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Four-dimensional phase space tomography from one-dimensional measurements of a hadron beam
In this paper, we use one-dimensional measurements to infer the four-dimensional phase space density of an accumulated proton beam in the Spallation Neutron Source (SNS) accelerator. The reconstruction was performed by maximizing the distribution’s entropy subject to the measurement constraints and thus represents the most conservative inference from the data. The reconstructed distribution reproduces the measured profiles down to the noise level, and simulations indicate that the problem is reasonably well constrained. Similar measurements could serve as benchmarks for beam dynamics simulations in the SNS or hadron accelerators.
Wave Measurements Using Multi-Frame Processing of Marine Radar Data
Marine radars have proven to be useful for measuring ocean waves, but the accuracy of the measurements is limited by several factors including the look-angle dependence of the radar signals as well as noise in the radar data. The look-angle dependence introduces a systematic error or bias in the measurements, and noise causes a random error. This paper describes a method of combining data from multiple radar frames that is optimal in the sense of minimizing the error for a set of biased measurements with random additive noise. The results are shown experimentally to increase the correlation of the radar estimates with buoy measurements.
Distributed phase sensing using two-mode squeezed states in a truncated SU(1,1) interferometer
We demonstrate distributed phase sensing with a truncated SU(1,1) interferometer through the measurement of a linear combination of two phases distributed between the two beams of a two-mode squeezed state. We theoretically analyze the sensitivity enhancement with respect to the corresponding classical strategy and experimentally demonstrate a 2 dB quantum noise reduction in the measurement of a linear combination of two phases.
Intelligent Agents for the Optimization of Atomic Layer Deposition
Atomic layer deposition (ALD) is a highly controllable thin film synthesis approach with applications in computing, energy, and separations. The flexibility of ALD means that it can access a massive chemical catalogue; however, this chemical and process diversity results in significant challenges in determining processing parameters that result in stable and uniform film growth with minimal precursor consumption. In situ measurements of the ALD growth per cycle (GPC) can accelerate process development but it still requires expert intuition and time-consuming trial and error to identify acceptable processing parameters. This procedure is made more difficult by the presence of experimental noise in the GPC values and the complexity of ALD surface chemistries. Here, a need exists for efficient optimization approaches capable of autonomously determining processing conditions resulting in optimal ALD film growth. In this work, we present the development of three optimization strategies and compare their performance in optimizing four simulated ALD processes. Furthermore, the effect of noise in the GPC measurements on optimization convergence is studied.
Highly sensitive 2D X-ray absorption spectroscopy via physics informed machine learning
Abstract Improving the spatial and spectral resolution of 2D X-ray near-edge absorption structure (XANES) has been a decade-long pursuit to probe local chemical reactions at the nanoscale. However, the poor signal-to-noise ratio in the measured images poses significant challenges in quantitative analysis, especially when the element of interest is at a low concentration. In this work, we developed a post-imaging processing method using deep neural network to reliably improve the signal-to-noise ratio in the XANES images. The proposed neural network model could be trained to adapt to new datasets by incorporating the physical features inherent in the latent space of the XANES images and self-supervised to detect new features in the images and achieve self-consistency. Two examples are presented in this work to illustrate the model’s robustness in determining the valence states of Ni and Co in the LiNi x Mn y Co 1-x-y O 2 systems with high confidence.
Probing many-body dynamics in a two-dimensional dipolar spin ensemble
Abstract The most direct approach for characterizing the quantum dynamics of a strongly interacting system is to measure the time evolution of its full many-body state. Despite the conceptual simplicity of this approach, it quickly becomes intractable as the system size grows. An alternate approach is to think of the many-body dynamics as generating noise, which can be measured by the decoherence of a probe qubit. Here we investigate what the decoherence dynamics of such a probe tells us about the many-body system. In particular, we utilize optically addressable probe spins to experimentally characterize both static and dynamical properties of strongly interacting magnetic dipoles. Our experimental platform consists of two types of spin defects in nitrogen delta-doped diamond: nitrogen-vacancy colour centres, which we use as probe spins, and a many-body ensemble of substitutional nitrogen impurities. We demonstrate that the many-body system’s dimensionality, dynamics and disorder are naturally encoded in the probe spins’ decoherence profile. Furthermore, we obtain direct control over the spectral properties of the many-body system, with potential applications in quantum sensing and simulation.
Transactive Electric Water Heater Agent: Design and Performance Evaluation
Electric water heaters (EWHs) are usually equipped with inbuilt thermostats to measure water temperature near the installed positions. Most of the existing EWHs have only two thermostats and often do not have water flow sensor installed. Estimation of state of heat energy (SOHE) inside the hot water tank, which is crucial for efficient control and operation of the EWH, under such imperfect system conditions is very challenging. This paper, therefore, designs a transactive EWH agent (TEWHA) to better estimate SOHE in presence of measurement errors and imperfect system knowledge. The TEWHA, built-in Python, approximates the EWH physics using limited measurements obtained from the ground truth GridLABD model. The performance of the TEWHA is validated against ground truth system for three scenarios: a) ideal condition where TEWHA has complete knowledge of the system, b) imperfect condition where TEWHA has incomplete system information, and c) noised condition where TEWHA measurements contain random errors. Finally, an optimization problem is formulated and solved to demonstrate how TEWHA could use the estimated and measured parameters for a transactive control of EWH. Index Terms—Demand flexibility
Facile Measurement of the Rotation of a Single Optically Trapped Nanoparticle Using the Diagonal Ratio of a Quadrant Photodiode
Optical tweezers are a powerful tool for exploring physical properties of particles in various environments through analysis of their dynamics in a trapping potential. Analysis of the trapped particle's position is often done using interferometric back-focal-plane detection microscopy in which interference between the trapping laser and the light interacting with the trapped particle is projected on a quadrant photo-diode (QPD). The QPD measurements are almost universally the voltage ratios between the transverse components of the detector (left/right; up/down) that are calibrated to ascertain particle positions and orientations (i.e., rotation). In this paper, we demonstrate how measuring the diagonal ratio of the QPD allows tracking the spinning and orbital motion of optically trapped objects with enhanced sensitivity compared to the conventional transverse ratio and reveals additional information about particle dynamics. The diagonal ratio "balances" the signal from opposite sides of the QPD, allowing common mode noise reduction and increased measurement sensitivity of the rotating nanospheres at frequencies from below 200 Hz to above 15 kHz. We show that the variability in particle rotation rates is due to slight differences in the aspect ratio of the nanoparticles. We also measure in situ a gradual acceleration in the spinning frequency of trapped nanoparticles due to photothermal shedding of surface ligands and the resulting decrease of rotational friction in solution. The accelerating particles spin at rates exceeding 30 kHz and are eventually "optically printed" onto the glass coverslip on top of the liquid cell enclosure. The diagonal ratio is a simple, readily available, and powerful enabler for measuring rotational dynamics with a 5-fold sensitivity increase over conventional QPD measurements. This affords opportunities for controlling the orientational dynamics of trapped nanoparticles, which is crucial for various applications, including nanoscale mechanical motors.
On the measurement of hardness at high strain rates by nanoindentation impact testing
Recent advances in electronics have enabled nanomechanical measurements with very low noise, fast time constants and high data acquisition rates. Furthermore, these capabilities open the door for a wide range of ultra-fast nanomechanical testing. Given the inherent dynamic nature of high-speed testing, a thorough understanding of the testing system's dynamics and electronics is extremely important for accurate measurements. In this work, an analytical framework that includes the mechanical and electronic contributions of the instrument and the material constitutive response is presented to provide guidelines for performing high strain rate measurements of hardness by nanoindentation testing. Simple closed-form solutions that provide insights on the choice of test methodology, test parameters and instrument design are presented along with the strain rate range over which accurate measurements can be performed with the commercially available nanoindenters.
Topological Defect Engineering and $\mathcal{PT}$ Symmetry in Non-Hermitian Electrical Circuits
In this work, we employ electric circuit networks to study topological states of matter in non-Hermitian systems enriched by parity-time symmetry $\mathcal{PT}$ and chiral symmetry anti-$\mathcal{PT (APT)}$. The topological structure manifests itself in the complex admittance bands which yields excellent measurability and signal to noise ratio. We analyze the impact of $\mathcal{PT}$-symmetric gain and loss on localized edge and defect states in a non-Hermitian Su-Schrieffer-Heeger (SSH) circuit. We realize all three symmetry phases of the system, including the $\mathcal{APT}$-symmetric regime that occurs at large gain and loss. We measure the admittance spectrum and eigenstates for arbitrary boundary conditions, which allows us to resolve not only topological edge states, but also a novel $\mathcal{PT}$-symmetric $\mathbb{Z}_2$ invariant of the bulk. We discover the distinct properties of topological edge states and defect states in the phase diagram. In the regime that is not $\mathcal{PT}$ symmetric, the topological defect state disappears and only reemerges when $\mathcal{APT}$ symmetry is reached, while the topological edge states always prevail and only experience a shift in eigenvalue. Our findings unveil a future route for topological defect engineering and tuning in non-Hermitian systems of arbitrary dimension.
High-fidelity retrieval from instantaneous line-of-sight returns of nacelle-mounted lidar including supervised machine learning
Abstract. Wind turbine applications that leverage nacelle-mounted Doppler lidar are hampered by several sources of uncertainty in the lidar measurement, affecting both bias and random errors. Two problems encountered especially for nacelle-mounted lidar are solid interference due to intersection of the line of sight with solid objects behind, within, or in front of the measurement volume and spectral noise due primarily to limited photon capture. These two uncertainties, especially that due to solid interference, can be reduced with high-fidelity retrieval techniques (i.e., including both quality assurance/quality control and subsequent parameter estimation). Our work compares three such techniques, including conventional thresholding, advanced filtering, and a novel application of supervised machine learning with ensemble neural networks, based on their ability to reduce uncertainty introduced by the two observed nonideal spectral features while keeping data availability high. The approach leverages data from a field experiment involving a continuous-wave (CW) SpinnerLidar from the Technical University of Denmark (DTU) that provided scans of a wide range of flows both unwaked and waked by a field turbine. Independent measurements from an adjacent meteorological tower within the sampling volume permit experimental validation of the instantaneous velocity uncertainty remaining after retrieval that stems from solid interference and strong spectral noise, which is a validation that has not been performed previously. All three methods perform similarly for non-interfered returns, but the advanced filtering and machine learning techniques perform better when solid interference is present, which allows them to produce overall standard deviations of error between 0.2 and 0.3 m s−1, or a 1 %–22 % improvement versus the conventional thresholding technique, over the rotor height for the unwaked cases. Between the two improved techniques, the advanced filtering produces 3.5 % higher overall data availability, while the machine learning offers a faster runtime (i.e., ∼ 1 s to evaluate) that is therefore more commensurate with the requirements of real-time turbine control. The retrieval techniques are described in terms of application to CW lidar, though they are also relevant to pulsed lidar. Previous work by the authors (Brown and Herges, 2020) explored a novel attempt to quantify uncertainty in the output of a high-fidelity lidar retrieval technique using simulated lidar returns; this article provides true uncertainty quantification versus independent measurement and does so for three techniques rather than one.
Accurate numerical simulations of open quantum systems using spectral tensor trains
Decoherence between qubits is a major bottleneck in quantum computations. Decoherence results from intrinsic quantum and thermal fluctuations as well as noise in the external fields that perform the measurement and preparation processes. With prescribed colored noise spectra for intrinsic and extrinsic noise, we present a numerical method, Quantum Accelerated Stochastic Propagator Evaluation (Q-ASPEN), to solve the time-dependent noise-averaged reduced density matrix in the presence of intrinsic and extrinsic noise. Q-ASPEN is arbitrarily accurate and can be applied to provide estimates for the resources needed to error-correct quantum computations. We employ spectral tensor trains, which combine the advantages of tensor networks and pseudospectral methods, as a variational ansatz to the quantum relaxation problem and optimize the ansatz using methods typically used to train neural networks. Here, the spectral tensor trains in Q-ASPEN make accurate calculations with tens of quantum levels feasible. We present benchmarks for Q-ASPEN on the spin-boson model in the presence of intrinsic noise and on a quantum chain of up to 32 sites in the presence of extrinsic noise. In our benchmark, the memory cost of Q-ASPEN scales as a low-order polynomial in the size of the system once the number of system states surpasses the number of basis functions used in the spectral expansion.
Determining Stress Orientation in Rock Valley, Nevada, Using Ambient Seismic Noise
The stress field and the mechanical properties of rocks are important to consider for nuclear explosion monitoring due to their effect on seismic wave radiation from earthquakes and explosions. At the Rock Valley Direct Comparison site, the regional orientation of the maximum horizontal compressive stress (SH max ) is well constrained, but it is unknown whether there are local heterogeneities. Here, I show that stress-induced anisotropy in nonlinear elasticity can be used to estimate the orientation of SH max . Rocks have compliant internal contacts, such as fractures and mineral grain boundaries, that respond to applied strains more strongly than individual mineral crystals. This strain response is asymmetric between compressive and dilatational strains and is affected by anisotropy in the ambient stress field. Traditional seismic velocity measurements are of strain-averaged velocity, which is less sensitive than nonlinear elasticity to the behavior of compliant internal contacts and therefore the stress field and fracture behavior. My results show that the orientation of SH max measured using ambient seismic noise aligns with regional estimates, with some heterogeneity that can also be explained by the limitations of the seismic array. I demonstrate the potential of a passive technique for monitoring the stress field in places that currently lack local measurements.
Low-Frequency Electronic Noise in Quasi-2D van der Waals Antiferromagnetic Semiconductor FePS 3 —Signatures of Phase Transitions
Here, low-frequency current fluctuations, i.e., noise, in the quasi-2D van der Waals antiferromagnetic semiconductor FePS 3 with the electronic bandgap of 1.5 eV is investigated. The electrical and noise characteristics of the p-type, highly resistive, thin films of FePS 3 are measured at different temperatures. The noise spectral density is of the 1/f-type over most of the examined temperature range but reveals well-defined Lorentzian bulges, and increases strongly near the Néel temperature T N =118 K (f is the frequency). Intriguingly, the noise spectral density attains its minimum at temperature T≈200 K, which is attributed to an interplay of two opposite trends in noise scaling—one for semiconductors and another for materials with the phase transitions. The Lorentzian corner frequencies reveal unusual dependence on temperature and bias voltage, suggesting that their origin is different from the generation—recombination noise in conventional semiconductors. The obtained results are important for proposed applications of antiferromagnetic semiconductors in spintronic devices. They also attest to the power of the noise spectroscopy for monitoring various phase transitions.
Infrared photon-number-resolving imager using a Skipper-CCD
Imaging in a broad light-intensity regime with a high signal-to-noise ratio is a key capability in fields as diverse as Quantum Metrology and Astronomy. Achieving high signal-to-noise ratios in quantum imaging leads to surpassing the classical limit in parameter estimation. In astronomical detection, the search for habitable exoplanets demands imaging in the infrared its atmospheres looking for biosignatures. These optical applications are hampered by detection noise, which critically limits their potential, and thus demands photon-number and spatial resolution detectors. Here we report an imaging device in the infrared wavelength range able to arbitrarily reduce the readout noise. We built a Measured Exposure Skipper-CCD Sensor Instrument equipped with a thick back-illuminated sensor, with photon-number-resolving capability in a wide dynamic range, spatial resolution, high quantum efficiency in the near-infrared and ultra-low dark counts. This device allows us to image objects in a broad range of intensities within the same frame and, by reducing the readout noise to less than 0.2e$^-$, to distinguish even those shapes with less than two photons per pixel, unveiling what was previously hidden in the noise. These results pave the way for building high-standard infrared imagers based on Skipper-CCDs.
Spatially resolved fibre cavity ring down spectroscopy
Abstract This paper presents a fibre cavity ring down spectroscopy probed by Rayleigh scattering optical frequency domain reflectometry (OFDR), which provides spatial location of stimuli and improved signal to noise ratio for distributed sensing measurements. A section of optical fibre was integrated into an active fibre ring cavity with optical gain and interrogated by the OFDR system for 11 cycles with a single laser scan. Through the cavity ring down configuration, root-mean-squared (RMS) noise of distributed temperature and strain measurements was reduced to 6.9 mK and less than 0.1 με, respectively for 1-cm spatially resolved measurements. Our work shows that the active fibre cavity configuration can be combined with distributed fibre sensing schemes to achieve both high spatial resolution and high sensitivity measurements.
Shot‐Noise‐Driven Macroscopic Vibrations and Displacement Transduction in Quantum Tunnel Junctions
Back‐action is an inevitable result of quantum measurement. Although the microscopic impacts of shot‐noise back‐action have been explored, macroscopic evidence is seldom documented, especially in the field of electrical transport. Tunneling shot‐noise has been shown to excite the fundamental flexural mode of the host crystal using a sub‐GHz lumped‐element radio‐frequency quantum point contact (QPC). Here, in this study, this aspect of shot‐noise back‐action is examined at much higher operational frequencies and wider bandwidths by employing a GaAs QPC integrated into a planar superconducting cavity within the circuit‐QED framework, leading to the observation of the excitation of multiple mechanical modes. The device operates in the shot‐noise‐limited regime. Constructed on a piezoelectric platform, there is positive feedback between the electrical and mechanical degrees of freedom within the QPC. Shot‐noise excites piezoelectric vibrational modes; concurrently the resulting polarization charges enhance tunneling and develop peaks in the shot‐noise spectra at the modal frequencies. The excitation of vibrational modes is a macroscopic demonstration of measurement back‐action, and the amplitudes of the noise‐peaks enable us to calibrate the displacement sensitivity of the QPC‐resonator systems, which is in the range $≈ 35 fm/√ Hz$, making it an excellent sensor for ultra‐sensitive and rapid strain/displacement detection.