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At least 109 records · Page 6

Consistency of Post-Newtonian Waveforms with Numerical Relativity

General relativity predicts the gravitational radiation signatures of mergers of compact binaries, such as coalescing binary black hole systems. Derivations of waveform predictions for such systems are required for optimal scientific analysis of observational gravitational wave data, and have so far been achieved primarily with the aid of the post-Newtonian (PN) approximation. The quality of this treatment is unclear, however, for the important late inspiral portion. We derive late-inspiral waveforms via a complementary approach, direct numerical simulation of Einstein's equations, which has recently matured sufficiently for such applications. We compare waveform phasing from simulations covering the last approximately 14 cycles of gravitational radiation from an equal-mass binary system of nonspinning black holes with the corresponding 3PN and 3.5PN orbital phasing. We find agreement consistent with internal error estimates based on either approach at the level of one radian over approximately 10 cycles. The result suggests that PN waveforms for this system are effective roughly until the system reaches its last stable orbit just prior to the final merger/

Baker, John G.↗

LISA Parameter Estimation using Numerical Merger Waveforms

Coalescing supermassive black holes are expected to provide the strongest sources for gravitational radiation detected by LISA. Recent advances in numerical relativity provide a detailed description of the waveforms of such signals. We present a preliminary study of LISA's sensitivity to waveform parameters using a hybrid numerical/analytic waveform describing the coalescence of two equal-mass, nonspinning black holes. The Synthetic LISA software package is used to simulate the instrument response and the Fisher information matrix method is used to estimate errors in the waveform parameters. Initial results indicate that inclusion of the merger signal can significantly improve the precision of some parameter estimates. For example, the median parameter errors for an ensemble of systems with total redshifted mass of 10(exp 6) deg M solar mass at a redshift of z is approximately 1 were found to decrease by a factor of slightly more than two when the merger was included.

Thorpe, J. I.↗

Accurate Waveforms for Non-spinning Binary Black Holes using the Effective-one-body Approach

Using numerical relativity as guidance and the natural flexibility of the effective-one-body (EOB) model, we extend the latter so that it can successfully match the numerical relativity waveforms of non-spinning binary black holes during the last stages of inspiral, merger and ringdown. Here, by successfully, we mean with phase differences < or approx. 8% of a gravitational-wave cycle accumulated until the end of the ringdown phase. We obtain this result by simply adding a 4 post-Newtonian order correction in the EOB radial potential and determining the (constant) coefficient by imposing high-matching performances with numerical waveforms of mass ratios m1/m2 = 1,2/3,1/2 and = 1/4, m1 and m2 being the individual black-hole masses. The final black-hole mass and spin predicted by the numerical simulations are used to determine the ringdown frequency and decay time of three quasi-normal-mode damped sinusoids that are attached to the EOB inspiral-(plunge) waveform at the light-ring. The accurate EOB waveforms may be employed for coherent searches of gravitational waves emitted by non-spinning coalescing binary black holes with ground-based laser-interferometer detectors.

Buonanno, Alessandra↗

An MSK Waveform for Radar Applications

We introduce a minimum shift keying (MSK) waveform developed for use in radar applications. This waveform is characterized in terms of its spectrum, autocorrelation, and ambiguity function, and is compared with the conventionally used bi-phase coded (BPC) radar signal. It is shown that the MSK waveform has several advantages when compared with the BPC waveform, and is a better candidate for deep-space radar imaging systems such as NASA's Goldstone Solar System Radar.

Minimum shift keying↗

Simulation of Satellite, Airborne and Terrestrial LiDAR with DART (I):Waveform Simulation with Quasi-Monte Carlo Ray Tracing

Light Detection And Ranging (LiDAR) provides unique data on the 3-D structure of atmosphere constituents and the Earth's surface. Simulating LiDAR returns for different laser technologies and Earth scenes is fundamental for evaluating and interpreting signal and noise in LiDAR data. Different types of models are capable of simulating LiDAR waveforms of Earth surfaces. Semi-empirical and geometric models can be imprecise because they rely on simplified simulations of Earth surfaces and light interaction mechanisms. On the other hand, Monte Carlo ray tracing (MCRT) models are potentially accurate but require long computational time. Here, we present a new LiDAR waveform simulation tool that is based on the introduction of a quasi-Monte Carlo ray tracing approach in the Discrete Anisotropic Radiative Transfer (DART) model. Two new approaches, the so-called "box method" and "Ray Carlo method", are implemented to provide robust and accurate simulations of LiDAR waveforms for any landscape, atmosphere and LiDAR sensor configuration (view direction, footprint size, pulse characteristics, etc.). The box method accelerates the selection of the scattering direction of a photon in the presence of scatterers with non-invertible phase function. The Ray Carlo method brings traditional ray-tracking into MCRT simulation, which makes computational time independent of LiDAR field of view (FOV) and reception solid angle. Both methods are fast enough for simulating multi-pulse acquisition. Sensitivity studies with various landscapes and atmosphere constituents are presented, and the simulated LiDAR signals compare favorably with their associated reflectance images and Laser Vegetation Imaging Sensor (LVIS) waveforms. The LiDAR module is fully integrated into DART, enabling more detailed simulations of LiDAR sensitivity to specific scene elements (e.g., atmospheric aerosols, leaf area, branches, or topography) and sensor configuration for airborne or satellite LiDAR sensors.

Gastellu-Etchegorry, Jean-Philippe↗

Surface Cancellation in Wideband Ground Penetrating Radar Employing Genetic Algorithm AI for Waveform Synthesis

This paper presents a wideband 600-1200 MHz ground penetrating radar (GPR) system for sub-surface exploration of the moon and other planetary bodies. The presented radar system uses an arbitrary waveform generator (AWG) to directly produce the frequency-modulated continuous wave (FMCW) chirp waveform. To address the key challenges of Tx-to-Rx leakage and surface reflections, the system uses a second AWG channel coupled directly to the receiver that provides a cancellation waveform to mitigate the unwanted signals. The system also uses a genetic algorithm AI engine which assesses the radar IF and iteratively improves the parameters of the cancellation waveform injected at the receiver.

Chang, Frank↗

Higher-Order Statistical Moments of Predicted Sonic Boom Waveforms Through Turbulence

Sonic booms generated by supersonic aircraft are affected by turbulence in the atmospheric boundary layer through which they propagate. Turbulence effects lead to random variability of the sonic boom waveforms measured on the ground, complicating the prediction of such waveforms. As an initial effort to predict the waveform variability, the solution of the coherent or mean sonic boom waveform has previously been formulated by the author. The current paper extends the formulation to derive an expression of the second-order statistical moment necessary to calculate the variance of the spectral amplitudes. Since the derivation uses a full wave equation, results using the derived expression will be compared with those obtained using a parabolic approximation. In order to fully quantify the uncertainty of our predictions, the higher order statistical moments are also formulated and are shown to be approximately zero. Consequently, the probability density function (pdf) of the spectral amplitudes is predicted to be Gaussian. The formulation is further extended to determine the pdf of the loudness of sonic booms and quantify the uncertainties associated with the loudness prediction. Results from the formulation are compared with available flight test data.

sonic booms↗

NEON AOP Survey of Upper East River CO Watersheds: Waveform LiDAR Binary Data

The waveform Light Detection and Ranging (LiDAR) data in this package were generated through a National Ecological Observatory Network Airborne Observation Platform (NEON AOP) acquisition over watersheds of interest surrounding Crested Butte, Colorado. The remote sensing imagery acquired by the NEON AOP supports an interdisciplinary project on hydrology, biogeochemistry, and ecosystem functioning in a snow-dominated headwater environment. These waveform LiDAR data enable spatially continuous estimation of vegetation structure parameters to facilitate analyses of the major environmental drivers of structural and compositional variability. The package contains 97 compressed file archives in 7-zip (.7z) format, each corresponding to one acquisition flightpath. Within each .7z archive is a set of constituent files describing properties of the LiDAR waveforms, such as return intensity, geolocation, outgoing pulse and other behavior of the sensor and signals. Once downloaded, the files must first be unzipped using the widely distributed command-line software utility 7z, using the command '7z x \[filename\].7z \[target_directory\]'. All files within the .7z archives can be opened in IDL, MatLab, or the open-source R statistical computing environment. Further details about the data package are in the attached user guide (neon_aop_crbu_waveformlidar_userguide.pdf). This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗

Waveform modelling for the Laser Interferometer Space Antenna

LISA, the Laser Interferometer Space Antenna, will usher in a new era in gravitational-wave astronomy. As the first anticipated space-based gravitational-wave detector, it will expand our view to the millihertz gravitational-wave sky, where a spectacular variety of interesting new sources abound: from millions of ultra-compact binaries in our Galaxy, to mergers of massive black holes at cosmological distances; from the early inspirals of stellar-mass black holes that will ultimately venture into the ground-based detectors’ view to the death spiral of compact objects into massive black holes, and many sources in between. Central to realising LISA’s discovery potential are waveform models, the theoretical and phenomenological predictions of the pattern of gravitational waves that these sources emit. This White Paper is presented on behalf of the Waveform Working Group for the LISA Consortium. It provides a review of the current state of waveform models for LISA sources, and describes the significant challenges that must yet be overcome.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Second release of the CoRe database of binary neutron star merger waveforms

Abstract We present the second data release of gravitational waveforms from binary neutron star (BNS) merger simulations performed by the Computational Relativity ( CoRe ) collaboration. The current database consists of 254 different BNS configurations and a total of 590 individual numerical-relativity simulations using various grid resolutions. The released waveform data contain the strain and the Weyl curvature multipoles up to ℓ = m = 4 . They span a significant portion of the mass, mass-ratio, spin and eccentricity parameter space and include targeted configurations to the events GW170817 and GW190425. CoRe simulations are performed with 18 different equations of state, seven of which are finite temperature models, and three of which account for non-hadronic degrees of freedom. About half of the released data are computed with high-order hydrodynamics schemes for tens of orbits to merger; the other half is computed with advanced microphysics. We showcase a standard waveform error analysis and discuss the accuracy of the database in terms of faithfulness. We present ready-to-use fitting formulas for equation of state-insensitive relations at merger (e.g. merger frequency), luminosity peak, and post-merger spectrum.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Maximizing the Radar Generalized Image Quality Equation for Bistatic SAR Using Waveform Frequency Agility

The radar generalized image quality equation (RGIQE) is a metric used to measure both monostatic and bistatic synthetic aperture radar (BSAR) image quality, it is a function of signal-to-noise ratio (SNR) and 2-D bandwidth. The 2-D bandwidth is equal to the area of the transfer function’s (TF) passband region. With the exception of side-looking monostatic geometries, almost all monostatic and bistatic geometries have skewed passband shapes when waveform frequency parameters remain unchanged from pulse to pulse. Most synthetic aperture radar (SAR) applications require a rectangular-shaped passband region, this is achieved by inscribing a rectangular region within the skewed intrinsic passband region. Increasing skewness results in less inscription area reducing 2-D bandwidth, image SNR, and thus RGIQE capacity. In this article, a waveform with frequency agility is used to rectify the skewness that degrades RGIQE capacity. By changing the waveform’s center frequency and instantaneous bandwidth from pulse to pulse in a particular manner, the intrinsic passband region can be de-skewed. The de-skewed shape maximizes the inscription area thus maximizing 2-D bandwidth, image SNR, and RGIQE capacity. Here, three examples are given in this article, one monostatic geometry, and two bistatic geometries. RGIQE capacity is increased by 52.02%, 44.42%, and 79.09% for the three examples.

47 OTHER INSTRUMENTATION↗

Voltage waveform tailoring for high aspect ratio plasma etching of SiO 2 using Ar/CF 4 /O 2 mixtures: Consequences of ion and electron distributions on etch profiles

The quality of high aspect ratio (HAR) features etched into dielectrics for microelectronics fabrication using halogen containing low temperature plasmas strongly depends on the energy and angular distribution of the incident ions (IEAD) onto the wafer, as well as potentially that of the electrons (EEAD). Positive ions, accelerated to high energies by the sheath electric field, have narrow angular spreads and can penetrate deeply into HAR features. Electrons typically arrive at the wafer with nearly thermal energy and isotropic angular distributions and so do not directly penetrate deeply into features. These differences can lead to positive charging of the insides of the features that can slow etching rates and produce geometric defects such as twisting. In this work, we computationally investigated the plasma etching of HAR features into SiO 2 using tailored voltage waveforms in a geometrically asymmetric capacitively coupled plasma sustained in an Ar/CF 4 /O 2 mixture at 40 mTorr. The tailored waveform consisted of a sinusoidal wave and its higher harmonics with a fundamental frequency of 1 MHz. We found that some degree of control of the IEADs and EEADs is possible by adjusting the phase of higher harmonics φ through the resulting generation of electrical asymmetry and electric field reversal. However, the IEADs and EEADs cannot easily be separately controlled. The control of IEADs and EEADs is inherently linked. The highest quality feature was obtained with a phase angle φ = 0° as this value generated the largest (most negative) DC self-bias and largest electric field reversal for accelerating electrons into the feature. That said, the consequences of voltage waveform tailoring (VWT) on etched features are dominated by the change in the IEADs. Although VWT does produce EEADs with higher energy and narrower angular spread, the effect of these electrons on the feature compared to thermal electrons is not large. Finally, this smaller impact of VWT produced EEADs is attributed to thermal electrons being accelerated into the feature by electric fields produced by the positive in-feature charging.

47 OTHER INSTRUMENTATION↗

A highly capable arbitrary waveform generator for next generation radar systems

We are developing an Arbitrary Waveform Generator (AWG) to provide enhanced capability for radar applications. The current design will accommodate two waveform generators on a single unit for dual frequency operation. The basic architecture of this unit employs a Field Programmable Gate Array (FPGA) and a high speed and high precision Digital to Analog Converter (DAC) for direct digital synthesis. This AWG will be capable of up to 450 MHz bandwidth with ability for frequency notching. Phase fidelity of less than 1.2(sup o) deviation RMS is also achievable. This AWG operates with lower power consumption as compared with other waveform generators, which is advantageous for future spaceborne applications. This will enable radars to return higher precision data, to be reduced in complexity, and to operate in any band without interfering with dedicated bandwidths.

chirp generators↗

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗

A new waveform analysis technique to extract good energy and position resolution from a dual-axis duo-lateral position-sensitive detector

The dual-axis duo-lateral position-sensitive silicon detector was developed to detect charged particles with high quality position and energy resolution. When these detectors were used with conventional signal processing electronics, an empirically determined correction was used to improve energy resolution. In this work, the waveforms from the detector after preamplification are studied in detail to investigate position information contained in the waveforms. A 7.22 MeV/nucleon alpha particle beam was impinged directly on a masked dual-axis duo-lateral detector. Data obtained using a 228 Th alpha particle source was also used. Here, by studying the waveform characteristics that give rise to the position-dependent distortions, a new summed trigger analysis method has been developed to significantly improve linearity in position reconstruction without sacrificing energy resolution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Evaluation of Waveform Profiles for Traveling Wave Ion Mobility Separations in Structures for Lossless Ion Manipulations

Structures for lossless ion manipulations (SLIM) have recently enabled a powerful implementation of traveling wave ion mobility spectrometry (TWIMS) for ultra-high resolution separations. However, optimization of experimental parameters remains challenging. Most TWIMS separations have utilized square-shaped waveforms applied by time-dependent voltage stepping across a repeating sets of electrodes; however, alternative waveforms may provide further improvements to resolution. In this work, we characterize five waveforms (including square and sine) in terms of their transmission efficiency, IMS resolution, and resolving power, and explore the effects of TW amplitude and speed on the performance of each. We found, consistent with previous work, separations were generally improved with higher TW amplitudes, moderately improved by lower speeds (limited by ion ‘surfing’ with the waves), and found decreases in signal intensity at the extremes of operating conditions. The triangle and asymmetric ‘ramp forward’ shaped profiles were found to provide modestly greater resolution and resolving power, an observation we tentatively attribute to their relatively uniform fields and minimal low-field regions.

59 BASIC BIOLOGICAL SCIENCES↗

SeismoGen: Seismic Waveform Synthesis Using GAN With Application to Seismic Data Augmentation

Abstract Detecting earthquake arrivals within seismic time series can be a challenging task. Visual, human detection has long been considered the gold standard but requires intensive manual labor that scales poorly to large data sets. In recent years, automatic detection methods based on machine learning have been developed to improve the accuracy and efficiency. However, the accuracy of those methods relies on access to a sufficient amount of high‐quality labeled training data, often tens of thousands of records or more. We aim to resolve this dilemma by answering two questions: (1) provided with a limited amount of reliable labeled data, can we use them to generate additional, realistic synthetic waveform data? and (2) can we use those synthetic data to further enrich the training set through data augmentation, thereby enhancing detection algorithms? To address these questions, we use a generative adversarial network (GAN), a type of machine learning model which has shown supreme capability in generating high‐quality synthetic samples in multiple domains. Once trained, our GAN model is capable of producing realistic seismic waveforms of multiple labels (noise and event classes). Applied to real Earth seismic data sets in Oklahoma, we show that data augmentation from our GAN‐generated synthetic waveforms can be used to improve earthquake detection algorithms in instances when only small amounts of labeled training data are available.

Wang, Tiantong↗

CANVAS: An Adjoint Waveform Tomography Model of California and Nevada

Abstract We present the California‐Nevada Adjoint Simulations (CANVAS) model, an adjoint waveform tomography model of the crust and uppermost mantle of the states of California and Nevada. We used WUS256 (Rodgers et al., 2022, https://doi.org/10.1029/2022jb024549 ) as the starting model and iteratively decreased the minimum period of CANVAS from 30 to 12 s. CANVAS was iterated in two distinct stages: the first stage with source mechanisms from the Global Centroid Moment Tensor (GCMT) catalog and the second stage with inverted moment tensors (MT) using the CANV_WUS model (Doody et al., 2023, https://doi.org/10.1029/2023jb026463 ). We show that updating the MTs with 3D Green's functions improved waveform fits and azimuthal coverage of windowed data used to calculate the gradients. As for the model itself, we improved waveform fits over WUS256, particularly in the dispersed surface waves. CANVAS resolved tectonic features seen in other models and accurately defined the depth to basement of major basins, including the Central Valley and the Ventura Basin. We propose CANVAS as a starting model for crustal tomography models on smaller scales.

58 GEOSCIENCES↗