Learnability of Optical Physical Unclonable Functions Through the Lens of Learning With Errors
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97 MATHEMATICS AND COMPUTING↗
SEARCH · Engineering Papers
Results for “Optical physics”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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A cavity-enhanced spectroscopist's lens on molecular polaritons
Polariton chemistry has been hailed as a potential new route to direct molecular processes with electromagnetic fields. To make further strides, the community needs to clarify which unusual polaritonic phenomena are true hallmarks of cavity quantum electrodynamics and which can be rationalized with classical optical physics. Here, in this work, we provide a tutorial perspective on the formation, spectroscopy, and behavior of molecular polaritons using classical optics. Where possible, we draw connections to cavity-enhanced spectroscopy and recast open questions in terms that may be more familiar to the broader community of physical chemists.
Cavity quantum electrodynamics↗
Fabrication of metal-organic framework thin films for luminescent sensing applications
Metal−organic frameworks (MOFs) have emerged as an exciting material class due to their nearly infinite design space: an inexhaustible combination of metal centers, organic linkers, and reaction conditions may be leveraged to obtained desired optical, physical, and chemical properties. This rich synthetic diversity enables properties such as pore dimensions and functionality to be rationally tailored for highly selective and sensitive detection of target analytes. Luminescent MOF-based sensors in particular offer advantages including low cost, portability, and ease-of-use. Often, performance is maximized by utilizing the luminescent MOF in thin film form, for example by integrating the film onto an optical fiber, yet the synthesis of high-quality MOF thin films often requires tedious, expensive, and/or slow approaches. Here, we demonstrate a rapid, simple strategy for growing copper MOFs using metal oxide templates; the concept is first demonstrated with a well-studied system, copper-1,3,5-benznetricarboxylate (Cu-BTC). Variables such as the choice of solvent, pH, and the choice of the copper salt anion all govern thin film quality. We then extend this method to synthesize a solvent and metal-responsive copper MOF that is particularly effective at detecting aluminum, an economically critical metal, at trace concentrations using a fully portable luminescence spectrometer. Taken together, this work demonstrates a convenient, versatile, and sustainable method for developing high quality MOF thin film-based optical sensors.
Crawford, Scott↗
End–to–End Metasurface Design for Temperature Imaging via Broadband Planck‐Radiation Regression
A theoretical framework is presented for temperature imaging from long-wavelength infrared (LWIR) thermal radiation (e.g., 8–12 µm) through the end-to-end design of a metasurface-optics frontend and a computational-reconstruction backend. A new nonlinear reconstruction algorithm, “Planck regression”, is introduced to reconstruct the temperature map from a gray scale sensor image, even in the presence of severe chromatic aberration, by exploiting black body and optical physics particular to thermal imaging. This algorithm is combined with an end-to-end approach that optimizes manufacturable, single-layer metasurfaces to yield the most accurate reconstruction. The designs demonstrate high-quality, noise-robust reconstructions of arbitrary temperature maps (including completely random images) in simulations of an ultra-compact thermal-imaging device. Here, it is also shown that Planck regression is much more generalizable to arbitrary images than a straightforward neural-network reconstruction, which requires a large training set of domain-specific images.
36 MATERIALS SCIENCE↗
Nonlinear encoding in diffractive information processing using linear optical materials
Nonlinear encoding of optical information can be achieved using various forms of data representation. Here, we analyze the performances of different nonlinear information encoding strategies that can be employed in diffractive optical processors based on linear materials and shed light on their utility and performance gaps compared to the state-of-the-art digital deep neural networks. For a comprehensive evaluation, we used different datasets to compare the statistical inference performance of simpler-to-implement nonlinear encoding strategies that involve, e.g., phase encoding, against data repetition-based nonlinear encoding strategies. We show that data repetition within a diffractive volume (e.g., through an optical cavity or cascaded introduction of the input data) causes the loss of the universal linear transformation capability of a diffractive optical processor. Therefore, data repetition-based diffractive blocks cannot provide optical analogs to fully connected or convolutional layers commonly employed in digital neural networks. However, they can still be effectively trained for specific inference tasks and achieve enhanced accuracy, benefiting from the nonlinear encoding of the input information. Our results also reveal that phase encoding of input information without data repetition provides a simpler nonlinear encoding strategy with comparable statistical inference accuracy to data repetition-based diffractive processors. Our analyses and conclusions would be of broad interest to explore the push-pull relationship between linear material-based diffractive optical systems and nonlinear encoding strategies in visual information processors.
42 ENGINEERING↗
Magnetically dependent photovoltages in permalloy films and gratings
Significant photoinduced voltages observed in permalloy structures consist of two contributions with different origins, which depend on illumination conditions, structure geometry and magnetic field in distinct ways. The first component is the plasmon drag effect voltage closely associated with plasmon propagation. The second contribution is magnetically dependent and can be related to photoinduced gradients in the sample temperature and spin polarization. Possible mechanisms of the effect are discussed
36 MATERIALS SCIENCE↗
EPCAPE-Partitioning Thrust-Los Alamos National Laboratory (EPCAPE-PT-LANL) Field Campaign Report
Coastal cities offer a unique environment for studying aerosol-cloud interactions and the effects of urban emissions on aerosol and cloud properties. As part of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM)’s Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), the Partitioning Thrust by Los Alamos National Laboratory (EPCAPE-PT-LANL) was conducted to complement the science goals of EPCAPE and add additional in-depth measurements of aerosols and clouds at Mt. Soledad. Our campaign focused on measuring the physical, optical, and chemical properties of aerosols, trace gases, and their interactions within marine stratocumulus clouds in La Jolla, California (see Figure 1).
54 ENVIRONMENTAL SCIENCES↗
Distributed optical fibre sensing in physical oceanography: emergence and future prospects
Distributed optical fibre sensing (DOFS) is a technology that enables continuous, real-time measurements of a range of environmental parameters along the length of a fibre optic cable. In this article, we review the recently emerged applications of DOFS techniques in physical oceanography and offer a perspective on the technology's potential for future growth within the field. The introduction of DOFS to physical oceanography is contextualised with a brief history of the technology, which spun off primarily from the use of seafloor-laid optical fibres for telecommunications purposes. The key components and underpinning physics of a DOFS system are outlined and, on their basis, the suite of variables that are observable with DOFS are discussed. The implementation factors of DOFS, which include trade-offs between measurement accuracy and spatio-temporal resolutions and ranges, are also examined. The physical oceanographic applications of DOFS to date are then illustrated with case examples of four distinct DOFS techniques: distributed temperature sensing (DTS), which can provide ocean temperature observations; distributed static strain sensing (DSS) and distributed acoustic sensing (DAS), which are sensitive to temperature, cable strain and strain-associated variables, such as pressure and ocean velocity; and ultra-long-range observations of ocean currents with optical interferometry. The forthcoming prospects of DOFS in physical oceanography are considered, and are proposed to include new fibre optic-based approaches to sense ocean salinity and measure through the water column. We conclude with reflections on the future role of DOFS within the Global Ocean Observing System, and highlight the opportunities provided by the existing world-wide network of seafloor-laid optical fibres.
Naveira Garabato, Alberto C. [Univ. of Southampton↗
Executive summary of error sources in dynamic surface temperature measurements
Obtaining bulk T requires measuring apparent sample interface T surface , knowledge of any window conditions T window , and knowledge of thermal transport from the sample to the window. To obtain bulk T with uncertainties below 5% requires relatively small uncertainties in each of these areas, and large uncertainties in one area require smaller uncertainties in others to maintain the error budget. For example, if T window is known to 10%, a 5% total uncertainty can be obtained if T surface is known to 1% and combined transport uncertainties are known to 20%. If T surface can only be measured to 2%, combined transport uncertainties must be 17% to reach the same overall uncertainty. As the form of transport is unknown at high pressure, and window temperatures are difficult to measure by their very nature, reducing surface T uncertainties is the practical first step. Here we will discuss various error sources in the measurement of apparent T surface , and which ones must be correctly obtained prior to the experiment.
36 MATERIALS SCIENCE↗
Predicting nonequilibrium Green’s function dynamics and photoemission spectra via nonlinear integral operator learning
Understanding the dynamics of nonequilibrium quantum many-body systems is an important research topic in a wide range of fields across condensed matter physics, quantum optics, and high-energy physics. However, numerical studies of large-scale nonequilibrium phenomena in realistic materials face serious challenges due to intrinsic high-dimensionality of quantum many-body problems and the absence of time-invariance. The nonequilibrium properties of many-body systems can be described by the dynamics of the correlator, or the Green's function of the system, whose time evolution is given by a high-dimensional system of integro-differential equations, known as the Kadanoff–Baym equations (KBEs). The time-convolution term in KBEs, which needs to be recalculated at each time step, makes it difficult to perform long-time numerical simulation. In this paper, we develop an operator-learning framework based on recurrent neural networks (RNNs) to address this challenge. We utilize RNNs to learn the nonlinear mapping between Green's functions and convolution integrals in KBEs. By using the learned operators as a surrogate model in the KBE solver, we obtain a general machine-learning scheme for predicting the dynamics of nonequilibrium Green's functions. Besides significant savings per each time step, the new methodology reduces the temporal computational complexity from $O(N_t^3)$ to $O(N_t)$ where N t is the number of steps taken in a simulation, thereby making it possible to study large many-body problems which are currently infeasible with conventional KBE solvers. Through various numerical examples, we demonstrate the effectiveness of the operator-learning based approach in providing accurate predictions of physical observables such as the reduced density matrix and time-resolved photoemission spectra. Moreover, our framework exhibits clear numerical convergence and can be easily parallelized, thereby facilitating many possible further developments and applications.
97 MATHEMATICS AND COMPUTING↗
Efficient simulation of optical spectra via machine learning and physical decomposition of environmental effects
Simulations of optical spectra can provide key insights to aid experimental interpretation of electronic excitation phenomena. For chromophores in the condensed phase, these spectra, which incorporate the coupling between electronic excitation and molecular and solvent nuclear motions, can be simulated using excitation energies obtained from molecular dynamics simulations of the chromophore and solvent. Here, we present a hybrid scheme that exploits machine learning and physically informed spectral densities to show that as few as 25 ground and excited state energetic gradient calculations can be used to construct models that accurately predict environment-influenced vibronic coupling in optical spectra. We demonstrate our approach for the green fluorescent protein chromophore in water and the cresyl violet chromophore in methanol. We show that our hybrid approach, employing a machine learning model for the high-frequency spectral density and an ab initio parameterized Debye spectral density for the low-frequency, results in a systematic improvement of the optical absorption lineshape, leading to a simple machine learning scheme that can be used for the simulation of spectral densities and optical spectra.
Snider, Andrew [Univ. of California, Merced, CA (U↗
Non-linear enhancement of ultrafast X-ray diffraction through transient resonances
Diffraction-before-destruction imaging with ultrashort X-ray pulses can visualize non-equilibrium processes, such as chemical reactions, with sub-femtosecond precision in the native environment. Here, a nanospecimen diffracts a single X-ray flash before it disintegrates. The sample structure can be reconstructed from the coherent diffraction image (CDI). State-of-the-art X-ray snapshots lack high spatial resolution because of weak diffraction signal. Bleaching effects from photo-ionization significantly restrain image brightness scaling. We find that non-linear transient ion resonances can overcome this barrier if X-ray laser pulses are shorter than in most experiments. We compared snapshots from individual ≈ 100 nm Xe nanoparticles as a function of pulse duration and incoming X-ray fluence. Our experimental results and Monte Carlo simulations suggest that transient resonances can increase ionic scattering cross sections significantly beyond literature values. This provides a novel avenue towards substantial improvement of the spatial resolution in CDI in combination with sub-femtosecond temporal precision at the nanoscale.
47 OTHER INSTRUMENTATION↗
A renewable double plasma mirror for Petawatt-class lasers
Exceptional pulse contrast can be critical for ultraintense laser experiments, particularly when using solid density targets, and their use is becoming widespread. However, current plasma mirror technology is becoming inadequate for the new generation of high repetition rate, high power lasers now available. We describe a novel double plasma mirror configuration based on renewable, free standing, ultrathin liquid crystal films tested at the BELLA Petawatt Laser Center. Although operating at a repetition rate of several shots per minute, this system can be scaled to a high repetition rate exceeding 1 Hz and represents an important step towards enabling sustained, continuous operation of plasma mirrors. We demonstrate an improvement of two to three orders of magnitude in contrast and a total throughput of 80%. We present the first measurements of a beam reflected from a single or double plasma mirror system using a wavefront sensor, showing a well preserved wavefront and spatial mode. Finally, we introduce a model that predicts the total throughput through this double plasma mirror. This is the first model that accurately predicts the peak reflectivity of a plasma mirror when given the laser temporal profile.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Atomic resolution coherent x-ray imaging with physics-based phase retrieval
Coherent x-ray imaging and scattering from accelerator based sources such as synchrotrons continue to impact biology, medicine, technology, and materials science. Many synchrotrons around the world are currently undergoing major upgrades to increase their available coherent x-ray flux by approximately two orders of magnitude. The improvement of synchrotrons may enable imaging of materials in operando at the atomic scale which may revolutionize battery and catalysis technologies. Current algorithms used for phase retrieval in coherent x-ray imaging are based on the projection onto sets method. These traditional iterative phase retrieval methods will become more computationally expensive as they push towards atomic resolution and may struggle to converge. Additionally, these methods do not incorporate physical information that may additionally constrain the solution. In this work, we present an algorithm which incorporates molecular dynamics into Bragg coherent diffraction imaging (BCDI). This algorithm, which we call PRAMMol (Phase Retrieval with Atomic Modeling and Molecular Dynamics) combines statistical techniques with molecular dynamics to solve the phase retrieval problem. We present several examples where our algorithm is applied to simulated coherent diffraction from 3D crystals and show convergence to the correct solution at the atomic scale.
47 OTHER INSTRUMENTATION↗
Virtual Beamline++: full-chain optical modeling for modern laser design and operation
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Physics - Solid state physics, Mathematics and Com↗
Rational design of efficient defect-based quantum emitters
Single-photon emitters are an essential component of quantum networks, and defects or impurities in semiconductors are a promising platform to realize such quantum emitters. Here, we present a model that encapsulates the essential physics of coupling to phonons, which governs the behavior of real single-photon emitters, and critically evaluate several approximations that are commonly utilized. Emission in the telecom wavelength range is highly desirable, but our model shows that nonradiative processes are greatly enhanced at these low photon energies, leading to a decrease in efficiency. Our results suggest that reducing the phonon frequency is a fruitful avenue to enhance the efficiency.
Optics↗
Quantum Wrapper Networking for Entanglement Management and Control in Transparent Optical Quantum Networks
The overarching goal of this three-year project was to design and experimentally demonstrate a quantum wrapper networking (QWN) protocol and architecture for entanglement management and control in coexisted classical-quantum networks, facilitating a global quantum Internet. In QWN, the quantum payload (qubits) is wrapped with classical bits as a header. The classical bits facilitate end-to-end transport of the quantum payload without having to read or alter the quantum data payload at intermediate switches and routers.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Optical Alignment with X-ray Sources Using Counter Propagating Laser Beams
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73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗