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At least 235 records · Page 13

Testing the Formation Scenarios of Binary Neutron Star Systems with Measurements of the Neutron Star Moment of Inertia

Two low-mass (M < 1.4 M ⊙ ) neutron stars, J0737-3039B and the companion to J1756-2251, show strong evidence of being formed in an ultra-stripped supernova explosion (US-SN) with a ONeMg or Fe progenitor. In this paper, using systematically generated sets of equations of state we map out the relationship between the moment of inertia of J0737-3039A, a candidate for a moment of inertia measurement within a decade, and the binding energy of the two low-mass neutron stars. This relationship, similar to the I-Love-Q relations, is more robust than a previously explored correlation between the binding energy and the slope of the nuclear symmetry energy L. We find that, if either J0737-3039B or the J1756-2251 companion were formed in a US-SN, no more than 0.06 M ⊙ could have been lost from the progenitor core. Furthermore, a measurement of the moment of inertia of J0737-3039A to within 10% accuracy can discriminate between formation scenarios and, given current constraints on the predicted core mass loss, potentially rule them out. Advanced LIGO can potentially measure the neutron star tidal polarizability to equivalent accuracy which, using the I-Love-Q relations, would obtain similar constraints on the formation scenarios. Such information would help constrain important aspects of binary evolution used for population synthesis predictions of the rate of binary neutron star mergers and resulting electromagnetic and gravitational wave signals. Finally, further progress needs to be made in modeling the core-collapse process that leads to low-mass neutron stars, particularly in making robust predictions for the mass loss from the progenitor core.

79 ASTRONOMY AND ASTROPHYSICS↗

Bayesian inference for high confidence signal validation and sensor calibration assessment - 328

Online Monitoring (OLM) systems has been investigated for applications in the nuclear industry for the past two decades. These systems offer economic and technological advantages over the currently implemented time based testing methods for sensors and associated instrumentations. The advantages offered not only augment economic viability of current nuclear reactors but will be an inevitable change called for to suit the technologies of the advanced reactors (Gen III+). Irrespective, the acceptance of OLM into the regulatory structure in the U.S. is still hindered by technical and structural clarity on uncertainty quantification used by these systems. A Bayesian inference technique is implemented in the form of a residual model to quantify uncertainty in OLM predictions. The developed system used Gaussian processes to represent the OLM predictions and model inadequacy, the stationary representation of model based uncertainty. In addition, model selection techniques are used in conjunction with numerical methods such as Genetic Algorithm (GA) and Gibb's sampler to add robustness to the estimation model inadequacy, particularly in the presence of drifts and faulted conditions. The prediction model developed herein find applicability in fault detection, obtaining real values of processes after the onset of faults etc. Further development looks into the extension of current method for a multiple output scale implementation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Syndecan-4 shedding impairs macrovascular angiogenesis in diabetes mellitus

Purpose: Syndecan-4 (synd4) is a ubiquitous heparan sulfate proteoglycan cell surface receptor that modulates cell proliferation, migration, mechanotransduction, and endocytosis. The extracellular domain of synd4 sheds heavily in acute inflammation, but the shedding of synd4 in chronic inflammation, such as diabetes mellitus (DM), is still undefined. We investigated the alterations of synd4 endothelial expression in DM and the influence of impaired synd4 signaling on angiogenesis in human umbilical vein endothelial cells (HUVECs), diabetic rats, synd4 null mice, and db/db mice. Material and methods: HUVECs were incubated with advanced glycation end products (AGEs). Western blot analysis was used to determine synd4 protein expression and ELISA was used to detect soluble synd4 fragments. The concentration of synd4 in the aortic endothelia of diabetic rats was detected by immunohistochemical staining. Aortic ring assays were performed to study the process of angiogenesis in the diabetic rats and in synd4 null and db/db mice. Recombinant adenoviruses containing the synd4 gene or null were constructed to enhance synd4 aortic expression in db/db mice. Results: Western blot analysis showed decreased expression of the synd4 extracellular domain in HUVECs, and ELISA detected increased soluble fragments of synd4 in the media. Synd4 endothelial expression in the aortas of diabetic rats was decreased. Aortic ring assay indicated impaired angiogenesis in synd4 null and db/db mice, which was partially reversed by synd4 overexpression in db/db mice. Conclusion: Synd4 shedding from vascular endothelial cells played an important role in the diabetes-related impairment of angiogenesis. -- Highlights: •Synd4 shedding from endothelial cells is accelerated under the stimulation of AGEs. •Extracellular domain of synd4 is diminished in the endothelium of DM rats. •Aortic rings of synd4 null mice showed impaired angiogenesis. •Overexpression of synd4 partly rescues macrovascular angiogenesis in db/db mice.

60 APPLIED LIFE SCIENCES↗

Real-time atomic-resolution imaging of crystal growth process in water by phase modulation atomic force microscopy at one frame per second

Recent advancement in dynamic-mode atomic force microscopy (AFM) has enabled its operation in liquid with atomic-scale resolution. However, its imaging speed has often been too slow to visualize atomic-scale dynamic processes. Here, we propose a method for making a significant improvement in the operation speed of dynamic-mode AFM. In this method, we use a wideband and low-latency phase detector with an improved algorithm for the signal complexification. We demonstrate atomic-scale imaging of a calcite crystal growth process in water at one frame per second. The significant improvement in the imaging speed should enable various studies on unexplored atomic-scale interfacial processes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Admiralty Inlet Advanced Turbulence Measurements: May 2015

This data is from measurements at Admiralty Head, in Admiralty Inlet (Puget Sound) in May of 2015. The measurements were made using Inertial Motion Unit (IMU) equipped ADVs mounted on a 'StableMoor' (Manufacturer: DeepWater Buoyancy) buoy and a Tidal Turbulence Mooring (TTM). These platforms position ADV heads above the seafloor to make mid-depth turbulence measurements. The inertial measurements from the IMU allows for removal of mooring motion in post processing. The mooring and buoy motion has been removed from the stream-wise and vertical velocity signals (u, w). The lateral (v) velocity has some 'persistent motion contamination' due to mooring sway. The TTM was deployed with one ADV, it's position was: 48 09.145', -122 41.209' The StableMoor was deployed twice, the first time it was deployed in 'wing-mode' with two ADVs ('Port' and 'Star') at: 48 09.166', -122 41.173' The second StableMoor deployment was in 'Nose' mode with one ADV at: 48 09.166', -122 41.174' Units ----- - Velocity data (_u, urot, uacc) is in m/s. - Acceleration (Accel) data is in m/s^2. - Angular rate (AngRt) data is in rad/s. - The components of all vectors are in 'ENU' orientation. That is, the first index is True East, the second is True North, and the third is Up (vertical). - All other quantities are in the units defined in the Nortek Manual. Motion correction and rotation into the ENU earth reference frame was performed using the Python-based open source DOLfYN library (http://lkilcher.github.io/dolfyn/). Details on motion correction can be found there. Additional details on TTM measurements at this site can be found in the included Marine Energy Technology Symposium paper.

16 TIDAL AND WAVE POWER↗

Design Construction and Operation of a Supercritical Carbon Dioxide (sCO 2 ) Loop for Investigation of Dry Cooling and Natural Circulation Potential for Use in Advanced Small Modular Reactors Utilizing sCO 2 Power Conversion Cycles.

The second-generation particle size velocity (Parsivel2, Figure 1) disdrometer, or laser disdrometer, referred to as “LDIS” within the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) User Facility, is a modern, laser-based disdrometer designed for comprehensive and reliable measurement of all types of precipitation. LDIS captures detailed information on the size and velocity of individual hydrometeors that fall to the ground, classifying them into a range of 32 categories. These raw measurements are processed by a fast signal processor (provided by the vendor) to calculate various parameters, including precipitation type, amount, intensity, kinetic energy, visibility in the precipitation, and equivalent radar reflectivity. These measurements have served as the primary data set and a valuable reference for a variety of studies, ranging from observational analyses of precipitation processes and climate model evaluations to monitoring or validating radar/satellite retrievals (e.g., Wang et al. 2018, Giangrande et al. 2019, Jackson et al. 2020).

Middleton, Bobby D.↗

Securing Smart Manufacturing: Detection of Cyber-Physical Attacks in CNC-Based Systems

As Industry 4.0 advances, the integration of computer numerical control (CNC) machines and advanced manufacturing technologies is transforming production into smart manufacturing systems that blend physical and digital processes as cyber-physical systems. However, this increased cyber-physical connectivity exposes manufacturing systems to cyber threats that can cause severe operational and financial disruptions. This paper presents a comparative study on cyber attacks and anomaly detection techniques in manufacturing, focusing on network traffic from CNC machines. The data extracted from network packets includes machine commands and control signals exchanged between the machine's interface and control system, crucial for maintaining operational integrity. We explore two types of cyber attacks, design modification and command injection, which pose substantial risks to CNC machine productivity and system integrity. Our investigation involves experiments on a real CNC system, highlighting the urgent need for effective detection mechanisms. To address these threats, we evaluate three anomaly detection methods: dynamic time warping (DTW), rolling average, and a deep learning, long short-term memory (LSTM) time-series-based autoencoder. Each is assessed for its effectiveness in identifying anomalous behaviors caused by the attacks. Our findings demonstrate the unique strengths and limitations of each detection technique, providing a deeper understanding of their applicability in realworld manufacturing environments. The comparative analysis indicates that while certain methods are highly effective against specific attack types, others offer broader applicability across different attacks. This study contributes to the accurate detection of anomalies in CNC machining processes, thereby enhancing the reliability and security of smart manufacturing systems against diverse cyber threats.

Williams, Bethanie [Tennessee Technological Univer↗

Spatial and temporal dynamics of receptor for advanced glycation endproducts, integrins, and actin cytoskeleton as probed with fluorescence-based imaging techniques

Systematic spatial and temporal fluctuations are a fundamental part of any biological process. For example, lateral diffusion of membrane proteins is one of the key mechanisms in their cellular function. Lateral diffusion governs how membrane proteins interact with intracellular, transmembrane, and extracellular components to achieve their function. Herein, fluorescence-based techniques are used to elucidate the dynamics of receptor for advanced glycation end-products (RAGE) and integrin membrane proteins. RAGE is a transmembrane protein that is being used as a biomarker for various diseases. RAGE dependent signaling in numerous pathological conditions is well studied. However, RAGE lateral diffusion in the cell membrane is poorly understood. For this purpose, effect of cholesterol, cytoskeleton dynamics, and presence of ligand on RAGE lateral diffusion is investigated.

59 BASIC BIOLOGICAL SCIENCES↗

Novel Cell-Type-Specific Drought-Responsive Proteins in Root Tips of Field-Grown Perennial Switchgrass

The root-tip region of plants, including the root cap, forms the most basal terminal of the root and exhibits a high degree of cellular complexity in terms of morphology, cytological function, and interaction with environmental cues in the soil. Cells in this region follow a developmental trajectory, transitioning from stem cells to meristematic cells, and ultimately to fully differentiated cell types. However, our understanding of root-tip cell-type specific proteomic responses to abiotic stresses, such as drought, particularly under field conditions, remains limited. This study aimed to identify spatially resolved, cell type-specific proteomes in switchgrass (Panicum virgatum) root tips under drought stress. Root tips were collected from seven-year-old, field-grown switchgrass ‘Alamo’ plants excavated under both well-watered and long-term drought conditions. Cell type-specific proteins were identified using laser capture microdissection (LCM) coupled with nanoPOTS (Nanodroplet Processing in One Pot for Trace Samples) and nano-LC-MS proteomics analysis. Five distinct cell types were targeted: (1) cells in the quiescent center and stem cell niche (QuC), (2) protodermal epidermal cells (PEC) in the meristematic zone, (3) epidermal cells in the transition and elongation zones above the root cap (Epi), (4) peripheral root cap cells (PRC), forming 2–3 layers below the PEC and 1–2 layers above the root border cells, and (5) columella root cap cells (Col) comprising of the columella initials and a single underlying layer of cells undergoing active growth. Principal component analysis (PCA) revealed clear separation among the five targeted cell types, confirming distinct proteomic profiles. Proteins predominantly enriched in each cell type were linked to distinct cellular functions, with QuC cells showing involvement in chromosomal behavior, DNA replication, and mitosis—key processes for stem cell niche regulation. Drought stress resulted in alterations of proteostasis, as evidenced by significant decreases in ribosomal proteins and increases in protein synthesis inhibitors. Moreover, drought stress induced unique cell-type–specific proteins involved in phytohormone biosynthesis and signaling pathways, including auxin, cytokinin, and jasmonic acid. In particular, QuC cells were more highly enriched in proteins associated with DNA repair and mitotic processes. Metabolic pathways related to amino acids, carbohydrates, and lipids were differentially affected in a cell-type–dependent manner, whereas general stress-responsive proteins exhibited consistent changes across all five cell types. Overall, this study provides unique spatially resolved, cell-type-specific proteomic profiles in root tips, representing a significant advancement in our understanding of the cellular mechanisms underlying plant responses to drought stress in natural field conditions.

perennial grass↗

Condition Monitoring of Wind Turbine Planetary Gearboxes Under Different Operating Conditions

Digitally enhanced services for wind power could reduce Operations and Maintenance (O&M) costs as well as the Levelised Cost Of Energy (LCOE). Therefore, there is a continuous need for advanced monitoring techniques which can exploit the opportunities of Internet of Things (IoT) and Big Data Analytics, revolutionizing the future of the energy sector. The heart of wind turbines is a rather complex epicyclic gearbox. Among others, extremely critical gearbox components which are often responsible for machinery stops are the rolling element bearings. The vibration signatures of bearings are rather weak compared to other components, such as gears, and as a result an extended number of signal processing techniques and tools have been proposed during the last decades, focusing towards accurate, early, and on time bearing fault detection with limited false alarms and missed detections. Envelope Analysis is one of the most important methodologies, where an envelope of the vibration signal is estimated usually after filtering around a frequency band excited by impacts due to the bearing faults. Different tools, such as Kurtogram, have been proposed in order to accurately select the optimum filter parameters (center frequency and bandwidth). Cyclic Spectral Correlation and Cyclic Spectral Coherence, based on Cyclostationary Analysis, have been proved as very powerful tools for condition monitoring. The monitoring techniques seem to have reached a mature level in case a machinery operates under steady speed and load. On the other hand, in case the operating conditions change, it is still unclear whether the change of the monitoring indicators is due to the change of the health of the machinery or due to the change of the operating parameters. Recently, the authors have proposed a new tool called IESFOgram, which is based on Cyclic Spectral Coherence and can automatically select the filtering band. Furthermore, the Cyclic Spectral Coherence is integrated along the selected frequency band leading to an Improved Envelope Spectrum. In this paper, the performance of the tool is evaluated and further extended on cases operating under different speeds and different loads. The effectiveness of the methodology is tested and validated on the National Renewable Energy Laboratory (NREL) wind turbine gearbox vibration condition monitoring benchmarking data set which includes various faults with different levels of diagnostic complexity as well as various speed and load operating conditions.

17 WIND ENERGY↗

In-situ sensor monitoring of multi-class gas porosity formation in laser powder bed fusion using convolutional neural network

In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79% accuracy and an Area Under the Receiver Operating Characteristic curve (AUC ROC) score of 0.89 with five-fold cross-validation. The results demonstrate that coupling CWT-based feature engineering with CNN architecture enables reliable multi-class pore detection in Al6061 builds using affordable in-situ sensors. This approach advances scalable and affordable quality assurance in additive manufacturing by moving beyond binary defect detection toward more nuanced classification of porosity mechanisms with in-situ sensors and machine learning.

Laser powder bed fusion, Multi-class pores, In-sit↗

Magnetic defects in chemically converted graphene nanoribbons: electron spin resonance investigation

Electronic spin transport properties of graphene nanoribbons (GNRs) are influenced by the presence of adatoms, adsorbates and edge functionalization. To improve the understanding of the factors that influence the spin properties of GNRs, local (element) spin-sensitive techniques such as electron spin resonance (ESR) spectroscopy are important for spintronics applications. Here, we present results of multi-frequency continuous wave (CW), pulse and hyperfine sublevel correlation (HYSCORE) ESR spectroscopy measurements performed on oxidatively unzipped graphene nanoribbons (GNRs), which were subsequently chemically converted (CCGNRs) with hydrazine. ESR spectra at 336 GHz reveal an isotropic ESR signal from the CCGNRs, of which the temperature dependence of its line width indicates the presence of localized unpaired electronic states. Upon functionalization of CCGNRs with 4-nitrobenzene diazonium tetrafluoroborate, the ESR signal is found to be 2 times narrower than that of pristine ribbons. NH{sub 3} adsorption/desorption on CCGNRs is shown to narrow the signal, while retaining the signal intensity and g value. The electron spin-spin relaxation process at 10 K is found to be characterized by slow (163 ns) and fast (39 ns) components. HYSCORE ESR data demonstrate the explicit presence of protons and {sup 13}C atoms. With the provided identification of intrinsic point magnetic defects such as proton and {sup 13}C has been reported, which are roadblocks to spin travel in graphene-based materials, this work could help in advancing the present fundamental understanding on the edge-spin (or magnetic)-based transport properties of CCGNRs.

36 MATERIALS SCIENCE↗

Simultaneous Optical and Electrical Spin-Torque Magnetometry with Phase-sensitive Detection of Spin Precession

Spin-based coherent information processing and encoding utilize the precession phase of spins in magnetic materials. However, the detection and manipulation of spin precession phases remain a major challenge for advanced spintronic functionalities. By using simultaneous electrical and optical detection, we demonstrate the direct measurement of the spin precession phase of a permalloy device driven by the spin-orbit torques from adjacent heavy metals. The spin Hall angle of the heavy metals can be independently determined from concurrent electrical and optical signals. The phase-sensitive optical detection also allows spatially-resolved measurements of local spin-torque parameters and ferromagnetic resonance with comprehensive amplitude and phase information. Here, our study offers a route toward future advanced characterizations of spin-torque oscillators, magnonic circuits, and tunneling junctions, where measuring the current-induced spin dynamics of individual nanomagnets is required.

36 MATERIALS SCIENCE↗

Use of Convolutional Neural Network Image Classification and High-Speed Ion Probe Data Toward Real-Time Detonation Characterization in a Water-Cooled Rotating Detonation Engine

As rotating detonation engines (RDEs) progress in maturity, the importance of monitoring advancements toward development of active control becomes more critical. Experimental RDE data processing at time scales which satisfy real-time diagnostics will likely require the use of machine learning. This study aims to develop and deploy a novel real-time monitoring technique capable of determining detonation wave number, direction, frequency, and individual wave speeds throughout experimental RDE operational windows. To do so, the diagnostic integrates image classification by a convolutional neural network (CNN) and ionization current signal analysis. Wave mode identification through single-image CNN classification bypasses the need to evaluate sequential images and offers instantaneous identification of the wave mode present in the RDE annulus. Here, real-time processing speeds are achieved due to low data volumes required by the methodology, namely one short-exposure image and a short window of sensor data to generate each diagnostic output. The diagnostic acquires live data using a modified experimental setup alongside Pylon and PyDAQmx libraries within a python data acquisition environment. Lab-deployed diagnostic results are presented across varying wave modes, operating conditions, and data quality, currently executed at 3–4 Hz with a variety of iteration speed optimization options to be considered as future work. These speeds exceed that of conventional techniques and offer a proven structure for real-time RDE monitoring. The demonstrated ability to analyze detonation wave presence and behavior during RDE operation will certainly play a vital role in the development of RDE active control, necessary for RDE technology maturation toward industrial integration.

42 ENGINEERING↗

Second Annual Report on Development of Microwave Resonant Cavity Transducer for Fluid Flow Sensing

We are investigating a microwave resonant cavity transducer for flow sensing in the vessel of a high temperature fluid advanced reactor (AR), such as a molten salt cooled reactor (MSCR) or a sodium fast reactor (SFR). This transducer is a hollow metallic cylindrical cavity, with the flat wall of the cylinder flexible enough to undergo microscopic deflection due to dynamic fluid pressure. Membrane deflection leads to a shift in the resonant frequency, which can be detected with a spectrum analyzer. Because the transducer is intended for immersion in a high temperature corrosive fluid, understanding of material degradation is crucial for estimation of transducer performance lifetime, and development of measurement interpretation algorithms. We conducted a preliminary computational investigation of relevant damage mechanisms of a stainless steel 316 cylindrical resonator in FLiBe salt. The two main damage mechanisms, creep and corrosion, were modeled using multiphysics COMSOL software. Degradation was modeled for a temperature range 500°C to 700°C. Coupling of the damage mechanisms was not considered. These models predict significant inelastic deformation at most temperatures due to creep, and qualitatively predict chromium depletion both along the liquid/solid interface and along the grain boundaries. An algorithmic approach for compensation of these degradation effects during fluid flow measurements will be developed in the future work. To validate sensor physics, we have performed proof-of-principle test of flow sensing in water. For this test, we have developed a cylindrical resonator for K-band, which was machined from brass. The cavity was excited through WR-42 waveguide through a subwavelength hole on the side of the wall of the cylinder. To increase the spectral signal visibility, we developed a signal processing method for baseline subtraction. A flow loop for proof-of-principle test of transducer performance in water was assembled. A commercial flow meter was installed in the loop for reference measurements. Cylindrical cavity was excited in the TE 011 mode with resonant frequency f ≈ 17.8GHz. Frequency shift of cavity spectral response was obtained by gradually increasing water flow rate from 0 to 60gpm. Corresponding monotonic increase of resonant frequency shift by several MHz was observed. Approximate figure of merit of sensitivity to flow rate is 100KHz/GPM. In addition, we have identified an existing liquid sodium experimental setup for demonstration of flow sensing in environment similar to that of an advanced reactor. The setup consists of a cylindrical vessel and center feed line, where transducer inserted through the lid will measure velocity of the impinging liquid jet. As a calibration experiment, we have assembled a water vessel with center feed with the same dimensions as those of the liquid sodium setup. We have also developed and insertion probe consisting of a 50cm brass waveguide enclosed in protective SS316 tube. Using the water loop, we have demonstrated feasibility of sensing the impinging liquid jet in the vessel.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Macromolecular crystallography with a large format CMOS detector

Recent advances in CMOS technology have allowed the production of large surface area detectors suitable for macromolecular crystallography experiments [1]. The Molecular Biology Consortium (MBC) Beamline 4.2.2 at the Advanced Light Source in Berkeley, CA, has installed a 2952 x 2820 mm RDI CMOS-8M detector with funds from NIH grant S10OD012073. The detector has a 20nsec dead pixel time and performs well with shutterless data collection strategies. The sensor obtains sharp point response and minimal optical distortion by use of a thin fiber-optic plate between the phosphor and sensor module. Shutterless data collections produce high-quality redundant datasets that can be obtained in minutes. The fine-sliced data are suitable for processing in standard crystallographic software packages (XDS, HKL2000, D*TREK, MOSFLM). Faster collection times relative to the previous CCD detector have resulted in a record number of datasets collected in a calendar year and de novo phasing experiments have resulted in publications in both Science and Nature [2,3]. The faster collections are due to a combination of the decreased overhead requirements of shutterless collections combined with exposure times that have decreased by over a factor of 2 for images with comparable signal to noise of the NOIR-1 detector. The overall increased productivity has allowed the development of new beamline capabilities and data collection strategies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Precise predictions for $V+$jets dark matter backgrounds

High-energy jets recoiling against missing transverse energy (MET) are powerful probes of dark matter at the LHC. Searches based on large MET signatures require a precise control of the $Z(\nu\bar\nu)+$jet background in the signal region. This can be achieved by taking accurate data in control regions dominated by $Z(\ell^+\ell^-)+$jet, $W(\ell\nu)+$jet and $\gamma+$jet production, and extrapolating to the $Z(\nu\bar\nu)+$jet background by means of precise theoretical predictions. In this context, recent advances in perturbative calculations open the door to significant sensitivity improvements in dark matter searches. In this spirit, we present a combination of state-of-the art calculations for all relevant $V+$jets processes, including throughout NNLO QCD corrections and NLO electroweak corrections supplemented by Sudakov logarithms at two loops. Predictions at parton level are provided together with detailed recommendations for their usage in experimental analyses based on the reweighting of Monte Carlo samples. Particular attention is devoted to the estimate of theoretical uncertainties in the framework of dark matter searches, where subtle aspects such as correlations across different $V+$jet processes play a key role. In conclusion, the anticipated theoretical uncertainty in the $Z(\nu\bar\nu)+$jet background is at the few percent level up to the TeV range.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

On-Line Monitoring of Gas-Phase Molecular Iodine Using Raman and Fluorescence Spectroscopy Paired with Chemometric Analysis

Molten salt reactors (MSRs) have the potential to safely support green energy goals. However, licensing and deployment of these systems will be aided through development of new technology. This includes on-line monitoring tools for real-time compositional analysis. Of particular interest is quantifying iodine within reactor off-gas streams to support design and operational control of reactor off-gas treatment systems. Here we discuss the development of advanced Raman spectroscopy systems for the on-line analysis of I 2(g) within the gas phase. Signal response is explored with two Raman instruments utilizing a 532 nm and a 671 nm excitation source, as a function of I 2(g) pressure and temperature. Furthermore, the applicability of chemometric modeling for advanced analysis of data is explored. Raman spectroscopy paired with chemometric analysis is demonstrated to be a powerful route to analyzing I 2(g) composition within the gas phase, which lays the foundation for applications within molten salt reactor off-gas analysis and other significant chemical processes producing iodine species.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗