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At least 325 records · Page 18

Visible nulling interferometer

The direct detection of Earthlike planets in the visible is a very challenging goal. This paper describes a new concept for visible direct detection of Earths using a nulling interferometer instrument behind a 4m telescope in space. The basic concept is described along with the key advantages of the nulling interferometer over more traditional approaches, an apodized aperture telescope or coronagraph. In the baseline design, a 4 beam nuller produces a very deep theta^4 null. With perfect optics, the stellar leakage is less than le-11 of the starlight at the location of the planet. With diffraction limited (lambda/20) telescope optics suppression of the starlight to -1e-10 would be possible.

Direct planet detection nulling coronagraph↗

Real-time deep-learning inversion of seismic full waveform data for CO 2 saturation and uncertainty in geological carbon storage monitoring

Deep-learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep-learning full waveform inversion method that after the neural network has been trained can image CO 2 saturation and its uncertainty in real time. Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO 2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO 2 saturation models and once fully trained directly estimates CO 2 saturation as a function of subsurface location. We further quantify uncertainties of CO 2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof-of-concept study, the CO 2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep-learning inversion experiments using noise-free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep-learning inversion can estimate 2D distributions of CO 2 fairly well even in the presence of Gaussian random noise and (2) both CO 2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep-learning inversion method can serve as a robust real-time monitoring tool for geological carbon storage and/or other time-varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.

58 GEOSCIENCES↗

A deep learning upscaling framework: Reactive transport and mineral precipitation in fracture-matrix systems

Pore-scale modeling has limited applicability at large scales due to its high computational cost. One common approach to upscale pore-scale models is the use of effective medium theories, which homogenize small-scale features in a porous structure and characterize the medium by macroscale properties (e.g., permeability) and equations (e.g., Darcy’s law). However, there are classes of physical processes for which effective medium approximations may become inaccurate, e.g., mineral precipitation and clogging during reactive transport. We have developed a deep learning upscaling framework, in which pore-scale modeling is directly employed in macroscale systems, without relying on effective medium approximations. The upscaling framework is first developed for general multiscale systems and then applied to modeling reactive transport with mineral precipitation in the altered layer in fracture-matrix structures. Solute transport from the fractures to the matrix is modeled as a wall boundary condition for the fractures, which, in turn, is predicted by recurrent neural networks using the concentration histories at the fracture-matrix boundary. Specifically, we consider a meter-scale fracture network embedded in sandstones, where the smallest feature is at the micron scale. Here the proposed framework allows us to span five orders of magnitude in length scales by capturing mineral precipitation in the altered layer of the rock matrix at the pore scale across the entire meter-scale fracture network.

42 ENGINEERING↗

Machine learning Frenkel Hamiltonian parameters to accelerate simulations of exciton dynamics

In this manuscript, we develop multiple machine learning (ML) models to accelerate a scheme for parameterizing site-based models of exciton dynamics from all-atom configurations of condensed phase sexithiophene systems. This scheme encodes the details of a system’s specific molecular morphology in the correlated distributions of model parameters through the analysis of many single-molecule excited-state electronic-structure calculations. These calculations yield excitation energies for each molecule in the system and the network of pair-wise intermolecular electronic couplings. In this work, we demonstrate that the excitation energies can be accurately predicted using a kernel ridge regression (KRR) model with Coulomb matrix featurization. We present two ML models for predicting intermolecular couplings. The first one utilizes a deep neural network and bi-molecular featurization to predict the coupling directly, which we find to perform poorly. The second one utilizes a KRR model to predict unimolecular transition densities, which can subsequently be analyzed to compute the coupling. We find that the latter approach performs excellently, indicating that an effective, generalizable strategy for predicting simple bimolecular properties is through the indirect application of ML to predict higher-order unimolecular properties. Such an approach necessitates a much smaller feature space and can incorporate the insight of well-established molecular physics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Alpert multi-wavelets for functional inverse problems: direct optimization and deep learning

Computational engineering models often contain unknown entities (e.g. parameters, initial and boundary conditions) that require estimation from other measured observable data. Estimating such unknown entities is challenging when they involve spatio-temporal fields because such functional variables often require an infinite-dimensional representation. Here, we address this problem by transforming an unknown functional field using Alpert wavelet bases and truncating the resulting spectrum. Hence the problem reduces to the estimation of few coefficients that can be performed using common optimization methods. We apply this method on a one-dimensional heat transfer problem where we estimate the heat source field varying in both time and space. The observable data is comprised of temperature measured at several thermocouples in the domain. This latter is composed of either copper or stainless steel. The optimization using our method based on wavelets is able to estimate the heat source with an error between 5% and 7%. We analyze the effect of the domain material and number of thermocouples as well as the sensitivity to the initial guess of the heat source. Finally, we estimate the unknown heat source using a different approach based on deep learning techniques where we consider the input and output of a multi-layer perceptron in wavelet form. We find that this deep learning approach is more accurate than the optimization approach with errors below 4%.

97 MATHEMATICS AND COMPUTING↗

Polarized 𝐽/𝜓 production in semi-inclusive DIS at large 𝑄 2 : Comparing quark fragmentation and photon-gluon fusion

We compare the relative importance of different mechanisms for polarized 𝐽/𝜓 production in semi-inclusive deep inelastic scattering processes at large 𝑄 2 . The transverse momentum dependent (TMD) factorization framework and nonrelativistic quantum chromodynamics are used to study the leading contributions from light quark fragmentation to polarized 𝐽/𝜓, and compared to direct production via photon-gluon fusion, which can proceed through color-singlet as well as color-octet mechanisms. We identify kinematic regimes where light quark fragmentation dominates, allowing for the extraction of the 3 𝑆$^{[8]}_{1}$ matrix element, as well as regimes where photon gluon fusion dominates, suggesting that the gluon TMD parton distribution function can be probed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Towards On-Chip Learning for Low Latency Reasoning with End-to-End Synthesis

The Software Defined Architectures (SODA) Synthesizer is an open-source compiler-based tool able to automatically generate domain-specialized systems targeting Application-Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs) starting from high-level programming. SODA is composed of a frontend, SODA-OPT, which leverages the multilevel intermediate representation (MLIR) framework to interface with productive programming tools (e.g., machine learning frame-works), identify kernels suitable for acceleration, and perform high-level optimizations, and of a state-of-the-art high-level synthesis backend, Bambu from the PandA framework, to generate custom accelerators. One specific application of the SODA Synthesizer is the generation of accelerators to enable ultra-low latency inference and control on autonomous systems for scientific discovery (e.g., electron microscopes, sensors in particle accelerators, etc.). This paper provides an overview of the flow in the context of the generation of accelerators for edge processing to be integrated in transmission electron microscopy (TEM) devices, focusing on use cases from precision material synthesis. We show the tool in action with an example of design space exploration for inference on reconfigurable devices with a conventional deep neural network model (LeNet). Finally, we discuss the research directions and opportunities enabled by SODA in the area of autonomous control for scientific experimental workflows.

Castellana, Vito G.↗

DeepMerge: Studying Distant Merging Galaxies with Deep Neural Networks

The hierarchical merging of galaxies is a probe of the cosmos to test the canonical ?CDM cosmology paradigm. A particularly interesting period is "cosmic high noon" at redshifts z? 2?3, during which star formation rates were the highest, and significant amounts of stellar mass assembled into galaxy-scale bodies. Detecting galaxy mergers in observations by conventional automated methods (which use extracted parameters of galaxy structure - asymmetry, clumpiness, concentration etc.) or by visual inspection has proven to be quite time-consuming and prone to errors. Convolutional Neural Network (CNNs) are a primary representative of deep learning algorithms which are used in computer vision tasks by training to detect features in images. CNNs were already used for classification of low-redshift merging galaxies [1, 2]. Here we will use CNN to to learn directly from images (without the need to extract morphology parameters) of distant merging galaxies in order to distinguish between merging and non-merging objects.

79 ASTRONOMY AND ASTROPHYSICS↗

Precision Neutrino Oscillation Physics with the Daya Bay and DUNE Experiments

The team supported by this grant made significant contributions to the final results of the Daya Bay Reactor Antineutrino Experiment. This experiment utilized eight identically designed antineutrino detectors positioned at varying distances from six 2.9 GW th nuclear reactors to precisely measure the oscillation parameters that govern antineutrino disappearance at short (<2 km) baselines. Our group played a leading role in the calibration and data quality efforts, both of which have been crucial for all final results. Additionally, we co-led the development of an independent measurement of the neutrino mixing angle θ 13 and the atmospheric mass splitting using a sample of antineutrinos identified via neutron capture on hydrogen. Lastly, we laid the groundwork for a search for seasonal modulation in Daya Bay’s measured muon flux using the final dataset, a result expected to be published soon. Simultaneously, our team ramped up its participation in the Deep Underground Neutrino Experiment (DUNE). This experiment will employ a powerful neutrino beam from Fermilab in Illinois directed to the Homestake mine in South Dakota to address some of the most pressing questions in neutrino physics, including the ordering of neutrino masses and whether neutrinos violate the CP symmetry. Our work focused on the development of the pixelated and modularized Liquid Argon Time-Projection Chamber technology that is being prepared for DUNE’s Near Detector. Our group took responsibility for the development, testing, and maintenance of the firmware for the control boards of the detector’s charge readout system and played an active role in analyzing data produced by the very first fully integrated prototypes.

2x2 Demonstrator↗

Planet detection and spectroscopy in visible light with a single aperture telescope and a nulling coronagraph

This talk describes a new concept for visible direct detection of Earth like extra solar planets using a nulling coronagraph instrument behind a 4m telescope in space. In the baseline design, a 4 beam nulling interferometer is synthesized from the telescope pupil, producing a very deep theta^4null which is then filtered by a coherent array of single mode fibers to suppress the residual scattered light. With perfect optics, the stellar leakage is less than 1e-11 of the starlight at the location of the planet. With diffraction limited telescope optics (lambda/20), suppression of the starlight to ~1e-10 is possible. The concept is described along with the key advantages over more traditional approaches such as apodized aperture telescopes and Lyot type coronagraphs.

coronagraph↗

Advanced Stirling Convertor Testing at NASA Glenn Research Center

The U.S. Department of Energy (DOE), Lockheed Martin Space Systems (LMSS), Sunpower Inc., and NASA Glenn Research Center (GRC) have been developing an Advanced Stirling Radioisotope Generator (ASRG) for use as a power system on space science and exploration missions. This generator will make use of the free-piston Stirling convertors to achieve higher conversion efficiency than currently available alternatives. The ASRG will utilize two Advanced Stirling Convertors (ASC) to convert thermal energy from a radioisotope heat source to electricity. NASA GRC has initiated several experiments to demonstrate the functionality of the ASC, including: in-air extended operation, thermal vacuum extended operation, and ASRG simulation for mobile applications. The in-air and thermal vacuum test articles are intended to provide convertor performance data over an extended operating time. These test articles mimic some features of the ASRG without the requirement of low system mass. Operation in thermal vacuum adds the element of simulating deep space. This test article is being used to gather convertor performance and thermal data in a relevant environment. The ASRG simulator was designed to incorporate a minimum amount of support equipment, allowing integration onto devices powered directly by the convertors, such as a rover. This paper discusses the design, fabrication, and implementation of these experiments.

Oriti, Salvatore↗

Advanced Stirling Convertor Testing at NASA Glenn Research Center

The U.S. Department of Energy (DOE), Lockheed Martin Space Systems (LMSS), Sunpower Inc., and NASA Glenn Research Center (GRC) have been developing an Advanced Stirling Radioisotope Generator (ASRG) for use as a power system on space science and exploration missions. This generator will make use of the free-piston Stirling convertors to achieve higher conversion efficiency than currently available alternatives. The ASRG will utilize two Advanced Stirling Convertors (ASC) to convert thermal energy from a radioisotope heat source to electricity. NASA GRC has initiated several experiments to demonstrate the functionality of the ASC, including: in-air extended operation, thermal vacuum extended operation, and ASRG simulation for mobile applications. The in-air and thermal vacuum test articles are intended to provide convertor performance data over an extended operating time. These test articles mimic some features of the ASRG without the requirement of low system mass. Operation in thermal vacuum adds the element of simulating deep space. This test article is being used to gather convertor performance and thermal data in a relevant environment. The ASRG simulator was designed to incorporate a minimum amount of support equipment, allowing integration onto devices powered directly by the convertors, such as a rover. This paper discusses the design, fabrication, and implementation of these experiments.

Oriti, Salvatore M.↗

Experimental Evaluation of the "Polished Panel Optical Receiver" Concept on the Deep Space Network's 34 Meter Antenna

The potential development of large aperture ground-based "photon bucket" optical receivers for deep space communications has received considerable attention recently. One approach currently under investigation proposes to polish the aluminum reflector panels of 34-meter microwave antennas to high reflectance, and accept the relatively large spotsize generated by even state-of-the-art polished aluminum panels. Here we describe the experimental effort currently underway at the Deep Space Network (DSN) Goldstone Communications Complex in California, to test and verify these concepts in a realistic operational environment. A custom designed aluminum panel has been mounted on the 34 meter research antenna at Deep-Space Station 13 (DSS-13), and a remotely controlled CCD camera with a large CCD sensor in a weather-proof container has been installed next to the subreflector, pointed directly at the custom polished panel. Using the planet Jupiter as the optical point-source, the point-spread function (PSF) generated by the polished panel has been characterized, the array data processed to determine the center of the intensity distribution, and expected communications performance of the proposed polished panel optical receiver has been evaluated.

photon counting detection↗

An ALMA+ACA measurement of the shock in the Bullet Cluster

Context: The thermal Sunyaev-Zeldovich (SZ) effect presents a relatively new tool for characterizing galaxy cluster merger shocks, traditionally studied through X-ray observations. Widely regarded as the “textbook example” of a cluster merger bow shock, the western, most-prominent shock front in the Bullet Cluster (1E0657-56) represents the ideal test case for such an SZ study. Aims: We aim to characterize the shock properties using deep, high-resolution interferometric SZ effect observations in combination with priors from an independent X-ray analysis. Methods: Our analysis technique relies on the reconstruction of a parametric model for the SZ signal by directly and jointly fitting data from the Atacama Large Millimeter/submillimeter Array (ALMA) and Atacama Compact Array (ACA) in Fourier space. Results: The ALMA+ACA data are primarily sensitive to the electron pressure difference across the shock front. To estimate the shock Mach number M, this difference can be combined with the value for the upstream electron pressure derived from an independent Chandra X-ray analysis. In the case of instantaneous electron-ion temperature equilibration, we find M = 2.08 (+0.12,−0.12), in ≈ 2.4σ tension with the independent constraint from Chandra, M(X)= 2.74 ± 0.25. The assumption of purely adiabatic electron temperature change across the shock leads to M = 2.53 (+0.33,−0.25), in better agreement with the X-ray estimate M(X) = 2.57 ± 0.23 derived for the same heating scenario. Conclusion: We have demonstrated that interferometric observations of the thermal SZ effect provide constraints on the properties of the shock in the Bullet Cluster that are highly complementary to X-ray observations. The combination of X-ray and SZ data yields a powerful probe of the shock properties, capable of measuring M and addressing the question of electron-ion equilibration in cluster shocks. Our analysis is however limited by systematics related to the overall cluster geometry and the complexity of the post-shock gas distribution. To overcome these limitations, a simultaneous, joint-likelihood analysis of SZ and X-ray data is needed.

Luca Di Mascolo↗

Novel Thermal Powered Technology for UUV Persistent Surveillance

Buoyancy Generation: Various technology attempts include melting a wax, which pushes directly against a piston (U.S. Patent 5,291,847) or against a bladder (Webb Research), using ammonia or Freon 21 (U.S. Patent 5,303,552), and using solar heat to expand an oil (www.space.com, April, 10, 2002). All these heat-activated buoyancy control designs have thus far proved impractical and have ultimately failed during repeated cycling in ocean testing. JPL has demonstrated fully reversible 10 C encapsulated wax phase change, which can be used to change buoyancy without electrical hydraulic pumps. This technique has greatly improved heat transfer and much better reversibility than previous designs. Power Generation: Ocean Thermal Energy Conversion (OTEC) systems have been designed that transfer deep, cold sea water to the surface to generate electricity using turbine cycles with ammonia or water as the working fluid. JPL has designed several UUV systems: 1) Using a propeller water turbine to generate power on a gliding submersible; 2) Employing a compact CO2 turbine cycle powered by moving through thermoclines; and 3) Using melted wax to directly produce power through a piston-geared generator.

submersible↗

X-Ray Detection and Processing Models for Spacecraft Navigation and Timing

The current primary method of deepspace navigation is the NASA Deep Space Network (DSN). High-performance navigation is achieved using Delta Differential One-Way Range techniques that utilize simultaneous observations from multiple DSN sites, and incorporate observations of quasars near the line-of-sight to a spacecraft in order to improve the range and angle measurement accuracies. Over the past four decades, x-ray astronomers have identified a number of xray pulsars with pulsed emissions having stabilities comparable to atomic clocks. The x-ray pulsar-based navigation and time determination (XNAV) system uses phase measurements from these sources to establish autonomously the position of the detector, and thus the spacecraft, relative to a known reference frame, much as the Global Positioning System (GPS) uses phase measurements from radio signals from several satellites to establish the position of the user relative to an Earth-centered fixed frame of reference. While a GPS receiver uses an antenna to detect the radio signals, XNAV uses a detector array to capture the individual xray photons from the x-ray pulsars. The navigation solution relies on detailed xray source models, signal processing, navigation and timing algorithms, and analytical tools that form the basis of an autonomous XNAV system. Through previous XNAV development efforts, some techniques have been established to utilize a pulsar pulse time-of-arrival (TOA) measurement to correct a position estimate. One well-studied approach, based upon Kalman filter methods, optimally adjusts a dynamic orbit propagation solution based upon the offset in measured and predicted pulse TOA. In this delta position estimator scheme, previously estimated values of spacecraft position and velocity are utilized from an onboard orbit propagator. Using these estimated values, the detected arrival times at the spacecraft of pulses from a pulsar are compared to the predicted arrival times defined by the pulsar s pulse timing model. A discrepancy provides an estimate of the spacecraft position offset, since an error in position will relate to the measured time offset of a pulse along the line of sight to the pulsar. XNAV researchers have been developing additional enhanced approaches to process the photon TOAs to arrive at an estimate of spacecraft position, including those using maximum-likelihood estimation, digital phase locked loops, and "single photon processing" schemes that utilize all available time data associated with each photon. Using pulsars from separate, non-coplanar locations provides range and range-rate measurements in each pulsar s direction. Combining these different pulsar measurements solves for offsets in position and velocity in three dimensions, and provides accurate overall navigation for deep space vehicles.

Sheikh, Suneel↗

Enhancements to the Open Access Spectral Band Adjustment Factor Online Calculation Tool for Visible Channels

With close to 40 years of satellite observations, from which, cloud, land-use, and aerosol parameters can be measured, inter-consistent calibrations are needed to normalize retrievals across satellite records. Various visible-sensor inter-calibration techniques have been developed that utilize radiometrically stable Earth targets, e.g., deep convective clouds and desert/polar ice pseudo-invariant calibration sites. Other equally effective, direct techniques for intercalibration between satellite imagers are simultaneous nadir overpass comparisons and ray-matched radiance pairs. Combining independent calibration results from such varied techniques yields robust calibration coefficients, and is a form of self-validation. One potential source of significant error when cross-calibrating satellite sensors, however, are the often small but substantial spectral discrepancies between comparable bands, which must be accounted for. As such, visible calibration methods rely on a Spectral Band Adjustment Factor (SBAF) to account for the spectral-response function- induced radiance differences between analogous imagers. The SBAF is unique to each calibration method as it is a function of the Earth-reflected spectra. In recent years, NASA Langley pioneered the use of SCIAMACHY-, GOME-2-, and Hyperion-retrieved Earth spectra to compute SBAFs. By carefully selecting hyperspectral footprints that best represent the conditions inherent to an inter-calibration technique, the uncertainty in the SBAF is greatly reduced. NASA Langley initially provided the Global Space-based Inter-calibration System processing and research centers with online SBAF tools, with which users select conditions to best match their calibration criteria. This article highlights expanded SBAF tool capabilities for visible wavelengths, with emphasis on the use of the spectral range filtering for the purpose of separating scene conditions for the channel that the SBAF is needed based on the reflectance values of other bands. In other words, spectral filtering will enable better scene-type selection for bands where scene determination is difficult without information from other channels, which should prove valuable to users in the calibration community.

Scarino, Benjamin↗

A microwave holography methodology for diagnostics and performance improvement for large reflector antennas

This microwave holography technique utilizes the Fourier transform relation between the complex far field radiation pattern of an antenna and the complex aperture field distribution. Resulting aperture phase and amplitude distribution data can be used to precisely characterize various crucial performance parameters, including panel alignment, panel shaping, subreflector position, antenna aperture illumination, directivity at various frequencies, and gravity deformation effects. The methodology of data processing presented here was successfully applied to the Deep Space Network (DSN) 34-m beam waveguide antennas. The antenna performance was improved at all operating frequencies by reducing the main reflector mechanical surface rms error to 0.43 mm. At Ka-band (32 GHz), the estimated improvement is 4.1 dB, resulting in an aperture efficiency of 52 percent. The performance improvement was verified by efficiency measurements and additional holographic measurements.

Rochblatt, D. J.↗