Transient Detection Research Articles

Toward real-time detection of unmodeled gravitational wave transients using convolutional neural networks

Toward real-time detection of unmodeled gravitational wave transients using convolutional neural networks

Generating tailored high frequency features in core collapse supernova gravitational wave signals applicable in LIGO interferometric studies

In this article, we introduce a methodology based on an analytical model of a damped harmonic oscillator subject to random forcing to generate transient gravitational wave signals. Such a model incorporates a simulated linear high-frequency component that mirrors the growing characteristic frequency over time observed in numerical simulations of core-collapse supernova gravitational wave signals. Unlike traditional numerical simulations, the method proposed in this study requires minimal computational resources, which makes it particularly advantageous for tasks such as data analysis, detection, and reconstruction of gravitational wave transients. To verify the physical accuracy of the generated signals, they are compared against the amplitude spectral of current LIGO interferometers and a 3D numerical simulation of a core-collapse supernova gravitational wave signal from the Andresen et al. 2017 model s15.nr. The results indicate that this approach is effective in generating scalable signals that align with LIGO interferometric data, offering potential utility in various gravitational wave transient investigations.

CalTrig: A GUI-Based Machine Learning Approach for Decoding Neuronal Calcium Transients in Freely Moving Rodents.

Advances in in vivo Ca2+ imaging using miniatured microscopes have enabled researchers to study single-neuron activity in freely moving animals. Tools such as MiniAN and CalmAn have been developed to convert Ca2+ visual signals to numerical information, collectively referred to as CalV2N. However, substantial challenges remain in analyzing the large datasets generated by CalV2N, particularly in integrating data streams, evaluating CalV2N output quality, and reliably and efficiently identifying Ca2+ transients. In this study, we introduce CalTrig, an open-source graphical user interface (GUI) tool designed to address these challenges at the post-CalV2N stage of data processing. CalTrig integrates multiple data streams, including Ca2+ imaging, neuronal footprints, Ca2+ traces, and behavioral tracking, and offers capabilities for evaluating the quality of CalV2N outputs. It enables synchronized visualization and efficient Ca2+ transient identification. We evaluated four machine learning models (i.e., GRU, LSTM, Transformer, and Local Transformer) for Ca2+ transient detection. Our results indicate that the GRU model offers the highest predictability and computational efficiency, achieving stable performance across training sessions, different animals and even among different brain regions. The integration of manual, parameter-based, and machine learning-based detection methods in CalTrig provides flexibility and accuracy for various research applications. The user-friendly interface and low computing demands of CalTrig make it accessible to neuroscientists without programming expertise. We further conclude that CalTrig enables deeper exploration of brain function, supports hypothesis generation about neuronal mechanisms, and opens new avenues for understanding neurological disorders and developing treatments.

Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams

We present Lightcurve Anomaly Identification and Similarity Search (LAISS), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly Zwicky Transient Facility (ZTF) Alert Stream via the ANTARES broker, identifying a manageable ∼1–5 candidates per night for expert vetting and coordinating follow-up observations. Our method leverages statistical light-curve and contextual host galaxy features within a random forest classifier, tagging transients of rare classes (spectroscopic anomalies), of uncommon host galaxy environments (contextual anomalies), and of peculiar or interaction-powered phenomena (behavioral anomalies). Moreover, we demonstrate the power of a low-latency (∼ms) approximate similarity search method to find transient analogs with similar light-curve evolution and host galaxy environments. We use analogs for data-driven discovery, characterization, (re)classification, and imputation in retrospective and real-time searches. To date, we have identified ∼50 previously known and previously missed rare transients from real-time and retrospective searches, including but not limited to superluminous supernovae (SLSNe), tidal disruption events, SNe IIn, SNe IIb, SNe I-CSM, SNe Ia-91bg-like, SNe Ib, SNe Ic, SNe Ic-BL, and M31 novae. Lastly, we report the discovery of 325 total transients, all observed between 2018 and 2021 and absent from public catalogs (∼1% of all ZTF Astronomical Transient reports to the Transient Name Server through 2021). These methods enable a systematic approach to finding the “needle in the haystack” in large-volume data streams. Because of its integration with the ANTARES broker, LAISS is built to detect exciting transients in Rubin data.

Analysis of waves dynamics in a rotating detonation combustor fueled by kerosene

The wave dynamics play a crucial role in the operation characteristics of the rotating detonation engine. We conducted numerical simulations of a rotating detonation combustor (RDC) using multicomponent reactive Navier–Stokes equations coupled with a discrete phases model. The RDC in this research employs a configuration with multiple coaxial injectors supplying oxygen-enriched air and kerosene spray at room temperature. To accurately identify and analyze waves within the RDC, we proposed a three-dimensional transient detonation wave detection method based on the combined parameters of normal Mach number and heat release rate in the flow field. Two typical wave modes, referred to as single-wave mode and counter-waves mode, are identified and then selected to conduct a detailed wave dynamics analysis. The general wave behavior is discussed, and velocity deficit is compared for these two wave modes. For the single-wave mode, intermittent micro-explosions are observed generating retonation waves periodically in the unburnt pockets behind the rotating detonation shock front. For the counter-waves mode, we analyzed the collision process of the two waves and the coupling/decoupling of the shock front with the detonative heat release zone, revealing the reason for significant velocity deficits in this wave mode. This research demonstrates that micro-explosions intermittently occur in the multiphase RDC in both single-wave and counter-waves modes and generate micro explosion shock waves periodically, which influence the complicated wave dynamics behavior.

A threshold activation-based simplified Lv's transform algorithm for transient multi-component linear frequency modulation signals analysis.

The high sampling rate in modern digital systems generates a large scale of data. To address the computational burden, this paper proposes a threshold activation-based simplified Lv's transform (SLVT) algorithm to analyze the transient multi-component linear frequency modulation signals. Only the signal arrival can trigger the signal analysis. This mechanism alleviates the computation pressure because of the sparsity of signals. The threshold activation mechanism enables transient signal detection and sampling rate adjustment, thereby enhancing the efficiency and effectiveness of the analytical process. The simplified Lv's transform (LVT) removes redundant computations on stretch weighting and the Discrete Fourier Transform (DFT) in Lv's transform. SLVT uses the efficient Bluestein chirp-z algorithm to implement the stretch keystone transform. The comparison results show that SLVT reduces the computational complexity of the original LVT by at least 30.8%. This algorithm exhibits superior performance compared to other advanced signal processing methods, such as discrete chirp Fourier transform, fractional Fourier transform, and Radon Wigner transform algorithms, in terms of parameter extraction accuracy, computational complexity, and execution time. Moreover, the implementation of a field programmable gate array accelerates SLVT computing by a factor of 116 in comparison to the CPU (Central Processing Unit) platform.

Start-Up and Steady-State Regimes Automatic Separation in Induction Motors by Means of Short-Time Statistics

Induction motors are widely used machines in a variety of applications as primary components for generating rotary motion. This is mainly due to their high efficiency, robustness, and ease of control. Despite their high robustness, these machines can experience failures throughout their lifespan due to various mechanical, electrical, and environmental factors. To prevent irreversible failures and all the implications and costs associated with breakdowns, various methodologies have been developed over the years. Many of these methodologies have focused on analyzing various physical quantities, either during start-up transients or during steady-state operations. This involves the use of specific techniques depending on the focus of the methodology (start-up transients or steady-state) to obtain optimal results. In this regard, it is of great importance to develop methods capable of separating and detecting the start-up transient of the motor from the steady state. This will enable the development of automatic diagnostic methodologies focused on the specific operating state of the motor. This paper proposes a methodology for the automatic detection of start-up transients in induction motors by using magnetic stray flux signals and processing by means of statistical indicators in time-sliding windows, the calculation of variances with a proposed method, and obtaining optimal values for the design parameters by using a Particle Swarm Optimization (PSO). The results obtained demonstrate the effectiveness of the proposed method for the start-up and steady-state regimes automatic separation, which is validated on a 0.746 kW induction motor supplied by a variable frequency drive (VFD).

How Do Schumann Resonance Frequency Changes in the Vertical Electric Field Component Reflect Global Lightning Dynamics at Different Time Scales?

AbstractThe electromagnetic waves in the Schumann resonance (SR) frequency range (<100 Hz) radiated by natural “lightning antennas” excite the Earth‐ionosphere cavity confined between the Earth's surface and the lower ionosphere. The peak frequencies of SR are known to vary with source‐observer distance (SOD), while the daily frequency range (DFR: fmax − fmin) is also indicative of the average size of thunderstorm regions. This paper provides observational evidence for these relationships based on SR frequency observations of the vertical electric (EZ) field component at Nagycenk (NCK), Hungary in Central Europe from the period 1994–2015. Variations of the peak frequencies are considered on the annual, seasonal and diurnal time scales as well as during a specific event when squall‐line formation of lightning activity in South America moves toward NCK. DFR is studied in relation to the El Niño Southern Oscillation (ENSO). Increasing area of lightning activity in mid‐high Northern hemisphere latitudes has been identified by DFR variations during the transition from warm to cold episodes of the ENSO in 1998 and 2010. The extension of the lightning area is considered as a consequence of energy released in the tropics and exported to higher latitudes with some months of delay from the end of the El Niño episodes. The frequency variations are interpreted via model calculations and supported with satellite‐based optical lightning observations (Optical Transient Detector, Geostationary Lightning Mapper). The described variations of SR peak frequencies and DFR yield information on the global/regional lightning dynamics and on this basis they have important application to climate issues as well.

Detection of Typical Transient Signals in Water by XGBoost Classifier Based on Shape Statistical Features: Application to the Call of Southern Right Whale

Whale sound is a typical transient signal. The escalating demands of ecological research and marine conservation necessitate advanced technologies for the automatic detection and classification of underwater acoustic signals. Traditional energy detection methods, which focus primarily on amplitude, often perform poorly in the non-Gaussian noise conditions typical of oceanic environments. This study introduces a classified-before-detect approach that overcomes the limitations of amplitude-focused techniques. We also address the challenges posed by deep learning models, such as high data labeling costs and extensive computational requirements. By extracting shape statistical features from audio and using the XGBoost classifier, our method not only outperforms the traditional convolutional neural network (CNN) method in accuracy but also reduces the dependence on labeled data, thus improving the detection efficiency. The integration of these features significantly enhances model performance, promoting the broader application of marine acoustic remote sensing technologies. This research contributes to the advancement of marine bioacoustic monitoring, offering a reliable, rapid, and training-efficient method suitable for practical deployment.

Pipeline leakage localization method based on transient signal detection and data fusion from multi-sensor

ABSTRACT Pipeline leaks pose significant risks to industries, the environment, and individuals, so localizing pipeline leaks is crucial for enhancing pipeline safety during operation. This paper applies a method for localizing pipeline leakages, integrating transient signal detection with multi-sensor data fusion. Addressing the challenges in detecting small leaks amidst strong noise and uncertainty, the method employs the Dempster–Shafer evidence framework for data fusion and an algorithm to analyze transient pressure waves. Comparing it with three spectrum-based methods, the performance of the fusion method is discussed in the single-leakage and multi-leakage cases. In the single-leakage case, even with high levels of noise, the fusion algorithm delivers precise localization estimates. The fusion method excels over the other three methods in the multi-leakage case. The approach significantly enhances the accuracy of leak localization in water pipelines. Extensive simulations demonstrate the method's effectiveness, particularly in noisy environments, offering a promising solution for maintaining pipeline integrity and reducing resource wastage.

Detection and localization of the highly active FRB 20240114A with MeerKAT

ABSTRACT We report observations of the highly active FRB 20240114A with MeerKAT using the ultra-high frequency (UHF; $544\!-\!1088$ MHz) and L band ($856\!-\!1712$ MHz) receivers. A total of 62 bursts were detected in coherent tied-array beams using the MeerTRAP real-time transient detection pipeline. We measure a structure-optimizing dispersion measure of $527.65\pm 0.01\, \text{pc}\, \text{cm}^{-3}$ using the brightest burst in the sample. We find the bursts of FRB 20240114A are generally detected in part of the broad-band of MeerKAT, $\sim 40~{{\ \rm per\ cent}}$ in the UHF and $\sim 30~{{\ \rm per\ cent}}$ in the L band, indicating the band limited nature. We analyse the fluence distribution of the 44 bursts detected at UHF, constraining the fluence completeness limit to $\sim 1$ Jy ms, above which the cumulative burst rate follows a power law $R (\gt F)\propto (F/1\, \text{Jy}\, \text{ms})^\gamma$ with $\gamma =-1.8\pm 0.2$. Using channelized telescope data captured in our transient buffer we localize FRB 20240114A in the image domain to RA = 21h27m39.86s, Dec. = +04d19m45.01s with an uncertainty of 1.4 arcsec. This localization allows us to confidently identify the host galaxy of FRB 20240114A. Also using the transient buffer data, we perform a polarimetric study and demonstrate that most of the bursts have $\sim 100~{{\ \rm per\ cent}}$ linear polarization fractions and up to $\sim 20~{{\ \rm per\ cent}}$ circular polarization fractions. Finally, we predict the flux density of a potential persistent radio source (PRS) associated with FRB 20240114A is $\backsimeq [0.6\!-\!60]\, \mu \text{Jy}$ based on the simple relation between the luminosity of the PRS and the rotation measure arising from the FRB local environment.

Beyond the Rotational Deathline: Radio Emission from Ultra-long Period Magnetars

ABSTRACT Motivated by the recent detection of ultralong-period radio transients, we investigate new models of coherent radio emission via low-altitude electron–positron pair production in neutron stars (NSs) beyond rotationally powered curvature radiation deathlines. We find that plastic motion (akin to ‘continental drift’) and qualitatively similar thermoelectric action by temperature gradients in the crusts of slowly rotating, highly magnetized NSs could impart mild local magnetospheric twists. Regardless of which mechanism drives twists, we find that particle acceleration initiates pair cascades across charge-starved gaps above a mild critical twist. Cascades are initiated via resonant inverse-Compton scattered photons or curvature radiation, and may produce broad-band coherent radio emission. We compute the pair luminosity (maximum allowed radio luminosity) for these two channels, and derive deathlines and ‘active zones’ in $P-\dot{P}$ space from a variety of considerations. We find these twist-initiated pair cascades only occur for magnetar-like field strengths $B \gtrsim 10^{14}$ G and long periods: $P_{\rm RICS} \gtrsim 120 \,\, (T/10^{6.5} {\rm K})^{-5} \, {\rm s}$ and $P_{\rm curv} \gtrsim 150 \,\, ({\rm v_{\rm pl}}/10^{3} {\, \rm cm \, yr^{-1}})^{-7/6} \, {\rm s}$. Using a simplified geometric model, we find that plastic motion or thermoelectrically driven twists might naturally reproduce the observed luminosities, time-scales, and timing signatures. We further derive ‘active zones’ in which rotationally powered pair creation occurs via resonantly scattered photons, beyond standard curvature deathlines for pulsars. All cascades are generically accompanied by simultaneous (non-)thermal X-ray/UV counterparts which might be detectable with current instrumentation.

Revisiting the OBS seafloor compliance signal removal with a stationarity and stacking-based approach: the BRUIT-FM toolbox

Summary This study focuses on improving the seafloor compliance noise removal method, which relies on estimates of the compliance transfer function frequency response (the deformation of the seafloor under long-period pressure waves). We first propose a new multi-scale deviation analysis of broadband ocean bottom seismometer data to evaluate stationarity properties that are key to the subsequent analysis. We then propose a new approach to removing the compliance noise from the vertical channel data, by stacking daily estimated transfer function frequency responses over a period of time. We also propose an automated transient event detection and data selection method based on a cross-correlation criterion. As an example, we apply the method to data from the Cascadia Initiative (network 7D2011). We find that the spectral extent of long-period forcing waves varies significantly over time so that standard daily transfer function calculation techniques poorly estimate the transfer function frequency response at the lowest frequencies, resulting in poor denoising performance. The proposed method more accurately removes noise at these lower frequencies, especially where coherence is low, reducing the mean deviation of the signal in our test case by 27 % or more. We also show that our calculated transfer functions can be transfered across time periods. The method should allow better estimates of seafloor compliance and help to remove compliance noise at stations with low pressure-acceleration coherence. Our results can be reproduced using the BRUIT-FM Python toolbox, available at https://gitlab.ifremer.fr/anr-bruitfm/bruit-fm-toolbox.

Unsupervised Transfer Learning Method via Cycle-Flow Adversarial Networks for Transient Fault Detection under Various Operation Conditions.

The efficient fault detection (FD) of traction control systems (TCSs) is crucial for ensuring the safe operation of high-speed trains. Transient faults (TFs) can arise due to prolonged operation and harsh environmental conditions, often being masked by background noise, particularly during dynamic operating conditions. Moreover, acquiring a sufficient number of samples across the entire scenario presents a challenging task, resulting in imbalanced data for FD. To address these limitations, an unsupervised transfer learning (TL) method via federated Cycle-Flow adversarial networks (CFANs) is proposed to effectively detect TFs under various operating conditions. Firstly, a CFAN is specifically designed for extracting latent features and reconstructing data in the source domain. Subsequently, a transfer learning framework employing federated CFANs collectively adjusts the modified knowledge resulting from domain alterations. Finally, the designed federated CFANs execute transient FD by constructing residuals in the target domain. The efficacy of the proposed methodology is demonstrated through comparative experiments.

Measurement of the Dispersion of χ(3)$\chi ^{(3)}$ of SiO2${\rm SiO}_2$ and SiN Across the THz and Far‐Infrared Frequency Bands

AbstractTerahertz (THz) radiation sources based on two‐color femtosecond plasmas in air are becoming a mature technology for coherent spectroscopy and strong‐field physics across the extended THz range to several tens of THz. The field‐resolved detection of such THz transients relies on the third‐order nonlinearity of the detection medium. Here, a comparative measurement is demonstrated with air‐biased coherent detection (ABCD) and solid‐state biased detection (SSBCD) as a novel method to measure the dispersion of the magnitude and phase of the relevant third‐order nonlinearity for fused silica () and silicon nitride (SiN). Based on the development of the ultrabroadband SSBCD device with a detection bandwidth exceeding 30 THz, measurements are obtained across the 1–28 THz frequency range, hence covering the THz and far‐infrared. It is shown that the vibrational modes in and SiN in the THz range lead to strong resonant enhancement and dispersion of the nonlinearity. The SSBCD devices operate down to nanojoule (nJ) probe energy, and their is demonstrated by measuring the dielectric function of the Lorentzian line profile of transverse‐optical (TO) phonon mode at 9 THz in single‐crystal gallium arsenide (GaAs) and observing the weak phonon combination bands near the TO phonon.

State Space Modelling for detecting and characterising gravitational waves afterglows

We propose the usage of an innovative method for selecting transients and variables. These sources are detected at different wavelengths across the electromagnetic spectrum spanning from radio waves to gamma-rays. We focus on radio signals and use State Space Models, which are also referred to as Dynamic Linear Models. State Space Models (and more generally parametric auto-regressive models) have been the mainstay of economic modelling for some years, but rarely they have been used in Astrophysics.The statistics currently used to identify radio variables and transients are not sophisticated enough to distinguish different types of variability. These methods simply report the overall modulation and significance of the variability, and the ordering of the data in time is insignificant. State Space Models are much more advanced and can encode not only the amount and significance of the variability but also properties, such as slope, rise or decline for a given time t.In this work, we evaluate the effectiveness of State Space Models for transient and variable detection including classification in time-series astronomy. We also propose a method for detecting a transient source hosted in a variable active galaxy, whereby the time-series of a static host galaxy and the dynamic nature of the transient in the galaxy are intertwined. Furthermore, we examine the hypothetical scenario where the target transient we want to detect is the gravitational wave source GW170817 (or similar).

Power Quality Transient Disturbance Diagnosis Based on Dynamic Large Convolution Kernel and Multi-Level Feature Fusion Network

Power quality is an important metric for the normal operation of a power system, and the accurate identification of transient signals is of great significance for the improvement of power quality. The diverse types of power system transient signals and strong characteristic coupling brings new challenges to the analysis and identification of power system transient signals. In order to enhance the identification accuracy of transient signals, one method of power system transient signal identification is proposed based on a dynamic large convolution kernel and multilevel feature fusion network. First, the more fine-grained and more informative features of the transient signals are extracted by the dynamic large convolution kernel feature extraction module. Then, the multi-scale local features are adaptively fused by the multilevel feature fusion module. Finally, the fused features are reduced in dimension by the fully connected layer in the classification module and fed into the SoftMax layer for transient signal type detection. The proposed method can effectively improve the small receptive field problem of convolutional neural networks and the lack of ability of Transformer network in extracting local context information. Compared with five other power quality transient disturbance identification models, the experimental results show that the proposed method has better diagnostic accuracy and anti-noise capability.

Impact of decreasing land–sea horizontal pressure gradient on the lightning activity over western India

AbstractWith future global warming projections, how lightning activity changes in the warmer world is still a debated and challenging question. During the Indian pre‐monsoon season (March–May), land surface heating and moisture availability due to prevailing winds from the neighbouring oceans provide favourable conditions for thunderstorm formation. Based on 24 years of lightning data from 2000 to 2023 detected by Lightning Imaging Sensor/Optical Transient Detector (LIS/OTD) and Indian Lightning Location Network (ILLN), the trend of lightning flashes over western India (15°–22°N, 72.5°–81°E) has been investigated. Our results demonstrate a steady decline in lightning activity during the pre‐monsoon season over western India, which contradicts the previous studies suggesting an increasing lightning trend over the Indian Subcontinent and other parts of the world. Our analysis has shown a falling trend of lightning activity at a rate of −0.066 flashes·km−2 year−1 from 2000 to 2013 (LIS/OTD) and −0.14 M flashes·year−1 from 2014 to 2023 (ILLN). Our observation and previous research strongly suggested that the pressure difference between the land and the neighbouring oceans during pre‐monsoon and monsoon has been weakening for a long time over the Indian region, and we have found a consistent reduction in wind speed over the study region. Here, we propose that the enhanced Indian Ocean warming potentially weakens the land–sea thermal contrast and, thereby, reduces the horizontal pressure gradient. Further, the decreasing trend in the land–sea horizontal pressure gradient resulted in a declining rate of wind speed over western India, affecting moisture transport over land. Thus, the study emphasizes the impact of the decreased land–sea horizontal pressure gradient on declining lighting activity in western India.

An Ultra-Wideband Dual Polarization Antenna Array for the Detection and Localization of Bright Fast Radio Transients in the Milky Way

Fast radio bursts (FRBs) are extraordinary astrophysical phenomena characterized by short radio pulses that last only a few milliseconds, yet their power can surpass that of 500 million suns. To date, most detected FRBs originate from beyond our galaxy. However, if an FRB were to originate within the Milky Way, it could be detected using small antennas. In this paper, we propose a compact and ad-hoc antenna array designed for the efficient detection and localization of FRBs within the Milky Way. The antenna operates within the 1200–1800[Formula: see text]MHz range and consists of three sub-arrays placed in an L-shape for source localization, occupying a total volume of [Formula: see text]. Each sub-array consists of 4 miniaturized, dual-polarized, half-space radiation antenna elements, forming a one-dimensional array that allows shaping the radiation pattern to match the form of the Milky Way without exhibiting grating lobes. A prototype was constructed and characterized to validate the design. The measured results exhibit good agreement with the simulations. In addition to having a custom elongated radiation pattern, the array has attractive merits, such as low reflections at the input ports, high radiation efficiency, and a distribution that inhibits the existence of phase ambiguities, thus facilitating source localization.

Transient temperature measurement of silicon carbide Schottky barrier diode based on thermal reflection.

In this article, we present a transient temperature detection device for silicon carbide (SiC) Schottky barrier diodes (SBDs) based on thermal reflection theory. We constructed a thermal reflection temperature measurement device based on a 530-nm green laser. This device is more suitable for transient temperature measurement of SiC SBDs than previous thermal reflection equipment. The accuracy of temperature measurement by our device was confirmed by comparison with the results of infrared thermal imaging. The high temporal resolution characteristics of the thermal reflection technology allowed the detection of millisecond-level transient temperature changes in SiC SBDs. In addition, we investigated the complementarity of transient temperature change curves during heating and cooling processes, as well as the reasons for the differences between these curves. Finally, we used the structural function method combined with the Bayesian deconvolution algorithm to obtain the thermal resistance along the heat flow path of the device and validated the results using an established thermal resistance testing method.