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Mathematical models of human auditory perception (sound quality metrics) are explored for the purpose of fitting human subject data from a prior published study. A variety of linearizing transforms and techniques are employed. Ultimately, a model is identified that predicts 98.9% of the variance in average human subject ratings of crackliness of jet noise waveforms.
Earthquake monitoring in urban settings is essential but challenging, due to the strong anthropogenic noise inherent to urban seismic recordings. Here, we develop a deep-learning-based denoising algorithm, UrbanDenoiser, to filter out urban seismological noise. UrbanDenoiser strongly suppresses noise relative to the signals, because it was trained using waveform datasets containing rich noise sources from the urban Long Beach dense array and high signal-to-noise ratio (SNR) earthquake signals from the rural San Jacinto dense array. Application to the dense array data and an earthquake sequence in an urban area shows that UrbanDenoiser can increase signal quality and recover signals at an SNR level down to ~0 dB. Earthquake location using our denoised Long Beach data does not support the presence of mantle seismicity beneath Los Angeles but suggests a fault model featuring shallow creep, intermediate locking, and localized stress concentration at the base of the seismogenic zone.
Flat optical components, or metasurfaces, have transformed optical imaging, data storage, information processing, and biomedical applications by providing unprecedented control over light-matter interactions. These nano-engineered structures enable compact, multidimensional manipulation of light’s amplitude, phase, polarization, and wavefront, producing scalar and vector beams with unique properties such as orbital angular momentum and knotted topologies. This flexibility has potential applications in optical communication and imaging, particularly in complex environments such as atmospheric turbulence and undersea scattering. However, designing metasurfaces for shorter wavelengths, such as visible and ultraviolet light, remains challenging due to fabrication limitations and material absorption. Here, we introduce an innovative concept called topology imprinting using judiciously designed all-dielectric nonlinear optical metasurfaces to replicate desired waveforms at fundamental and harmonic frequencies, opening promising avenues for advanced photonic applications.
Earthquakes follow a well-known power-law size relation, with smaller events occurring much more often than larger events. Earthquake catalogs are thus dominated by small earthquakes yet are still missing a much larger number of even smaller events because of signal fidelity issues. To overcome these limitations, we applied a template-matching detection technique to the entire waveform archive of the regional seismic network in Southern California. This effort resulted in a catalog with 1.81 million earthquakes, a 10-fold increase, which provides important insights into the geometry of fault zones at depth, foreshock behavior and nucleation processes, and earthquake-triggering mechanisms. The rich detail resolved in this type of catalog will facilitate the next generation of analyses of earthquakes and faults.
The amplitude−temporal characteristics of a supershort avalanche electron beam (SAEB) with an amplitude of up to 100 A, as well as of the breakdown voltage and discharge current, are studied experimentally with a picosecond time resolution. The waveforms of discharge and SAEB currents are synchronized with those of the voltage pulses. It is shown that the amplitude−temporal characteristics of the SAEB depend on the gap length and the designs of the gas diode and cathode. The mechanism for the generation of runaway electron beams in atmospheric-pressure gases is analyzed on the basis of the obtained experimental data.
Nonstationary processes in atmospheric-pressure glow discharge manifest themselves in spontaneous transitions from the normal glow discharge into a spark. In the experiments, both so-called completed transitions in which a highly conductive constricted channel arises and incomplete transitions accompanied by the formation of a diffuse channel are observed. A model of the positive column of a discharge in air is elaborated that allows one to interpret specific features of the discharge both in the stationary stage and during its transition into a spark and makes it possible to calculate the characteristic oscillatory current waveforms for completed transitions into a spark and aperiodic ones for incomplete transitions. The calculated parameters of the positive column in the glow discharge mode agree well with experiment. Data on the densities of the most abundant species generated in the discharge (such as atomic oxygen, metastable nitrogen molecules, ozone, nitrogen oxides, and negative oxygen ions) are presented.
The spectrum and waveforms of broadband terahertz-radiation pulses generated by low-temperature In{sub 0.53}Ga{sub 0.47}As epitaxial films under femtosecond laser pumping are investigated by terahertz time-resolved spectroscopy. The In{sub 0.53}Ga{sub 0.47}As films are fabricated by molecular-beam epitaxy at a temperature of 200°C under different arsenic pressures on (100)-oriented InP substrates and, for the first time, on (411)A InP substrates. The surface morphology of the samples is studied by atomic-force microscopy and the structural quality is established by high-resolution X-ray diffraction analysis. It is found that the amplitude of terahertz radiation from the LT-InGaAs layers on the (411)A InP substrates exceeds that from similar layers formed on the (100) InP substrates by a factor of 3–5.
This paper develops a novel limited-memory method to solve dynamic optimization problems. The memory requirements for such problems often present a major obstacle, particularly for problems with PDE constraints such as optimal flow control, full waveform inversion, and optical tomography. In these problems, PDE constraints uniquely determine the state of a physical system for a given control; the goal is to find the value of the control that minimizes an objective. While the control is often low dimensional, the state is typically more expensive to store. This paper suggests using randomized matrix approximation to compress the state as it is generated and shows how to use the compressed state to reliably solve the original dynamic optimization problem. Concretely, the compressed state is used to compute approximate gradients and to apply the Hessian to vectors. The approximation error in these quantities is controlled by the target rank of the sketch. This approximate first- and second-order information can readily be used in any optimization algorithm. As an example, we develop a sketched trust-region method that adaptively chooses the target rank using a posteriori error information and provably converges to a stationary point of the original problem. Numerical experiments with the sketched trust-region method show promising performance on challenging problems such as the optimal control of an advection-reaction-diffusion equation and the optimal control of fluid flow past a cylinder.
Abstract P-type point contact (PPC) HPGe detectors are a leading technology for rare event searches due to their excellent energy resolution, low thresholds, and multi-site event rejection capabilities. We have characterized a PPC detector’s response to $$\alpha $$ α particles incident on the sensitive passivated and p $$^+$$ + surfaces, a previously poorly-understood source of background. The detector studied is identical to those in the Majorana Demonstrator experiment, a search for neutrinoless double-beta decay ( $$0\nu \beta \beta $$ 0 ν β β ) in $$^{76}$$ 76 Ge. $$\alpha $$ α decays on most of the passivated surface exhibit significant energy loss due to charge trapping, with waveforms exhibiting a delayed charge recovery (DCR) signature caused by the slow collection of a fraction of the trapped charge. The DCR is found to be complementary to existing methods of $$\alpha $$ α identification, reliably identifying $$\alpha $$ α background events on the passivated surface of the detector. We demonstrate effective rejection of all surface $$\alpha $$ α events (to within statistical uncertainty) with a loss of only 0.2% of bulk events by combining the DCR discriminator with previously-used methods. The DCR discriminator has been used to reduce the background rate in the $$0\nu \beta \beta $$ 0 ν β β region of interest window by an order of magnitude in the Majorana Demonstrator and will be used in the upcoming LEGEND-200 experiment.
Primary challenges for current and future precision neutrino experiments using liquid argon time projection chambers (LArTPCs) include understanding detector effects and quantifying the associated systematic uncertainties. This paper presents a novel technique for assessing and propagating LArTPC detector-related systematic uncertainties. The technique makes modifications to simulation waveforms based on a parameterization of observed differences in ionization signals from the TPC between data and simulation, while remaining insensitive to the details of the detector model. The modifications are then used to quantify the systematic differences in low- and high-level reconstructed quantities. This approach could be applied to future LArTPC detectors, such as those used in SBN and DUNE.
Pulse shape discrimination (PSD) is widely used in particle and nuclear physics. Specifically in liquid scintillator detectors, PSD facilitates the classification of different particle types based on their energy deposition patterns. This technique is particularly valuable for studies of the diffuse supernova neutrino background (DSNB), nucleon decay, and dark matter searches. This paper presents a detailed investigation of the PSD technique, applied in the DSNB search performed with the Jiangmen Underground Neutrino Observatory (JUNO). Instead of using conventional cut-and-count methods, we employ methods based on boosted decision trees and neural networks and compare their capability to distinguish the DSNB signals from the atmospheric neutrino neutral-current background events. The two methods demonstrate comparable performance, resulting in a 50–80% improvement in signal efficiency compared to a previous study performed for JUNO (An et al. [JUNO] in J Phys G 43(3):030401, 2016). Moreover, we study the dependence of the PSD performance on the visible energy and final state composition of the events and find a significant dependence on the presence/absence of 11 C. Finally, we evaluate the impact of the detector effects (photon propagation, PMT dark noise, and waveform reconstruction) on the PSD performance.
High density interconnects are required for increased input/output for microelectronics applications, incentivizing the development of Cu electrochemical deposition (ECD) processes for high aspect ratio through-silicon vias (TSVs). This work outlines Cu ECD processes for 62.5 μ m diameter TSVs, etched into a 625 μ m thick silicon substrate, a 10:1 aspect ratio. Cu ECD in high aspect ratio features relies on a delicate balance of electrolyte composition, solution replenishment, and applied voltage. Implementing a CuSO 4 -H 2 SO 4 electrolyte, which contains suppressor and a low chloride concentration, allows for a tunable relationship between applied voltage and localized deposition in the vias. A stepped potential waveform was applied to move the Cu growth front from the bottom of the via to the top. Sample characterization was performed through mechanical cross-sections and X-ray computed tomography (CT) scans. The CT scans revealed small seam voids in the Cu electrodeposit, and process parameters were tuned accordingly to produce void-free Cu features. During the voltage-controlled experiments, measured current data showed a characteristic current minimum, which was identified as an endpoint detection method for Cu deposition in these vias. We believe this is the first report of this novel endpoint detection method for TSV filling.
A conditionally guided generative latent diffusion process that is trained on a set of experimental processing parameters and their associated resulting electron microscope images of the electrodeposition process is able to interpolate between processing parameters in a physically consistent way. Electrodeposition of rhenium with pulse and pulse-reverse waveforms is used as a model system, and the process is adaptable to other electrodeposition, electropolishing, or corrosion processes. The method is able to extrapolate, predicting estimates of material morphologies for experimental setups unseen in the training data. The results are demonstrated with experimental data.
Slycat™ is a web-based system for analysis of large, high-dimensional data such as that produced by High Performance Computing (HPC) platforms. The Slycat™ server integrates data ingestion, scalable analysis, data management, and visualization with commodity web clients using a multi-tiered hierarchy of data and model storage. Analysis inputs are pushed or pulled into Slycat™, where models are generated and analysis artifacts are stored in a project database. These artifacts are the basis for visualizations that are delivered to users’ desktops through ordinary web browsers, eliminating the need to build and deploy platform-specific client applications. Slycat™ currently provides four types of analysis: canonical correlation analysis (CCA) to model relationships between inputs and output metrics, time series analysis featuring clustering and comparative visualization of waveforms, parameter space visualization including remote retrieval of images and movies from HPC disks, and VideoSwarm for modeling video similarities over time.
The purpose of this software is to interpret and analyze data taken using the NI PXIe-5171R digitizer based data acquisition system for the UCNB and Nab experiments. The detection and data acquisition systems are identical for the 2 experiments, with some differences in analysis requirements. The software converts raw binary files produced by the NI DAQ into ROOT TTree format, performs waveform analysis using trapezoidal filter algorithms, pulse fitting, and noise analysis routines, and applies variable criteria to identify valid events in the data stream. The software will be used to perform analysis of the events for multi-channel coincidences, timing and energy studies, and event rates under different experimental conditions.
MTtime is a software tool developed for time domain inversion of complete seismic waveform data to obtain the seismic moment tensor. It supports both deviatoric and full moment tensor inversions as well as 1-D and 3-D basis Green's functions.
This is firmware and software designed for a Zynq UltraScale+ RFSoC chip made by Xilinx, specifically targeted to laser-based individual addressing of trapped ion qubits via a multi-channel AOM. The design features 8 dual-tone outputs with frequencies up to 409 MHz that can be controlled statically or dynamically via on-chip cubic spline interpolation engines that allow one to generate sequences of output waveforms with fast modulation of frequency, phase, amplitude, and z-rotations that are smooth/discrete and can run simultaneously across all parameters/tones. It also features global phase alignment on all tones regardless of frequency and the ability to return to a known phase/frequency at any later time on a per-tone basis without the need for manual bookkeeping. Acoustic and optical cross-talk can also be destructively canceled by adding neighboring outputs with variable amplitude/phase/delay. Each tone can be optionally frequency locked to an external RF source. SAND2020-12703 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
The labquake_future_prediction code is a collection of python modules and scripts that serves as supporting information for the article “Predicting future laboratory fault friction through deep learning” for publication in the journal of “Geophysical Research Letters”. It is designed to predict laboratory fault slips in the immediate future by scanning continuous acoustic emission (AE) waveforms recorded in laboratory biaxial shear experiments. The predictions are made with a deep learning model based on convolutional encoder-decoder (CED) models and the Transformer model primarily developed for Natural Language Processing (NLP). The deep learning model is trained with the tensorflow package using publicly available laboratory data sets in standard binary file format in numpy. The utility functions for reading data files, configuring model hyperparameters, constructing the CED and Transformer models, training and testing of the models are defined in python module files. The workflow of training the models for labquake future predictions and the multiple GPU’s rapid model hyperparameter optimization as described in the journal article, are demonstrated in accompanying python script files and Jupyter notebooks.