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A simple iterative algorithm for reconstructing the images of compact radio sources from very-long-baseline interferometry (VLBI) measurements of visibility amplitudes and 'closure' phases is described. The method makes efficient use of redundant sampling of spatial frequencies by making a global solution of phase 'closure' equations for a complete set of observations.
In this paper a technique of compensating for pneumatic distortion in aircraft surface pressure sensing devices is developed. The compensation allows conventional pressure sensing technology to obtain improved unsteady pressure measurements. Pressure distortion caused by frictional attenuation and pneumatic resonance within the sensing system makes obtaining unsteady pressure measurements by conventional sensors difficult. Typically, most of the distortion occurs within the pneumatic tubing used to transmit pressure impulses from the surface of the aircraft to the measurement transducer. This paper develops a second-order distortion model that accurately describes the behavior of the primary wave harmonic of the pneumatic tubing. The model is expressed in state-variable form and is coupled with standard results from minimum-variance estimation theory to develop an algorithm to compensate for the effects of pneumatic distortion. Both postflight and real-time algorithms are developed and evaluated using simulated and flight data. Covariance selection and filter-tuning examples are presented. Results presented verify that, given appropriate covariance magnitudes, the algorithms accurately reconstruct surface pressure values from remotely sensed pressure measurements.
This grant was awarded in support of an observational study with the NASA IUE Observatory during the 15th episode (1992), and it subsequently also supported our continuing work in 16th (1994) and 18th (1995) episodes. The project involved the study of FUV spectra of massive spectroscopic binary systems containing hot stars of spectral type O. We applied a Doppler tomography algorithm to reconstruct the individual component UV spectra of stars in order to obtain improved estimates of the temperature, gravity, UV intensity ratio, and projected rotational velocity for stars in each system, and to make a preliminary survey for abundance anomalies through comparison with standard spectra. We also investigated the orbital phase-related variations in the UV stellar wind lines to probe the geometries of wind-wind collisions in these systems. The project directly supported two Ph.D. dissertations at Georgia State University (by Penny and Thaller), and we are grateful for this support. No inventions were made in the performance of this work. Detailed results are summarized in the abstracts listed in the following section.
Suitably formulated spatial modulation of a scene imaged by a computed-tomography imaging spectrometer (CTIS) has been found to be useful as a means of improving the imaging performance of the CTIS. As used here, "spatial modulation" signifies the imposition of additional, artificial structure on a scene from within the CTIS optics. The basic principles of a CTIS were described in "Improvements in Computed- Tomography Imaging Spectrometry" (NPO-20561) NASA Tech Briefs, Vol. 24, No. 12 (December 2000), page 38 and "All-Reflective Computed-Tomography Imaging Spectrometers" (NPO-20836), NASA Tech Briefs, Vol. 26, No. 11 (November 2002), page 7a. To recapitulate: A CTIS offers capabilities for imaging a scene with spatial, spectral, and temporal resolution. The spectral disperser in a CTIS is a two-dimensional diffraction grating. It is positioned between two relay lenses (or on one of two relay mirrors) in a video imaging system. If the disperser were removed, the system would produce ordinary images of the scene in its field of view. In the presence of the grating, the image on the focal plane of the system contains both spectral and spatial information because the multiple diffraction orders of the grating give rise to multiple, spectrally dispersed images of the scene. By use of algorithms adapted from computed tomography, the image on the focal plane can be processed into an image cube a three-dimensional collection of data on the image intensity as a function of the two spatial dimensions (x and y) in the scene and of wavelength (lambda). Thus, both spectrally and spatially resolved information on the scene at a given instant of time can be obtained, without scanning, from a single snapshot; this is what makes the CTIS such a potentially powerful tool for spatially, spectrally, and temporally resolved imaging. A CTIS performs poorly in imaging some types of scenes in particular, scenes that contain little spatial or spectral variation. The computed spectra of such scenes tend to approximate correct values to within acceptably small errors near the edges of the field of view but to be poor approximations away from the edges. The additional structure imposed on a scene according to the present method enables the CTIS algorithms to reconstruct acceptable approximations of the spectral data throughout the scene.
In this NIAC report we present a new technique enabling a practical option for mapping these structures at a relatively low cost: Photon Time-of-Flight (PTOF) imaging. Consider a scene with a surface that is in a camera's line of sight, with unknown geometry beyond the line of sight. PTOF works by directing laser pulses onto the visible surface and detecting the returned light after it reflects off the visible surface, onto the hidden surfaces, and back to the detector. An algorithm then reconstructs a 3d model of the hidden surface. The information gained has a wide variety of applications across NASA, the scientific community, the burgeoning commercial space industry, and in commercial and industrial use here on Earth.
Northern peatlands are an integral part of the global carbon cycle—a strong sink of atmospheric carbon dioxide and source of methane. Increasing anthropogenic carbon dioxide and methane in the atmosphere are thought to strongly impact these environments, and yet, peatlands are not routinely included in Earth system models. Here we present a quantification of the sink and stock of northern peat carbon from the last glacial period through the pre-industrial period. Additional data and new algorithms for reconstructing the history of peat carbon accumulation and the timing of peatland initiation increased the estimate of total northern peat carbon stocks from 545 Gt to 1,055 Gt of carbon. Further, the post-glacial increases in peatland initiation rate and carbon accumulation rate are more abrupt than previously reported. Peatlands have been a strong carbon sink throughout the Holocene, but the atmospheric partial pressure of carbon dioxide has been relatively stable over this period. While processes such as permafrost thaw and coral reef development probably contributed some additional carbon to the atmosphere, we suggest that deep ocean upwelling was the most important mechanism for balancing the peatland sink and maintaining the observed stability.
Advances in machine learning and deep neural networks has enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions.
The computed-tomography imaging spectrometer (CTIS) captures a scene's spatial and spectral information without any type of scanning.
Recent advances in forward modeling of the electromagnetic scattering properties of sea ice are presented. The results have direct relevance to microwave remote sensing, and serve at the basis for inverse algorithms for reconstructing the physical properties of sea ice from scattering data.
Inverse scattering algorithms for reconstructing the physical properties of sea ice from scattered electromagnetic field data are developed.
A Green’s function inverse heat transfer (IHT) approach is used to reconstruct the surface heating conditions on ablative thermal protection system (TPS) materials from embedded heat flux sensor and temperature probe measurements. The approach models the temperature time-history at the measurement location as a discrete linear system, allowing for the heat flux boundary condition to be recovered directly without the need for time-marching schemes. The effects of material decomposition and pyrolysis gas transport are modeled using an energy source/sink analogue. The performance of the reconstruction approach is analyzed on a 1D test case representative of an atmospheric entry heating scenario. The approach can recover the TPS surface heat flux to within 4% of the input heating condition with a computation time of 2-3 seconds (>3 orders of magnitude faster than current time-marching IHT methods). As a byproduct of the surface heating reconstruction, the algorithm also captures the surface pyrolysis gas mass flux and solid decomposition at multiple through-thickness locations within the TPS.
Streamers are generally described as regions of the corona in which the density is higher than in coronal holes because the plasma is trapped by closed loops of magnetic flux. In contrast, MHD models of the global corona show the plasma beta in streamers above siml.2R_S (heliocentric). There are three recent contributions to this topic. The first is that heating near the cusp further drives beta up and results in release of new slow solar wind plasma from the top of the streamer. The second is SOHO/UVCS observations, in combination with a potential field/source surface model of the magnetic field, that show beta above 1.2R_S in a streamer observed near solar sunspot minimum. The third is a magnetic field reconstruction technique that uses field deforming algorithms and is more versatile for local fields than potential field models . The field reconstruction algorithm was applied to an isolated active region (AR 7999) and to the Pneuman Kopp global MHD model (beta has never been published for their model). In the active region, beta becomes larger than unity at siml.2 R_S. In the Pneuman & Kopp model, beta at the base of the streamer and rises with increasing height, becoming 15-20 at 1.6R_S and 35- 50 at 1.7R_S. Global simulations go on to show that the reason streamers do not simply explode under such high beta conditions is that they are held down by pressure from the sides due to the magnetic fields (and low beta) in adjacent coronal holes. The main role of the closed magnetic loop near the cusp is to keep the steamer from continuously leaking plasma, as otherwise happens in a magnetic pinch which is similar but has no closed loops. Awareness of MHD physical conditions in streamers is causing us to focus more attention on the details of the heating. On obvious suggestion is that heating is at much lower heights in streamers than in coronal holes. Also, energy which is directly delivered as momentum in coronal holes might all be deposited as heat in streamers.
The dynamics model and data sources used to perform air-data reconstruction are discussed, as well as the Kalman filter. The need for adaptive determination of the noise statistics of the process is indicated. The filter innovations are presented as a means of developing the adaptive criterion, which is based on the true mean and covariance of the filter innovations. A method for the numerical approximation of the mean and covariance of the filter innovations is presented. The algorithm as developed is applied to air-data reconstruction for the space shuttle, and data obtained from the third landing are presented. To verify the performance of the adaptive algorithm, the reconstruction is also performed using a constant covariance Kalman filter. The results of the reconstructions are compared, and the adaptive algorithm exhibits better performance.
The dynamics model and data sources used to perform air-data reconstruction are discussed, as well as the Kalman filter. The need for adaptive determination of the noise statistics of the process is indicated. The filter innovations are presented as a means of developing the adaptive criterion, which is based on the true mean and covariance of the filter innovations. A method for the numerical approximation of the mean and covariance of the filter innovations is presented. The algorithm as developed is applied to air-data reconstruction for the Space Shuttle, and data obtained from the third landing are presented. To verify the performance of the adaptive algorithm, the reconstruction is also performed using a constant covariance Kalman filter. The results of the reconstructions are compared, and the adaptive algorithm exhibits better performance.
Results are presented in terms of the performance improvement of a multi-feed array configuration over the usual single feed system when an adaptive least-squares algorithm is applied for the signal reconstruction. Two versions of the least-squares algorithm are presented, one of which is based on the maximization of the signal-to-noise ratio while the other is based on the deconvolution of received signal field. These algorithms were developed for the purpose of minimizing degradations arising from various sources, which can severely limit the performance (gain) of a single-feed system.
The particle-flow (PF) algorithm constructs a global description of each particle collision by producing a comprehensive list of final-state particles, and is central to event reconstruction in the CMS experiment at the CERN LHC. The existing PF implementation relies on physics-motivated heuristics and assumptions that can be replaced by machine-learning (ML) models trained directly on simulated data and naturally suited to modern graphics processing units (GPUs). A state-of-the-art ML-based PF (MLPF) reconstruction algorithm, implemented within the CMS software framework, is presented. The MLPF algorithm performs a learnable full-event reconstruction on GPUs, generalizes across detector conditions and collision energies, and replaces multiple modular reconstruction steps with a single unified model. Physics performance comparable to standard PF reconstruction is achieved in both simulation and data, with improved jet energy resolution and inference time. In simulated top quark-antiquark events under LHC Run-3 (2023-2024) conditions, the jet energy resolution improves by 10-20% for jets with transverse momentum between 30-100 GeV. Inference time is evaluated using simulated multijet events, with a median of $20\,\hbox {ms}$ per event on an Nvidia L4 GPU, compared to approximately $110\,\hbox {ms}$ for the standard CMS PF reconstruction.
We present a data processing algorithm for angular reconstruction and event selection applied to 2-D photoelectron track images from X-ray polarimeters. The method reconstructs the initial emission angle of a photoelectron from the initial portion of the track, which is obtained by continuously cutting a track until the image moments or number of pixels fall below tunable thresholds. In addition, event selection which rejects round tracks quantified with eccentricity and circularity is performed so that polarimetry sensitivity considering a trade-off between the modulation factor and signal acceptance is maximized. The modulation factors with applying track selection are 26.6 ± 0.4, 46.1 ± 0.4, 62.3 ± 0.4, and 61.8 ± 0.3% at 2.7, 4.5, 6.4, and 8.0 keV, respectively, using the same data previously analyzed by Iwakiri et al. (2016), where the corresponding numbers are 26.9±0.4, 43.4±0.4, 54.4±0.3, and 59.1 ± 0.3%. The method improves polarimeter sensitivity by 5%–10% at the high energy end of the band previously presented (Iwakiri et al. 2016).
The widely used transverse parameters characterizing particle beams are the Twiss parameters. These parameters can be measured experimentally but they do not fully characterize the beam since they do not account for possible correlations in particle distribution between two transverse coordinates. These correlations may occur due to uncompensated magnetic field at the cathode or misalignment of focusing quadrupoles in the transport beamline. We test a novel diagnostic for diagnosing full 4D beam matrix which may be used to identify such imperfections. The diagnostic is based on transporting the beam through the beamline which includes a quadrupole and a skew quadrupole magnets and measuring the resulting 2D beam distribution at the screen downstream. Such a measurement can be viewed as measuring a 2D projection of the 4D distribution. Different settings of the quads provide measurements of different slices of the phase space. The reconstruction of the original beam matrix from a number of measurements is done using machine learning algorithm, which provides a fast and reliable way of reconstruction for an arbitrary configuration of the scanning beamline. In August 2024, we set up the diagnostic beamline to perform a quadrupole scan of the beam. The setup includes a skew quadrupole, a regular quadrupole, and a screen. The images on the screen were post-processed to remove experimental artifacts and enhance contrast by eliminating background noise outside the core of the distribution=. The rms parameters of the distribution were then calculated and used as inputs for the reconstruction algorithm. This algorithm attempts to determine the initial beam matrix that produces expected images on the screen closely matching the observed images across all quadrupole settings. The algorithm found a solution in which the expected rms parameters closely align with the observations. Validation of the results is planned for FY25.