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At least 235 records · Page 13

Review: Real-space analyses of local dynamics in liquid using X-ray scattering

In this review, we advocate describing the liquid dynamics using the Van Hove correlation function, a temporal and spatial correlation function. Recent progress in X-ray optics and X-ray sources makes it possible to carry out inelastic and quasi-elastic x-ray scattering whose spectra can be converted into the dynamic correlation function in real space. We describe the basic concept of the Van Hove correlation function, how it is obtained from the X-ray scattering spectra using synchrotron X-rays and X-ray free electron lasers, and the atomic dynamics in water and aqueous salt solution.

Shinohara, Yuya↗

Parallel Quantum-Enhanced Sensing

Quantum metrology takes advantage of quantum correlations to enhance the sensitivity of sensors and measurement techniques beyond their fundamental classical limit, given by the shot-noise limit. The use of both temporal and spatial correlations present in quantum states of light can extend quantum-enhanced sensing to a parallel configuration that can simultaneously probe an array of sensors or independently measure multiple parameters. To this end, we use multispatial-mode bright twin beams of light, which are characterized by independent quantum-correlated spatial subregions in addition to quantum temporal correlations, to probe a four-sensor quadrant plasmonic array. We show that it is possible to independently and simultaneously measure local changes in refractive index for all four sensors with a quantum enhancement in sensitivity in the range of 22% to 24% over the corresponding classical configuration. Finally, these results provide a first step toward highly parallel spatially resolved quantum-enhanced sensing techniques and pave the way toward more complex quantum sensing and quantum imaging platforms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Correlation-Aided Robust Decentralized Dynamic State Estimation of Power Systems with Unknown Control Inputs

This paper proposes a correlation-aided robust adaptive unscented Kalman filter for power system decentralized dynamic state estimation with unknown inputs, termed as robust AUKF-UI. The temporal and spatial correlations among the unknown inputs are used to derive a vector auto-regressive (VAR) model in an adaptive manner. This VAR model is further integrated together with state transition and measurement models for joint state and unknown inputs estimation. This allows taking into account the implicit cross-correlations between the states and the unknown inputs. As a result, the rank requirement for unknown input vector estimation is relaxed and the local generator frequency measurement is not required. The temporal correlations of time series innovation vectors, predicted state and input vectors are also leveraged by the robust AUKFUI to detect, identify and process bad data. Without these correlations, it is very challenging to address bad data with unknown inputs. Simulation results carried out on the IEEE 39-bus system demonstrate that the proposed robust AUKF-UI achieves much better results than other methods in the presence of low measurement redundancy, strong nonlinearity, and bad data.

Zhao, Junbo↗

Using Ultrafast Entangled Photon Correlations to Measure the Temporal Evolution of Optically Excited Molecular Entanglement (Final Technical Report)

The goal of this project is to build an ultrafast entangled photon spectrometer and use it to measure theoretical predictions that photoexcited states are enhanced by spin-photon interactions. Entanglement of two states describes a specific type of quantum superposition in which measuring one state gives information about a second state. While entanglement is well explored for quantum information and computing systems, its effects on photoexcited states are less understood, especially in the ultrafast domains of molecular vibronic coupling. The grant designs a frequency and temporally resolved entangled photon spectrometer and uses it to test theoretical predictions like enhanced two photon absorption, non-reciprocal Fourier relations, and coupling of photons to spin systems. The end goal of the project is an understanding of how, when, and where entanglement is useful for spectroscopy.

47 OTHER INSTRUMENTATION↗

Spatial and Temporal Uncertainty of Crop Yield Aggregations

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Inter-comparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four datasets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia).Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05∗Corresponding (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.

Aggregation uncertainty↗

Error reduction in laser remote sensing - Combined effects of cross correlation and signal averaging

A systematic analysis is presented of the extent to which the accuracy of a differential-absorption lidar (DIAL) measurement may be improved by using the combined effects of signal averaging and temporal cross correlation. Previous studies which considered these effects separately are extended by incorporating both effects into a single analytical framework. In addition, experimental results involving lidar returns from a diffusely reflecting target using a dual-CO2 laser DIAL system with both heterodyne and direct detection are presented. These results are shown to be in good agreement with the theoretical analysis and help establish the limits of accuracy achievable under various experimental conditions.

Menyuk, N.↗

Sunspot Pattern Classification using PCA and Neural Networks (Poster)

The sunspot classification scheme presented in this paper is considered as a 2-D classification problem on archived datasets, and is not a real-time system. As a first step, it mirrors the Zuerich/McIntosh historical classification system and reproduces classification of sunspot patterns based on preprocessing and neural net training datasets. Ultimately, the project intends to move from more rudimentary schemes, to develop spatial-temporal-spectral classes derived by correlating spatial and temporal variations in various wavelengths to the brightness fluctuation spectrum of the sun in those wavelengths. Once the approach is generalized, then the focus will naturally move from a 2-D to an n-D classification, where "n" includes time and frequency. Here, the 2-D perspective refers both to the actual SOH0 Michelson Doppler Imager (MDI) images that are processed, but also refers to the fact that a 2-D matrix is created from each image during preprocessing. The 2-D matrix is the result of running Principal Component Analysis (PCA) over the selected dataset images, and the resulting matrices and their eigenvalues are the objects that are stored in a database, classified, and compared. These matrices are indexed according to the standard McIntosh classification scheme.

Rajkumar, T.↗

Unscreened forces in the quark-gluon plasma?

We study the correlator of temporal Wilson lines at nonzero temperature in 2+1 flavor lattice QCD with the aim to define the heavy quark-antiquark potential at nonzero temperature. For temperatures 153 MeV ≤ 𝑇 ≤ 352 MeV the spectral representation of this correlator is consistent with a broadened peak in the spectral function, position, or width of which then defines the real or imaginary parts of the heavy quark-antiquark potential at nonzero temperature, respectively. We find that the potential’s real part is not screened contrary to the widely held expectations. We comment on how this fact may modify the picture of quarkonium melting in the quark-gluon plasma.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Multi-qubit nanoscale sensing with entanglement as a resource

Nitrogen vacancy (NV) centres in diamond are widely deployed as local magnetic sensors, using single-qubit control to measure both time-averaged fields and noise with nanoscale spatial resolution. Moving beyond single qubits to multi-qubit control enables new sensing modalities such as measuring nonlocal spatiotemporal correlators or using entangled states to enhance measurement sensitivity. Here, in this work, we describe protocols for using optically unresolved NV centre pairs and nuclear spins as multi-qubit sensors for measuring correlated noise at nanometre length scales. For noninteracting NV centres, we implement a phase-cycling protocol that disambiguates magnetic correlations from variance fluctuations, leveraging the presence of a third qubit, a 13 C nucleus, to effect coherent single-NV spin flips and enable phase cycling even for co-aligned NV centres that are spectrally unresolved. For length scales around 10 nm, we create maximally entangled Bell states through dipole–dipole coupling between two NV centres and use these entangled states to directly read out the magnetic field correlation, rather than reconstructing it from independent measurements of unentangled NV centres. Importantly, this changes the scaling of sensitivity with readout noise from quadratic to linear. For conventional off-resonant readout of the NV centre spin state (for which the readout noise is roughly 30 times the quantum projection limit), this results in more than an order of magnitude improvement in sensitivity. Finally, we demonstrate methods for detecting high spatial- and temporal-resolution correlators with pairs of strongly interacting NV centres.

condensed-matter physics↗

Quantum Langevin theory for two coupled phase-conjugated electromagnetic waves

We provide a general macroscopic phenomenological formula of quantum Langevin equations for two coupled phase-conjugated electromagnetic fields with linear loss (gain) and complex nonlinear coupling coefficient. The macroscopic phenomenological formula is obtained from the coupling matrix to preserve the field commutation relations and correlations, which does not require knowing the microscopic details of light-matter interaction and internal atomic structures. To validate this phenomenological formula, we take spontaneous four-wave mixing in a double-Λ four-level atomic system as an example to numerically confirm that our macroscopic phenomenological result is consistent with that obtained from the microscopic Heisenberg-Langevin theory. We find that a complex-valued nonlinear coupling coefficient can lead to noises even without linear gain or loss. Lastly, we apply the quantum Langevin equations to study the effects of linear gain and loss, complex phase mismatching, as well as complex nonlinear coupling coefficient in entangled photon pair (biphoton) generation, particularly to their temporal quantum correlations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Tracking hurricane paths

The South East coastal region experiences hurricane threat for almost six months in every year. To improve the accuracy of hurricane forecasts, meteorologists would need the storm paths of both the present and the past. A hurricane path can be established if we could identify the correct position of the storm at different times right from its birth to the end. We propose a method based on both spatial and temporal image correlations to locate the position of a storm from satellite images. During the hurricane season, the satellite images of the Atlantic ocean near the equator are examined for the hurricane presence. This is accomplished in two steps. In the first step, only segments with more than a particular value of cloud cover are selected for analysis. Next, we apply image processing algorithms to test the presence of a hurricane eye in the segment. If the eye is found, the coordinate of the eye is recorded along with the time stamp of the segment. If the eye is not found, we examine adjacent segments for the existence of hurricane eye. It is probable that more than one hurricane eye could be found from different segments of the same period. Hence, the above process is repeated till the entire potential area for hurricane birth is exhausted. The subsequent/previous position of each hurricane eye will be searched in the appropriate adjacent segments of the next/previous period to mark the hurricane path. The temporal coherence and spatial coherence of the images are taken into account by our scheme in determining the segments and the associated periods required for analysis.

Prabhakaran, Nagarajan↗

Direct Evidence of Photoinduced Charge Transport Mechanism in 2D Conductive Metal Organic Frameworks

Conductive metal organic frameworks (MOFs) represent a promising class of porous crystalline materials that have demon-strated potential in photo-electronics and photocatalytic applications. However, the lack of fundamental understanding on charge transport (CT) mechanism as well as the correlation of CT mechanism with their structure hampered their further development. Herein, we report the direct evidence of CT mechanism in 2D Cu-THQ MOFs and the correlation of temporal and spatial behaviors of charge carriers with their photoconductivity by combining three advanced spectroscopic methods including time resolved optical and X-ray absorption spectroscopy and terahertz spectroscopy. In addition to Cu-THQ, the CT in Cu/Zn-THQ after incorporating Zn2+ guest metal was also examined to uncover the contribution of through space pathway as the presence of the redox inactive 3d10 Zn2+ is expected to perturb the long range in-plane CT. We show that the hot carriers in Cu-THQ generated after photoexcitation are highly mobile and undergo fast localization to a lower energy state (cool carriers) with electrons occupying Cu center and holes in ligands. The cool carriers, which have super long lifetime (> 17 ns) are responsible for the long-term photoconductivity in Cu-THQ and transport through O-Cu-O motif with negligible contribution from interlayer ligand π-π stacking as incorporation of Zn2+ in Cu-THQ significantly reduced photoconductivi-ty. These unprecedent results not only demonstrate the capability to experimentally probe CT mechanism but also provide important insight in the rational design of 2D MOFs for photoelectronic and photocatalytic applications.

14 SOLAR ENERGY↗

Machine learning for continuous quantum error correction on superconducting qubits

Abstract Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identify bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Implementation of a Time-domain Cosmic-Ray-Muon Tagger for the NEXUS Low-background Cryogenic Facility

Quantifying the effects of radiation on the operation of qubits both as quantum information systems as well as particle detectors has emerged as a pressing issue in quantum science in recent years. We present an overview of the design, operation, and deployment of a 90-$\mathrm{in}^2$ three-panel muon detector for use in the NEXUS experimental facility at Fermilab to temporally isolate correlated errors in qubits and determine if they possess an astrophysical origin. Constructed with three scintillator-attached PMTs read out with NIM modules in a triply-coincident logic scheme, we measure a surface-level muon luminosity of 9.7425 muons per second---consistent with an average surface-level cosmic-ray flux of approximately one muon per square centimeter per minute. Integrating the NIM modules with an MCC 128 DAQ HAT and Raspberry Pi, a Python script records exactly when a muon struck the detector and writes a timestamp to a log file for follow-up cross referencing. This experiment will broadly contribute to further studies aimed at understanding the source and mitigation of information loss in qubits.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

The roles of callose, elicitors and ethylene in thigmomorphogenesis and gravitropism

A correlation (both temporal and through the inhibitor, 2-deoxy-D-glucose) of callose deposition and ethylene evolution in mechanically perturbed (MP) bean or pine stems or in gravitationally stimulated corn shoots was demonstrated. It was suggested that the callose, which is deposited on the inside of the cell wall, and adjacent to the plasma membrane causes, in some way, the ethylene production. A hypothesis explaining the mechanism is discussed which states that there is a chemical activation of the enzyme system by the callose which is being deposited in apposition with it. Experimental data supporting the hypothesis are presented.

Jaffe, M. J.↗

A Coordinated RXTE/ASCA Study of Absorption Dips in Circinus X-1 at Phase Zero

This proposal was for an Rossi X Ray Timing Explorer (RXTE) study of Circinus X-1 with the ASCA satellite for the purpose of studying the iron emission feature during intensity dips. These dips had been discovered previously with ASCA and they appeared to vary in equivalent width and in the energy of the iron K edge. Proper analyses requires good continuum measurements which Rossi X Ray Timing Explorer/Proportional Counter Array (RXTE/PCA) could provide. Also, the PCA data would show any temporal evolution correlated with spectral evolution. RXTE provided 90 ks of data during March 3-5, 1998 which included phase zero which is where the dips usually take place. The ASCA observations were taken by Dr. Neil Brandt of Penn State University. Unfortunately, the source intensity showed far less dipping activity than is typical near phase zero. The light curve exhibited only very narrow sporadic dips rather than some long deep dips necessary for sufficient statistics in the ASCA and RXTE instruments for spectral studies. This prevented us from carrying out the primary goal of the proposal.

Bradt, Hale↗

A Regional CO2 Observing System Simulation Experiment Using ASCENDS Observations and WRF-STILT Footprints

Knowledge of the spatiotemporal variations in emissions and uptake of CO2 is hampered by sparse measurements. The recent advent of satellite measurements of CO2 concentrations is increasing the density of measurements, and the future mission ASCENDS (Active Sensing of CO2 Emissions over Nights, Days and Seasons) will provide even greater coverage and precision. Lagrangian atmospheric transport models run backward in time can quantify surface influences ("footprints") of diverse measurement platforms and are particularly well suited for inverse estimation of regional surface CO2 fluxes at high resolution based on satellite observations. We utilize the STILT Lagrangian particle dispersion model, driven by WRF meteorological fields at 40-km resolution, in a Bayesian synthesis inversion approach to quantify the ability of ASCENDS column CO2 observations to constrain fluxes at high resolution. This study focuses on land-based biospheric fluxes, whose uncertainties are especially large, in a domain encompassing North America. We present results based on realistic input fields for 2007. Pseudo-observation random errors are estimated from backscatter and optical depth measured by the CALIPSO satellite. We estimate a priori flux uncertainties based on output from the CASA-GFED (v.3) biosphere model and make simple assumptions about spatial and temporal error correlations. WRF-STILT footprints are convolved with candidate vertical weighting functions for ASCENDS. We find that at a horizontal flux resolution of 1 degree x 1 degree, ASCENDS observations are potentially able to reduce average weekly flux uncertainties by 0-8% in July, and 0-0.5% in January (assuming an error of 0.5 ppm at the Railroad Valley reference site). Aggregated to coarser resolutions, e.g. 5 degrees x 5 degrees, the uncertainty reductions are larger and more similar to those estimated in previous satellite data observing system simulation experiments.

Wang, James S.↗