Noise Scenarios Research Articles

Model analysis and experiment study for effects of thermal viscous and fluid flow on ventilated acoustic metamaterials labyrinth

In many noise scenarios, ventilation is necessary. The practical realization of noise attenuation under the premise of ensuring ventilation is an urgent problem to be solved. In this paper, a ventilated acoustic metamaterial labyrinth (VAML) is proposed, and the corresponding analytical and numerical computational models are developed considering the thermal viscous effect (TVE). The loss in sound transmission of the VAML is quantitatively obtained and experimentally verified. The theoretical results are compared with the experimental results with an error of 0.6%. By comparing the transmission coefficient and the impedance at the entrance of the side branches, the mechanism of the TVE on the performance of VAML is analyzed, and it is clarified that the TVE is responsible for the sound absorption coefficient. The effects of structure parameters on the transmission coefficient of the VAML are analyzed individually. The results show that the resonant frequency of the system decreases as the length of channel 1 l1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l_1$$\\end{document}, the length of channel 2 l2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l_2$$\\end{document}, and the width of channel d increase. By adjusting the magnitude of these three parameters, the control of the resonant frequency can be realized. Meanwhile, as l1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l_1$$\\end{document}, l2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l_2$$\\end{document} and ventilation radius R decrease the valley of transmission coefficient decreases which means better noise reduction. Finally, the effect of flow velocity on the transmission coefficient is analyzed, revealing the mechanism of fluid action on macroscopic acoustic properties.

Figure-ground segmentation based medical image denoising using deep convolutional neural networks

Medical image noise can have a substantial impact on both diagnosis accuracy and image quality. Noise can cause problems with tasks related to diagnosis, like defining object boundaries, which are essential for precise diagnosis. By applying denoising techniques, medical imaging professionals can significantly improve image quality, reduce errors, and enhance the accuracy of diagnoses and treatments. This article presents a method for reducing noises like Gaussian noise, salt and pepper noise, speckle noise, and ring artefacts in medical images, such as magnetic resonance imaging (MRI), computed tomography (CT), and chest X-ray images. This study explores the integration of adaptive CNNs with guided image filtering for enhanced image quality and deep learning-driven Figure-Ground segmentation. The proposed method's performance is extensively evaluated on chest X-ray and MRI/CT images under diverse noise scenarios, with comparisons to existing techniques using established statistical metrics. The results validate that the proposed approach attains superior performance, yielding the highest values across these metrics.

Expected constraints on Phobos interior from the MMX gravity and rotation observations

The origin of the Martian moons is still uncertain, and knowledge about their interior could provide support to some of its leading theories. In preparation for the JAXA Martian Moons eXploration (MMX) mission, we review our current knowledge on the interior of Phobos, and provide synthetic tests showing how the gravity and rotation determination could allow the detection of specific interior-structure properties. The inversion of the geodetic observables for the retrieval of the internal mass distribution of a set of synthetic interior models is performed via non-linear least-squares, where the interior parameterization is based on the level-set method. We additionally provide simple expressions allowing to relate some of these interior models to the geodetic observables of Phobos. The results, based on realistic measurement resolution and noise scenarios, show good retrievals for most of the models at the data resolutions expected from MMX. Specifically, we find the gravity information is realistically sufficient for the detection of mass anomalies below the Stickney crater, as well as large scale heterogeneous regions within plausible rubble-pile structures. Libration helps retrieve the more degenerate models for gravity, such as those with concentric layers or with density varying linearly with depth. The incremental improvement from further adding an hypothetical mean obliquity measurement is marginal. Finally, we apply the level-set inversion and the analytical formulas to estimate possible interior characteristics of the ‘real’ Phobos from the currently-available scarce geodetic observables. The level-set solutions for the real-data inversion generally converge to a higher mass concentration towards the surface in the equatorial region. Markov chain Monte Carlo estimations of parameters relative to a simple 2-layer model or a radial density distribution similarly hint at a lighter region inside of Phobos.

Label-noise learning via uncertainty-aware neighborhood sample selection

Existing deep learning methods often require a large amount of high-quality labeled data. Yet, the presence of noisy labels in the real-world training data seriously affects the generalization ability of the model. Sample selection techniques, the current dominant approach to mitigating the effects of noisy labels on models, use the consistency of sample predictions and observed labels to make clean selections. However, these methods rely heavily on the accuracy of the sample predictions and inevitably suffer when the model predictions are unstable. To address these issues, we propose an uncertainty-aware neighborhood sample selection method. Especially, it calibrates for sample prediction by neighbor prediction and reassigns model attention to the selected samples based on sample uncertainty. By alleviating the influence of prediction bias on sample selection and avoiding the occurrence of prediction bias, our proposed method achieves excellent performance in extensive experiments. In particular, we achieved an average of 5% improvement in asymmetric noise scenarios.

Dual De-confounded Causal Intervention method for knowledge graph error detection

Due to the Knowledge Graph (KG) construction process, erroneous triples are virtually inevitable to be introduced into real-world KGs. Since these errors hinder the expressiveness and applicability of KGs, the development of knowledge graph error detection (KGED) methods is necessary. Despite the overall effectiveness of current KGED methods, their capacity to identify challenging errors is limited. In this work, we conduct empirical studies and find that previous works introduce structural and semantic bias, impeding the identification of erroneous triples, especially in challenging cases. To address this issue, we design a causal graph for the KGED task and propose a Dual De-confounded Causal Intervention (DuDCI) method for debiasing. Firstly, DuDCI utilizes the neighborhood and textual descriptions of triples to calculate their graph and text embeddings. Next, a Causal De-confounded Module is constructed to mitigate the impact of shortcuts caused by the bias through the front-door adjustment. Furthermore, we introduce Disentanglement Constraints to disentangle the information expressed by each embedding, thereby facilitating further bias mitigation. Experimental results on three widely used KGED datasets validate the effectiveness of DuDCI and demonstrate that DuDCI outperforms current KGED methods, with an improvement of at least 2.2%, especially in more challenging noise scenarios.

A physics-driven sensor placement optimization methodology for temperature field reconstruction

Perceiving the global field from sparse sensors has been a grand challenge in the monitoring, analysis, and design of physical systems. In this context, sensor placement optimization is a crucial issue. Most existing works require large and sufficient data to construct data-based criteria, which are intractable in data-free scenarios without numerical and experimental data. To this end, we propose a novel physics-driven sensor placement optimization (PSPO) method for temperature field reconstruction using a physics-based criterion to optimize sensor locations. In our methodological framework, we firstly derive the theoretical upper and lower bounds of the reconstruction error under noise scenarios by analyzing the optimal solution, proving that error bounds correlate with the condition number determined by sensor locations. Furthermore, the condition number, as the physics-based criterion, is used to optimize sensor locations by the genetic algorithm. Finally, the best sensors are validated by reconstruction models, including non-invasive end-to-end models, non-invasive reduced-order models, and physics-informed models. Experimental results, both on a numerical and an application case, demonstrate that the PSPO method significantly outperforms random and uniform selection methods, improving the reconstruction accuracy by nearly an order of magnitude. Moreover, the PSPO method can achieve comparable reconstruction accuracy to the existing data-driven placement optimization methods.

A deep learning approach to improve signal quality: spike denoising for reliable sorting using transformer networks

Accurate sorting is critical in neural signal processing. This paper presents a spike denoising method using a transformer network for enhanced spike sorting. Accurate spike sorting involves identifying and isolating signals generated by individual neurons from recordings obtained from multiple neurons. A transformer is a deep learning model that uses self-attention to differentially weight the significance of each part of the input data. Transformer networks consist of two main parts: the Encoder and the Decoder. The Encoder is made up of encoding layers that iteratively map the input to a new space, while the Decoder contains decoding layers that generate a denoised signal from the output of the Encoder. The aim of this paper is to present a supervised sorting model that could isolate the raw spike waveforms to their fired neurons with higher accuracies, leveraging the attention mechanism in the proposed model. The developed approach utilizes the transformer network to increase the accuracy of detecting the main source of spikes. Additionally, this method extracts the main spike source in cases of overlapped spikes. To assess our model’s performance, we tested it on two public datasets. The first dataset was designed to evaluate the model’s ability to handle noise, while the second dataset presented challenges for spike classification, making it a suitable test for our model’s capabilities. Our model outperformed other existing models on both datasets. Our model achieved an average classification accuracy of 99.85% on the simulated dataset and 95.06% on the experimental dataset, outperforming other models. These results suggest that our model is well-suited for handling noise and difficult spike classification scenarios and is superior to other currently available models.

A Multi-Mechanism Fusion Method for Robust State of Charge Estimation via Bidirectional Long Short-Term Memory Model with Mixture Kernel Mean p-Power Error Loss Optimized by Golden Jackal Optimization Algorithm

Abstract Accurate state of charge (SOC) estimation is crucial for effective battery management in various applications. The bidirectional long short-term memory (BiLSTM) as an outstanding nonlinear regression model can be used for SOC estimation. This work develops a novel multi-mechanism fusion method based on BiLSTM to further enhance its estimation performance for SOC, in which the convolutional neural network (CNN), attention mechanism, and mixture kernel mean p-power error (MKMPE) loss are introduced into the BiLSTM framework for addressing different issues. First, the introduction of CNN components aims to extract essential features from battery data, enhancing the model's comprehension of complex information. Then, the attention mechanism is used to further refine the model's perceptual ability and a robust MKMPE loss is introduced into the BiLSTM framework to replace its original mean squared error loss, and a novel robust model is developed to suppress non-Gaussian noise interference. Finally, some key hyperparameters of the proposed model are fine-tuned using the golden jackal optimization algorithm, resulting in improved estimation performance. Comparative numerical experiments are meticulously conducted in various cases to evaluate the performance of the proposed method, and the experiment results demonstrate that it can perform outstanding effectiveness in handling non-Gaussian noise scenarios.

A measurement modified centered error entropy cubature Kalman filter for integrated INS/GNSS

Due to unpredictable environmental factors, the measurement noise in INS/GNSS integration can be affected by outliers or exhibit statistical uncertainty. A single adaptive or robust filter may not be suitable for all noise scenarios. To address this issue, a new approach called the measurement noise covariance matrix (MNCM) ’R’ modified centered error entropy cubature Kalman filter (RMCEECKF) is proposed in this study. In this method, the MNCM is adjusted based on the innovation sequence, and outliers are detected using the Mahalanobis distance. If outliers are identified, the CEE criterion with strong robustness is applied for the posterior update. Simulation results on INS/GNSS integration demonstrate that the RMCEECKF offers higher estimation accuracy compared to existing methods in scenarios involving outliers, uncertain noise covariance, and outliers under uncertain noise covariance. The inclusion of outlier detection also enhances the computational efficiency of RMCEECKF when compared to the CEECKF.

CoFix: Advancing Semi-Supervised Learning with Noisy Label Mitigation Through Sample Selection and Consistent Regularization

Deep Neural Networks (DNNs) have revolutionized various fields through their ability to model complex patterns, yet their performance critically hinges on the availability of large-scale, accurately labeled datasets. The costly and time-consuming process of creating such datasets has motivated research into Learning with Noisy Labels (LNL), which aims to reduce reliance on perfect labels. This paper introduces CoFix, an innovative LNL framework that integrates sample selection with semi-supervised learning techniques to address the challenge of noisy labels in training. CoFix employs a Gaussian Mixture Model (GMM) to dynamically segment the training data into clean and noisy subsets, leveraging semi-supervised learning approaches on both. Inspired by the FixMatch algorithm, CoFix refines consistent regularization and pseudo-labeling strategies, enhancing augmentation strategies and temperature sharpening techniques. Additionally, CoFix explores label smoothing to augment the loss function, further refining model performance. Our experiments demonstrate CoFix’s superiority over state-of-the-art methods, achieving significant improvements in fewer training epochs, particularly in lower noise scenarios. The robustness and versatility of CoFix are evident through its consistent performance across various benchmark datasets and noise levels. The contributions of this paper include a novel LNL method with enhanced generalization capability, an investigation into the impact of label smoothing on the loss function, and extensive testing that confirms CoFix’s efficiency and adaptability to different noise levels.

A novel lithium-ion battery state-of-health estimation method for fast-charging scenarios based on an improved multi-feature extraction and bagging temporal attention network

Accurately estimating the state of health (SOH) of fast-charging lithium-ion batteries is crucial for safely and reliably operating battery systems. However, handling data scarcity and rapid charging scenarios under diverse operational conditions is challenging. In this paper, a novel approach for estimating the SOH of lithium-ion batteries (LIBs) is introduced based on an improved multisegment feature extraction and bagging temporal attention network. First, four health features, including the time differences observed at equal voltages, the cumulative integral of voltage changes, the total circuit charge variation and the levels of slope peaks, are identified. Second, a residual bidirectional long short-term memory (Bi-LSTM) attention mechanism is designed to focus on the temporal dimension of battery data by incorporating a multilayered complex neural network design comprising convolutional layers, pooling layers, Bi-LSTM layers and fully connected layers. This design effectively captures the features and relationships contained in battery data. The predictions derived from randomly initialized parameters and multiple submodels are stacked to improve the generalizability of the model. Finally, comprehensive evaluations are conducted through comparative experiments, ablation studies and noise experiments, with six evaluation metrics considered across three datasets. The MAE, RMSE, MAEP and MAXE of the proposed model reach 0.366, 0.605, 0.519 and 1.56, respectively. The results indicate that the proposed method enhances the robustness, resilience and generalizability of the estimates produced under different conditions and noise scenarios.

Advanced seismic control strategies for smart base isolation buildings utilizing active tendon and MR dampers

This paper investigates the seismic behaviour of a five-storey shear building that incorporates a base isolation system. Initially, the study considers passive base isolation and employs a multi-objective archived-based whale optimization algorithm called MAWOA to optimize the parameters of base isolation. Subsequently, a novel model is proposed, which incorporates an interval type-2 Takagi-Sugeno fuzzy logic controller (IT2TSFLC) utilizing clustering techniques. The building includes Magneto-rheological (MR) dampers installed at the base isolation level and two active tendons positioned on the first and second storeys of the structure. The semi-active control force of the base isolation with MR dampers is determined by the fuzzy system, while the active control force for the active tendons is computed using the linear quadratic regulator (LQR) algorithm, enabling control force provision during seismic events. The primary objective of this model is to enhance the seismic control of the building. Therefore, it is classified as a proposed model. The structural system is subjected to seismic analyses, considering three different structural configurations: uncontrolled, equipped with passive base isolation, and equipped with semi-active base isolation combining MR dampers and active tendons. The findings of the research demonstrate that by considering the optimization of parameters of the passive base isolation based on the white noise scenario and using these parameters as design parameters, during seismic analysis of the structure in some earthquakes, increased structural responses were observed when compared to uncontrolled structure, highlighting a potential risk. Nevertheless, the proposed system effectively addresses this drawback of passive control systems by markedly reducing structural responses, as compared to both passive base isolation and uncontrolled structure. These results suggest that the proposed system is an effective solution for mitigating seismic risks in structural seismic control.

Semi-supervised adaptive anti-noise meta-learning for few-shot industrial gearbox fault diagnosis

Abstract Real-time and accurate predictive maintenance of industrial equipment is fundamental for ensuring the safety and stability of advanced manufacturing processes. Current fault diagnosis methods based on data mining rely on a large number of labeled samples, and obtaining sufficient labeled data for diagnosing industrial equipment faults is challenging. Meta-learning can achieve the diagnosis of few-shot samples to a certain extent, but the effect is not ideal. Semi-supervision can effectively leverage a large number of unlabeled samples, which is of great practical significance for handling scenarios involving limited labeled samples. However, noise interference can occur when unlabeled samples appear that do not belong to known categories. Therefore, this study proposes adaptive semi-supervised meta-learning networks for noisy few-shot gearbox fault diagnosis. Firstly, a residual network with a Morlet Wavelet layer is used to extract signal features. Next, sample-level attention is defined to select unlabeled samples that are more similar to labeled sample prototypes, thereby reducing the influence of noisy samples. The adaptive metric is used to obtain the relational distance functions of labeled samples and unlabeled samples. Adaptive semi-supervised meta-learning networks uses unlabeled data to refine prototypes for better fault diagnosis. The effectiveness and anti-noise performance of the proposed method are verified by using two gearbox datasets with various few-shot noise scenarios.

A unified sensor and actuator fault diagnosis in digital twins for remote operations

This paper explores the development of a unified hybrid approach for sensor and actuator fault diagnosis in digital twins for remote operations. Central to this approach is the implementation of a robust adaptive Kalman filter algorithm, which forms the backbone of the proposed unified algorithm. The essence of this unified algorithm lies in its capability to effectively filter the sensor measurements. The algorithm is enriched with tuning parameters, offering flexibility in adjusting the convergence rate to suit operational requirements. Noteworthy for its robustness, our approach excels in handling uncertainties and diverse types of faults, including drift, bias, noise, and freeze fault scenarios. Through comprehensive simulation and experimental evaluations conducted on a small surface vessel, the method demonstrates remarkable proficiency in accurately identifying sensor and actuator faults. This precision enables early detection and prompt mitigation of anomalies, contributing to heightened operational resilience.

Enhanced nonlinear active noise control: A novel approach using brain storm optimization algorithm

Active Noise Control (ANC) systems play a crucial role in reducing unwanted noise in various settings. Traditional ANC methods, like the Filtered-x Least Mean Squares (FxLMS) algorithm, are effective in linear noise scenarios. However, they often struggle with more nonlinear and complex noise patterns. This paper introduces a novel approach using the brain storm optimization (BSO) algorithm in nonlinear ANC systems, which represents a significant departure from conventional techniques. The BSO algorithm, inspired by human brainstorming processes, excels in addressing the complexities of nonlinear noise by incorporating principles, such as delayed evaluation, free imagination, quantity and quality, and comprehensive improvement. By combining the BSO algorithm with an Extended Kalman Filter (EKF), a new ANC system is proposed that can adapt to a wide range of noise types with improved speed and accuracy. Experimental results showcase the superior performance of the BSO algorithm, achieving an impressive noise reduction of up to 48 dB (dB) in a 500Hz sinusoidal noise scenario, with a convergence time as fast as 0.01 s, outperforming the FxLMS algorithm by a significant margin. Moreover, in complex environments with multi-frequency and random noise, the BSO algorithm consistently demonstrates better noise reduction and quicker convergence, reducing noise levels by up to 27 dB within 0.001 s. The innovative use of the BSO algorithm in ANC systems not only enhances noise reduction capabilities, especially for nonlinear and complex noise signals, but also improves convergence times, paving the way for future advancements in ANC technologies.

CRLNet: A Multimodal Peach Detection Network Based on Cooperative Asymptotic Enhancement and the Fusion of Granularity Refinement.

Accurate peach detection is essential for automated agronomic management, such as mechanical peach harvesting. However, ubiquitous occlusion makes identifying peaches from complex backgrounds extremely challenging. In addition, it is difficult to capture fine-grained peach features from a single RGB image, which can suffer from light and noise in scenarios with dense small target clusters and extreme light. To solve these problems, this study proposes a multimodal detector, called CRLNet, based on RGB and depth images. First, YOLOv9 was extended to design a backbone network that can extract RGB and depth features in parallel from an image. Second, to address the problem of information fusion bias, the Rough-Fine Hybrid Attention Fusion Module (RFAM) was designed to combine the advantageous information of different modes while suppressing the hollow noise at the edge of the peach. Finally, a Transformer-based Local-Global Joint Enhancement Module (LGEM) was developed to jointly enhance the local and global features of peaches using information from different modalities in order to enhance the percentage of information about the target peaches and remove the interference of redundant background information. CRLNet was trained on the Peach dataset and evaluated against other state-of-the-art methods; the model achieved an mAP50 of 97.1%. In addition, CRLNet also achieved an mAP50 of 92.4% in generalized experiments, validating its strong generalization capability. These results provide valuable insights for peach and other outdoor fruit multimodal detection.

Interpretable wind power forecasting combining seasonal-trend representations learning with temporal fusion transformers architecture

Wind power forecasting plays a critical role in the efficient integration of wind energy into power systems. Accurate short-term WPF helps maintain grid stability, optimize power dispatch, and reduce operational costs. However, current deep learning-based WPF architectures rely on mapping spatial–temporal features into a single high-dimensional representation, compromising performance and interpretability. In this work, the interpretable seasonal-trend representation algorithm (ISTR) is proposed to learn a couple of disentangled latent representations that describe seasonal-trend of wind power time series. The ISTR is developed in a self-supervised manner with no need for prior knowledge, providing sufficient interpretable information while avoiding the feature entanglement problem. The ISTR is then integrated into the temporal fusion transformer (TFT) model through seasonal time-point embedding to perform accurate multi-horizon WPF with interpretable meanings. In the case study, ISTR-TFT outperforms the current state-of-the-art methods by achieving an 8.9% improvement in mean absolute percentage error (MAPE) compared to DeepAR and a 15.7% improvement in pinball score. The proposed ISTR-TFT also shows enhanced robustness to noise and missing data scenarios. The interpretability results generated by ISTR-TFT reveal the importance of various input features and the learned temporal patterns, offering valuable insights for decision-making in power system operations.

Robust Tensor Completion via Capped Frobenius Norm.

Tensor completion (TC) refers to restoring the missing entries in a given tensor by making use of the low-rank structure. Most existing algorithms have excellent performance in Gaussian noise or impulsive noise scenarios. Generally speaking, the Frobenius-norm-based methods achieve excellent performance in additive Gaussian noise, while their recovery severely degrades in impulsive noise. Although the algorithms using the lp -norm ( ) or its variants can attain high restoration accuracy in the presence of gross errors, they are inferior to the Frobenius-norm-based methods when the noise is Gaussian-distributed. Therefore, an approach that is able to perform well in both Gaussian noise and impulsive noise is desired. In this work, we use a capped Frobenius norm to restrain outliers, which corresponds to a form of the truncated least-squares loss function. The upper bound of our capped Frobenius norm is automatically updated using normalized median absolute deviation during iterations. Therefore, it achieves better performance than the lp -norm with outlier-contaminated observations and attains comparable accuracy to the Frobenius norm without tuning parameter in Gaussian noise. We then adopt the half-quadratic theory to convert the nonconvex problem into a tractable multivariable problem, that is, convex optimization with respect to (w.r.t.) each individual variable. To address the resultant task, we exploit the proximal block coordinate descent (PBCD) method and then establish the convergence of the suggested algorithm. Specifically, the objective function value is guaranteed to be convergent while the variable sequence has a subsequence converging to a critical point. Experimental results based on real-world images and videos exhibit the superiority of the devised approach over several state-of-the-art algorithms in terms of recovery performance. MATLAB code is available at https://github.com/Li-X-P/Code-of-Robust-Tensor-Completion.

Speech Signal Enhancement with Integrated Weighted Filtering for PSNR Reduction in Multimedia Applications

This paper investigates the effectiveness of the Weighted Kalman Integrated Band Rejection (WKBR) method for enhancing speech signals in multimedia applications. Speech enhancement is crucial for improving the quality and intelligibility of audio in environments with varying noise types and levels. The WKBR method is evaluated across ten different noise scenarios, including white noise, babble noise, street noise, airplane cabin noise, and more. Performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Short-Time Objective Intelligibility (STOI) are used to quantify the enhancement. The results show significant improvements, with PSNR increasing from an average of 12.8 dB before enhancement to 21.9 dB after enhancement, MSE reducing from an average of 0.0179 to 0.0053, and STOI scores improving from an average of 0.58 to 0.75. These findings highlight the potential of WKBR as a powerful tool for speech signal enhancement, making it a promising solution for real-world multimedia applications where clear and intelligible speech is essential.

White dwarf constraints on geological processes at the population level

ABSTRACT White dwarf atmospheres are frequently polluted by material from their own planetary systems. Absorption features from Ca, Mg, Fe, and other elements can provide unique insights into the provenance of this exoplanetary material, with their relative abundances being used to infer accretion of material with core- or mantle-like composition. Across the population of white dwarfs, the distribution of compositions reveals the prevalence of geological and collisional processing across exoplanetary systems. By predicting the distribution of compositions in three evolutionary scenarios, this work assesses whether they can explain current observations. We consider evolution in an asteroid belt analogue, in which collisions between planetary bodies that formed an iron core lead to core- or mantle-rich fragments. We also consider layer-by-layer accretion of individual bodies, such that the apparent composition of atmospheric pollution changes during the accretion of a single body. Finally, we consider that compositional spread is due to random noise. We find that the distribution of Ca, Fe, and Mg in a sample of 202 cool DZs is consistent with the random noise scenario, although 7 individual systems show strong evidence of core-mantle differentiation from additional elements and/or low noise levels. Future surveys that detect multiple elements in each of a few hundred white dwarfs, with well-understood biases, have the potential to confidently distinguish between the three models.