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DOE OSTI2015

FJK - A cap-free fragment approach with embedding Fock potentials

We present a cap-free fragment-based approach for Hartree-Fock calculations where full Fock potentials are used for monomer embedding. In this FJK (Fragmentation with Coulomb (J) and exchange (K) embedding) method the system is divided into fragments by heterolytic fission of covalent bonds. The one-electron operators for each fragment are built including the mean-field Coulomb and exchange potential of all the remaining monomers. The description is improved by iterative updates of the monomer potentials within which the method converges to the full system Hartree-Fock result. It is shown that a small number of iterations is sufficient to yield errors in the absolute energy comparable to those usually observed in density fitting procedures. Conformational energies of organic molecules are used as benchmark, demonstrating the robustness of the procedure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1993

Evaluating the design of an Earth Radiation Budget Instrument with systen simulations. Part 1: Instantaneous estimates

A set of system simulations has been performed to evaluate candidate scanner designs for an Earth Radiation Budget Instrument (ERBI) for the Earth Observing System (EOS) of the late 1990s. Five different instruments are considered: (1) the Active Cavity Array (ACA), (2) the Clouds and Earth's Radiant Energy System-Instrument (CERES-I), (3) the Conically Scanning Radiometer (CSR), (4) the Earth Radiation Budget Experiment Cross-Track Scanner (ERBE), and (5) the Nimbus-7 Biaxial Scanner (N7). Errors in instantaneous, top-of-the-atmosphere (TOA) satellite flux estimates are assumed to arise from two measurement problems: the sampling of space over a given geographic domain, and sampling in angle about a given spatial location. When angular sampling errors vanish due to the application of correct angular dependence models (ADMs) during inversion, the accuracy of each scanner design is determined by the instrument's ability to map the TOA radiance field in a uniform manner. In this regard, the instruments containing a cross-track scanning component (CERES-I and ERBE) do best. As errors in ADMs are encountered, cross-track instruments incur angular sampling errors more rapidly than biaxial instruments (N7, ACA, and CSR) and eventually overtake the biaxial designs in their total error amounts. A latitude bias (north-south error gradient) in the ADM error of cross-track instruments also exists. This would be objectionable when ADM errors are systematic over large areas of the globe. For instantaneous errors, however, cross-track scanners outperform biaxial or conical scanners for 2.5 deg latitude x 2.5 deg longitude target areas, providing that the ADM error is less than or equal to 30%. A key issue is the amount of systematic ADM error (departures from the mean models) that is present at the 2.5 deg resolution of the ERBE target areas. If this error is less than 30%, then the CERES-I, ERBE, and CSR, in order of increasing error, provide the most accurate instantaneous flux estimates, within 2-3 W/sq m of each other in reflected shortwave flux. The magnitude of this error is near the 10 W/sq m accuracy requirement of the user community. Longwave flux errors have been found to have the same space and time characteristics as errors in shortwave radiation, but only about 25% as large.

Stowe, Larry↗

NASA NTRS1981

Precision orbit computations for an operational environment

Taking advantage of the improvements to the Earth's gravitation field and tracking station coordinates, an orbital computational consistency of the order of 5 meters was achieved for total position differences between orbital solutions for the Seasat and GEOS-3. The main source of error in these solutions was in the mathematical models that are required to generate these results, i.e., gravitation, atmospheric drag, etc. Different Earth gravitation fields and tracking coordinates were analyzed and evaluated in obtaining these computational results. Comparisons and evaluations of the Seasat results were obtained in terms of different solution types such as the Doppler only, Laser only, Doppler and Laser, etc. Other investigation using the Seasat data were made in order to determine their effect on the computational results at this particular level of consistency.

Doll, C. E.↗

DOE OSTI2023

Soft syndrome iterative decoding of quantum LDPC codes and hardware architectures

In practical quantum error correction implementations, the measurement of syndrome information is an unreliable step—typically modeled as a binary measurement outcome flipped with some probability. However, the measured syndrome is in fact a discretized value of the continuous voltage or current values obtained in the physical implementation of the syndrome extraction. In this paper, we use this “soft” or analog information to benefit iterative decoders for decoding quantum low-density parity-check (QLDPC) codes. Syndrome-based iterative belief propagation decoders are modified to utilize the soft syndrome to correct both data and syndrome errors simultaneously. We demonstrate the advantages of the proposed scheme not only in terms of comparison of thresholds and logical error rates for quasi-cyclic lifted-product QLDPC code families but also with faster convergence of iterative decoders. Additionally, we derive hardware (FPGA) architectures of these soft syndrome decoders and obtain similar performance in terms of error correction to the ideal models even with reduced precision in the soft information. The total latency of the hardware architectures is about 600 ns (for the QLDPC codes considered) in a 20 nm CMOS process FPGA device, and the area overhead is almost constant—less than 50% compared to min-sum decoders with noisy syndromes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements

Physics-informed neural networks (PINNs) were recently proposed in [18] as an alternative way to solve partial differential equations (PDEs). A neural network (NN) represents the solution, while a PDE-induced NN is coupled to the solution NN, and all differential operators are treated using automatic differentiation. Here, we first employ the standard PINN and a stochastic version, sPINN, to solve forward and inverse problems governed by a non-linear advection–diffusion–reaction (ADR) equation, assuming we have some sparse measurements of the concentration field at random or pre-selected locations. Subsequently, we attempt to optimise the hyper-parameters of sPINN by using the Bayesian optimisation method (meta-learning) and compare the results with the empirically selected hyper-parameters of sPINN. In particular, for the first part in solving the inverse deterministic ADR, we assume that we only have a few high-fidelity measurements, whereas the rest of the data is of lower fidelity. Hence, the PINN is trained using a composite multi-fidelity network, first introduced in [12], that learns the correlations between the multi-fidelity data and predicts the unknown values of diffusivity, transport velocity and two reaction constants as well as the concentration field. For the stochastic ADR, we employ a Karhunen–Loève (KL) expansion to represent the stochastic diffusivity, and arbitrary polynomial chaos (aPC) to represent the stochastic solution. Correspondingly, we design multiple NNs to represent the mean of the solution and learn each aPC mode separately, whereas we employ a separate NN to represent the mean of diffusivity and another NN to learn all modes of the KL expansion. For the inverse problem, in addition to stochastic diffusivity and concentration fields, we also aim to obtain the (unknown) deterministic values of transport velocity and reaction constants. The available data correspond to 7spatial points for the diffusivity and 20 space–time points for the solution, both sampled 2000 times. We obtain good accuracy for the deterministic parameters of the order of 1–2% and excellent accuracy for the mean and variance of the stochastic fields, better than three digits of accuracy. In the second part, we consider the previous stochastic inverse problem, and we use Bayesian optimisation to find five hyper-parameters of sPINN, namely the width, depth and learning rate of two NNs for learning the modes. Here, we obtain much deeper and wider optimal NNs compared to the manual tuning, leading to even better accuracy, i.e., errors less than 1% for the deterministic values, and about an order of magnitude less for the stochastic fields.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2019

Bit Error Rate Performance of a Free Space Optical Link Using Double Clad Fibers

Mobile and embedded applications are emerging in the growing field of free space optical links (FSOL). Some mobile applications for FSOL include spacecraft, aircraft, and automotive. These applications by nature require low size weight and power (SWaP) solutions. The main challenge with any FSOL system is the strict pointing requirements. Common solutions to pointing and alignment of FSOL include gimbals, fast steering mirrors, and adaptive optics. All of which provide viable solutions at the cost of increased SWaP. Previously, we presented the use of both large core fibers and double clad fibers (DCF) to interface FSOL transmit and receive optics with small form factor pluggable optical transceivers (SFP). Double clad fibers have been shown to enable a common optical path by transmitting through a single mode core and receiving through a large inner cladding. This enables a single set of symmetric transmit and receive optics, which decreases the SWaP. In addition, using DCF increases the received power stability of the link relative to a multi-mode fiber (MMF) transmitting. To determine the viability of the system, bit error rate performance needs to be investigated. The results of this paper show that at a bit rate of 10 Gbps, double clad fibers offer similar bit error rate performance to single mode fibers when transmitting and multi-mode fibers when receiving enabling a symmetric duplex FSOL reducing SWaP.

Small Form-Factor Pluggable Optical Transceiver↗

NASA NTRS2019

Bit Error Rate Performance of a Free Space Optical Link Using Double Clad Fibers

Double Clad Fiber↗

NASA NTRS2012

The Precise Orbit and the Challenge of Long Term Stability

The computation of a precise orbit reference is a fundamental component of the altimetric measurement. Since the dawn of the modern altimeter age, orbit accuracy has been determined by the quality of the GPS, SLR, and DORIS tracking systems, the fidelity of the measurement and force models, and the choice of parameterization for the orbit solutions, and whether a dynamic or a reduced-dynamic strategy is used to calculate the orbits. At the start of the TOPEX mission, the inaccuracies in the modeling of static gravity, dynamic ocean tides, and the nonconservative forces dominated the orbit error budget. Much of the error due to dynamic mismodeling can be compensated by reduced-dynamic tracking techniques depending on the measurement system strength. In the last decade, the launch of the GRACE mission has eliminated the static gravity field as a concern, and the background force models and the terrestrial reference frame have been systematically refined. GPS systems have realized many improvements, including better modeling of the forces on the GPS spacecraft, large increases in the ground tracking network, and improved modeling of the GPS measurements. DORIS systems have achieved improvements through the use of new antennae, more stable monumentation, and of satellite receivers that can track multiple beacons, and as well as through improved modeling of the nonconservative forces. Many of these improvements have been applied in the new reprocessed time series of orbits produced for the ERS satellites, Envisat, TOPEX/Poseidon and the Jason satellites, and as well as for the most recent Cryosat-2 and HY2A. We now face the challenge of maintaining a stable orbit reference for these altimetric satellites. Changes in the time-variable gravity field of the Earth and how these are modelled have been shown to affect the orbit evolution, and the calibration of the altimetric data with tide gauges. The accuracy of the reference frame realizations, and their projection into the future remains a source of error. Other sources of omission error include the geocenter for which no consensus model is as of yet applied. Although progress has been made in nonconservative force modeling through the use of detailed satellite-specific models, radiation pressure modeling, and atmospheric density modeling remain a potential source of orbit error. The longer term influence of variations in the solar and terrestrial radiation fields over annual and solar cycles remains principally untested. Also the long term variation in optical and thermal properties of the space vehicle surfaces would contribute to biases in the orbital frame if ignored. We review the status of altimetric precision orbit determination as exemplified by the recent computations undertaken by the different analysis centers for ERS, Envisat, TOPEX/Poseidon, Jason, Cryosat2 and HY2A, and we provide a perspective on the challenges for future missions such as the Jason-3, SENTINEL-3 and SWOT.

Lemoine, Frank G.↗

NASA NTRS1981

Investigations of medium wavelength magnetic anomalies in the Eastern Pacific using Magsat data

Processing continued on the data set covering the first three months of the mission. Orbits were selected over a specified range of longitude and latitude and those orbits falling within the geographical boundaries were stored on a disk and a listing made. To verify the quality of the data, plots were made showing altitude vs latitude, anomalous field vs latitude, and corrected anomalous field vs latitude. Results of the plotting showed that: (1) data from the first day of the mission are not usable because they are too noisy; (2) some orbits contain spikes, no doubt a processing error on the tape; (3) some orbits contain a 20 deg region in latitude, offset roughly 30 nT from the rest of the anomalous field profiles; and (4) the correlation between the adjacent orbits is poor. Even the new ring current coefficients provided with a later, corrected set of data tapes did not not yield the desired correlations.

Harrison, C. G. A.↗

DOE OSTI2022

Advanced Finite-Volume Numerics and Source Term Assumptions for Kernel and G-Equation Modelling of Propane/Air Flames

Here G-Equation models represent propagating flame fronts with an implicit two-dimensional surface representation (level-set). Level-set methods are fast, as transport source terms for the implicit surface can be solved with finite-volume operators on the finite-volume domain, without having to build the actual surface. However, they include approximations whose practical effects are not properly understood. In this study, we improved the numerics of the FRESCO CFD code’s G-Equation solver and developed a new method to simulate kernel growth using signed distance functions and the analytical sphere-mesh overlap. We analyzed their role for simulating propane/air flames, using three well-established constant-volume configurations: a one-dimensional, freely propagating laminar flame; a disc-shaped, constant-volume swirl combustor; and torch-jet flame development through an orifice from a two-chamber device. We tested the explicit (sub-cycled) vs. implicit formulation for the standard transport operators (advection, diffusion, compressibility). In addition to the accurate flame swept-volume method for chemistry and species source term, we developed a more accurate estimator for the burnt/unburnt split cell composition. Then, we developed a signed-distance-function (SDF) based method which provides a more stable reinitialization of the level-set field at every time-step. We found that simplifying assumptions common to several G-Equation implementations, for straightforward terms such as compressibility and advection, lead to large errors in predicting the propagation of even laminar flames, with deviations up to ~300% in simulated vs. formulated flame speed. Conversely, the enhanced numerics enabled through the SDF field reinitialization and improved chemistry source term improve simulation stability and smooth flame propagation even with significantly larger solver time-steps.

42 ENGINEERING↗

DOE OSTI2013

Measuring the thermal Sunyaev-Zel'dovich effect through the cross correlation of Planck and WMAP maps with ROSAT galaxy cluster catalogs

We measure a significant correlation between the thermal Sunyaev-Zel'dovich effect in the Planck and WMAP maps and an X-ray cluster map based on ROSAT. We use the 100, 143 and 343 GHz Planck maps and the WMAP 94 GHz map to obtain this cluster cross spectrum. We check our measurements for contamination from dusty galaxies using the cross correlations with the 217, 545 and 857 GHz maps from Planck. Our measurement yields a direct characterization of the cluster power spectrum over a wide range of angular scales that is consistent with large cosmological simulations. The amplitude of this signal depends on cosmological parameters that determine the growth of structure (σ{sub 8} and Ω M) and scales as σ{sub 8}{sup 7.4} and Ω M{sup 1.9} around the multipole (ℓ) ∼ 1000. We constrain σ{sub 8} and Ω M from the cross-power spectrum to be σ{sub 8}(Ω M/0.30){sup 0.26} = 0.8±0.02. Since this cross spectrum produces a tight constraint in the σ{sub 8} and Ω M plane the errors on a σ{sub 8} constraint will be mostly limited by the uncertainties from external constraints. Future cluster catalogs, like those from eRosita and LSST, and pointed multi-wavelength observations of clusters will improve the constraining power of this cross spectrum measurement. In principle this analysis can be extended beyond σ{sub 8} and Ω M to constrain dark energy or the sum of the neutrino masses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2015

WE-AB-BRA-11: Development of Physiological-Based Virtual Thorax Phantoms for Evaluation of Deformable Image Registration

Purpose: To develop a methodology of constructing physiological-based virtual thorax phantom based on hyperpolarized (HP) gas tagging MRI for evaluating deformable image registration (DIR). Methods: Three healthy subjects were imaged at both the end-of-inhalation (EOI) and the end-of-exhalation (EOE) phases using a high-resolution (2.5mm isovoxel) 3D proton MRI, as well as a hybrid MRI which combines HP gas tagging MRI and a low-resolution (4.5mm isovoxel) proton MRI. A sparse tagging displacement vector field (tDVF) was derived from the HP gas tagging MRI by tracking the displacement of tagging grids between EOI and EOE. Using the tDVF and the high-resolution MR images, we determined the motion model of the entire thorax in the following two steps: 1) the DVF inside of lungs was estimated based on the sparse tDVF using a novel multi-step natural neighbor interpolation method; 2) the DVF outside of lungs was estimated from the DIR between the EOI and EOE images (Velocity AI). The derived motion model was then applied to the high-resolution EOI image to create a deformed EOE image, forming the virtual phantom where the motion model provides the ground truth of deformation. Five DIR methods were evaluated using the developed virtual phantom. Errors in DVF magnitude (Em) and angle (Ea) were determined and compared for each DIR method. Results: Among the five DIR methods, free form deformation produced DVF results that are most closely resembling the ground truth (Em=1.04mm, Ea=6.63°). The two DIR methods based on B-spline produced comparable results (Em=2.04mm, Ea=13.66°; and Em =2.62mm, Ea=17.67°), and the two optical-flow methods produced least accurate results (Em=7.8mm; Ea=53.04°; Em=4.45mm, Ea=31.02°). Conclusion: A methodology for constructing physiological-based virtual thorax phantom based on HP gas tagging MRI has been developed. Initial evaluation demonstrated its potential as an effective tool for robust evaluation of DIR in the lung.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Quantifying UAS Observation Error Variance Used in Data Assimilation Systems and Its Impact on Predictive Skill

Observation error determines the weights of the observations and background state used in data assimilation to generate analyses. Quantifying observation error is critical for the optimal assimilation of observational data sets. Uncrewed Aircraft System (UAS) observations have shown potential benefits in filling observational gaps in the lower atmosphere; however, characterization of their error characteristics has been limited. To optimize the use of UAS observations in numerical weather prediction, UAS observation error is estimated based on the 3‐cornered hat diagnostic approach which uses three independent estimates of the atmospheric state. This approach is applied to data from the 2018 Lower Atmospheric Profiling Studies at Elevation‐a Remotely‐piloted Aircraft Team Experiment field campaign using collocated UAS and rawinsonde observations along with output from a set of convection‐permitting model simulations. The estimated observation error values for UAS temperature, wind, and relative humidity measurements were found to be only weakly dependent on height AGL with mean values equal to 0.5°C, 0.8 m s −1 , and 3%, respectively. Only the newly estimated observation error for temperature differed from that previously used to assimilate commercial aircraft observations into global models (1.0°C). However, using this reduced temperature observation error produced more accurate mesoscale analyses and forecasts of both terrain‐driven flows and convection initiation generated by colliding outflow boundaries within the San Luis Valley of Colorado.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2011

Confirmation of IGR J01363 plus 6610 as a Be X-Ray Binary with Very Low Quiescent X-Ray Luminosity

The field containing the candidate High Mass X-ray Binary IGR J01 363+6610 was observed by XMM-Newton on 2009 July 31 for 28 ks. A Be star was previously suggested as the possible counterpart of the INTEGRAL source, and although Chandra, during a 2007 observation, did not detect an X-ray source at the position of the Be star, we find a variable source (XMMU 101 3549.5+661243) with an average X-ray flux of 2 x 10(exp -13)ergs/sq cm/s (0.2-12 keV, unabsorbed) at this position with XMM-Newton. The spectrum of this source is consistent with a hard power law with a photon index of r = 1.4+/-0.3 and a column density of N(sub H) = (15(+0.7/-0.5)) x 10(exp 22)/sq cm (90% confidence errors). These results, along with our optical investigation of other X-ray sources in the field, make the association with the Be star very likely, and the 2 kpc distance estimate for the Be star indicates an X-ray luminosity of 9.1 x 10(exp 31) ergs/s. This is lower than typical for a Be X-ray binary, and the upper limit on the luminosity was even lower ( < 1.4 x 10(exp 3)ergs/s assuming the same spectral model) during the Chandra observation. We discuss possible implications of the very low quiescent luminosity for the physical properties of IGR 101363+6610.

Tomsick, John A.↗

NASA NTRS2001

Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model

This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.

Shen, Samuel S. P.↗

DOE OSTI2019

Capturing Maize Stand Heterogeneity Across Yield-Stability Zones Using Unmanned Aerial Vehicles (UAV)

Despite the new equipment capabilities, uneven crop stands are still common occurrences in crop fields, mainly due to spatial heterogeneity in soil conditions, seedling mortality due to herbivore predation and disease, or human error. Non-uniform plant stands may reduce grain yield in crops like maize. Thus, detecting signs of variability in crop stand density early in the season provides critical information for management decisions and crop yield forecasts. Processing techniques applied on images captured by unmanned aerial vehicles (UAVs) has been used successfully to identify crop rows and estimate stand density and, most recently, to estimate plant-to-plant interval distance. Here, we further test and apply an image processing algorithm on UAV images collected from yield-stability zones in a commercial crop field. Our objective was to implement the algorithm to compare variation of plant-spacing intervals to test whether yield differences within these zones are related to differences in crop stand characteristics. Our analysis indicates that the algorithm can be reliably used to estimate plant counts (precision >95% and recall >97%) and plant distance interval (R2 ~0.9 and relative error <10%). Analysis of the collected data indicated that plant spacing variability differences were small among plots with large yield differences, suggesting that it was not a major cause of yield variability across zones with distinct yield history. This analysis provides an example of how plant-detection algorithms can be applied to improve the understanding of patterns of spatial and temporal yield variability.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Reducing model error using optimized galaxy selection: weak lensing cluster mass estimation

Galaxy clusters are one of the most powerful probes to study extensions of General Relativity and the Standard Cosmological Model. Upcoming surveys like the Vera Rubin Observatory’s Legacy Survey of Space and Time are expected to revolutionise the field, by enabling the analysis of cluster samples of unprecedented size and quality. To reach this era of high-precision cluster cosmology, the mitigation of sources of systematic error is crucial. A particularly important challenge is bias in cluster mass measurements induced by the mismodelling of photometric redshift estimates of source galaxies. This work proposes a method to optimise the source sample selection in cluster weak lensing analyses drawn from wide-field survey lensing catalogs to reduce the bias on reconstructed cluster masses. We use a combinatorial optimisation scheme and methods from variational inference to select galaxies in latent space to produce a probabilistic galaxy source sample catalog for highly accurate cluster mass estimation. We show that our method reduces the critical surface mass density Σ crit relative modelling bias on the 60-70% level, while maintaining up to 90% of galaxies. We highlight that our methodology has applications beyond cluster mass estimation as an approach to jointly combine galaxy selection and model inference under sources of systematics.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2025

Uncertainty Quantification for Empirical X-59 Sonic Boom Loudness Levels

Estimates of the total uncertainty for empirically determined loudness levels are documented when GRS (Ground Recording System) noise monitors are used to record X 59 sonic boom waveforms. The total uncertainty is characterized by combining nine different sources of uncertainty that may affect the apparent gain of the measurement chain. These uncertainty estimates are presented as expected measurement error relative to the true loudness level, and separate error estimates are provided for eight different noise metrics in which NASA has interest. The behavior of the Perceived Level (PL) metric is studied within the report body, while the total uncertainties for the seven other noise metrics are summarized in appendices for brevity. The effects of four sources of uncertainty are estimated simply from information found on hardware specification sheets provided by the manufacturer. However, mock acoustic recordings are created to estimate the effects of other sources because those effects are expected to induce spectral coloration, so they may vary with noise metric type and sound level. These sources are not well modeled by simple gain adjustments. Importantly, measurement error is computable when processing mock recordings since the true levels are knowable, which is not the case when processing data recorded in the field. Specifically, the true levels are knowable because the components of the mock recordings are separable – e.g., loudness levels of booms can be computed with or without superimposed background noise. Mock acoustic recordings also have the benefit of allowing analysis of sonic booms from vehicles that are not yet flying, like the X-59, since the mock recordings are created by combining vehicle-specific predicted waveforms with other audio sources. The estimates of total measurement error are documented as a function of the signal-to-noise ratio (SNR) of the loudness level, where the corrected SNR is computed while accounting for the effects of the method that is used to correct for background noise contamination when computing the noise metric values. The corrected SNR calculations used here can be applied to both mock recordings and in-field measurements, so the uncertainty of in-field recordings can be found using pre-computed lookup tables that identify the relationship between metric type, corrected SNR, and the expected measurement error.

Sonic Boom↗