Status and Future Prospects for the Light Dark Matter eXperiment
The constituents of dark matter are still unknown, and the viable possibilities span a vast range of masses. The physics community has established searching for sub-GeV dark matter as a high priority and identified accelerator-based experiments as an essential facet of this search strategy. A key goal of the accelerator-based dark matter program is testing the broad idea of thermally produced sub-GeV dark matter through experiments designed to directly produce dark matter particles. The most sensitive way to search for the production of light dark matter is to use a primary electron beam to produce it in fixed-target collisions. The Light Dark Matter eXperiment (LDMX) is an electron-beam fixed-target missing-momentum experiment that realizes this approach and provides unique sensitivity to light dark matter in the sub-GeV range. This contribution provides an overview of the theoretical motivation, the main experimental challenges, how LDMX addresses these challenges, and projected sensitivities. We further describe the capabilities of LDMX to explore other interesting new and standard physics, such as visibly-decaying axion and vector mediators or rare meson decays, and to provide timely electronuclear scattering measurements that will inform the modeling of neutrino-nucleus scattering for DUNE.
General Protocol for the Accurate Prediction of Molecular 13 C/ 1 H NMR Chemical Shifts via Machine Learning Augmented DFT
An accurate prediction of NMR chemical shifts at affordable computational cost is very important for different types of structural assignments in experimental studies. Density functional theory (DFT) and gauge-including atomic orbital (GIAO) are two of the most popular computational methods for NMR calculation, yet they often fail to resolve ambiguities in structural assignments. In this work, we present a new method that uses machine learning (ML) techniques (DFT + ML) that significantly increases the accuracy of 13 C/ 1 H NMR chemical shift prediction for a variety of organic molecules. The input of the generalizable DFT + ML model contains two critical parts: one is a vector providing insights into chemical environments, which can be evaluated without knowing the exact geometry of the molecule; the other one is the DFT-calculated isotropic shielding constant. The DFT + ML model was trained with a data set containing 476 13 C and 270 1 H experimental chemical shifts. For the DFT methods used here, the root mean square deviations (RMSDs) for the errors between predicted and experimental 13 C/ 1 H chemical shifts can be as small as 2.10/0.18 ppm, which is much lower than those from simple DFT (5.54/0.25 ppm), or DFT + linear regression (LR) (4.77/0.23 ppm) approaches. It also has a smaller maximum absolute error than two previously proposed NMR-predicting ML models. The robustness of the DFT + ML model is tested on two classes of organic molecules (TIC10 and hyacinthacines), where the correct isomers were unambiguously assigned to the experimental ones. Overall, the DFT + ML model shows promise for structural assignments in a variety of systems, including stereoisomers, that are often challenging to determine experimentally.