Source Detection Research Articles

Characterizing gravitational wave detector networks: from A♯ to cosmic explorer

Abstract Gravitational-wave observations by the Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo have provided us a new tool to explore the Universe on all scales from nuclear physics to the cosmos and have the massive potential to further impact fundamental physics, astrophysics, and cosmology for decades to come. In this paper we have studied the science capabilities of a network of LIGO detectors when they reach their best possible sensitivity, called A#, given the infrastructure in which they exist and a new generation of observatories that are factor of 10 to 100 times more sensitive (depending on the frequency), in particular a pair of L-shaped Cosmic Explorer observatories (one 40 km and one 20 km arm length) in the US and the triangular Einstein Telescope with 10 km arms in Europe. We use science metrics derived from the top priorities of several funding agencies to characterize the science capabilities of different networks. The presence of one or two A# observatories in a network containing two or one next generation observatories, respectively, will provide good localization capabilities for facilitating multimessenger astronomy and precision measurement of the Hubble parameter. Two Cosmic Explorer observatories are indispensable for achieving precise localization of binary neutron stars, facilitating detection of electromagnetic counterparts and transforming multimessenger astronomy. Their combined operation is even more important in the detection and localization of high-redshift sources, such as binary neutron stars, beyond the star-formation peak, and primordial black hole mergers, which may occur roughly 100 million years after the Big Bang. The addition of the Einstein Telescope to a network of two Cosmic Explorer observatories is critical for accomplishing all the identified science metrics. For most metrics the triple network of next generation terrestrial observatories are a factor 100 better than what can be accomplished by a network of three A# observatories.

CoaddNet: Enhancing signal-to-noise ratio in single-shot images using convolutional neural networks with coadded image effect

Noise in astronomical images significantly impacts observations and analyses. Traditional denoising methods, such as increasing exposure time and image stacking, are limited when dealing with single-shot images or studying rapidly changing astronomical objects. To address this, we developed a novel deep-learning denoising model, CoaddNet, designed to improve the image quality of single-shot images and enhance the detection of faint sources. To train and validate the model, we constructed a dataset containing high and low signal-to-noise ratio (SNR) images, comprising coadded and single-shot types. CoaddNet combines the efficiency of convolutional operations with the advantages of the Transformer architecture, enhancing spatial feature extraction through a multi-branch structure and reparameterization techniques. Performance evaluation shows that CoaddNet surpasses the baseline model, NAFNet, by increasing the Peak Signal-to-Noise Ratio (PSNR) by 0.03 dB and the Structural Similarity Index (SSIM) by 0.005 while also improving throughput by 35.18%. The model significantly improves the SNR of single-shot images, with an average increase of 22.8, surpassing the noise reduction achieved by stacking 70-90 images. By boosting the SNR, CoaddNet significantly enhances the detection of faint sources, enabling SExtractor to detect an additional 22.88% of faint sources. Meanwhile, CoaddNet reduced the Mean Absolute Percentage Error (MAPE) of flux measurements for detected sources by at least 27.74%.

Holographic method of under-water noise source localization in shallow water

The results of a high-frequency experiment for the detection and direction estimation of underwater sound noisy source are presented. The experiment was conducted in the shallow waters of the Black Sea coast. The noise emission of the source was received by three vector-scalar receivers located on the bottom. By using holographic processing, the detection and direction estimation of a moving underwater source against the background of intense shipping in the experiment region were carried out. Estimates of the input signal-to-noise ratio are presented.

YOLOGAS: An Intelligent Gas Leakage Source Detection Method Based on Optical Gas Imaging

YOLOGAS: An Intelligent Gas Leakage Source Detection Method Based on Optical Gas Imaging

Transfer learning for high-dimensional linear regression via the elastic net

In this paper, the high-dimensional linear regression problem is explored via the Elastic Net under the transfer learning framework. Within this framework, potentially related source datasets are leveraged to enhance estimation or prediction beyond what can be achieved solely with the target data. When transferable sources are known, an oracle transfer learning algorithm is proposed based on the Elastic Net. Additionally, the ℓ1/ℓ2 estimation error bounds for the corresponding estimator are established. When the transferable sources are unknown, a novel procedure for detecting transferable sources via the Elastic Net is also proposed, with its selection consistency demonstrated under regular conditions. This method transforms the source detection problem into a variable selection problem in high-dimensional space and always gets results that are consistent with the true outcomes. The performance of these methods is further demonstrated through a variety of numerical examples. Finally, our approach is applied to analyze several real datasets for illustrative purposes.

Distributed Rumor Source Detection via Boosted Federated Learning

Distributed Rumor Source Detection via Boosted Federated Learning

Malicious Source Detection and Threats Mitigation in Named Data Networking Using Deep Learning

Malicious Source Detection and Threats Mitigation in Named Data Networking Using Deep Learning

Source detection in social networks under independent cascade model

Source detection in social networks under independent cascade model

SiamVIT: A patchwise network for gamma-ray point source detection

Conventional point source detection methods generally work in a pixelwise manner and can hardly exploit the overall semantic information of sources; consequently, these methods usually suffer from low precision. In this work we achieve point source detection in fully patchwise mode by proposing a siamese network called SiamVIT that includes a visual transformer (VIT). SiamVIT can effectively and accurately locate point sources from gamma -ray maps with high purity not only in higher flux regions, but also in lower flux regions, which is extremely challenging to achieve with state-of-the-art methods. SiamVIT consists of two VIT branches and a matching block. In the feature extraction stage, gamma -ray maps are fed into one VIT branch to obtain patch representations with adequate semantic and contextual information, whereas detection templates with location information are fed into the other branch to produce template representations. In the location stage, a patch representation and all template representations are fed into the matching block to determine whether the associated gamma -ray map patch contains a point source and where that point source is located, if applicable. We compare our proposed SiamVIT with the current advanced methods and find that SiamVIT has significantly better purity and completeness and a superior Dice coefficient on the test set. In addition, when point sources overlap, SiamVIT can better distinguish different point sources.

A real scale application of a novel set of spatial and similarity features for detection and classification of natural seismic sources from Distributed Acoustic Sensing data

Summary Distributed Acoustic Sensing (DAS) turns a fiber optic into a very dense network of equally-distributed seismic sensors. We focused on the high-density sampling of the seismic wavefield, expressed in strain rates, measured by DAS. Classical approaches used to identify seismic signals rely on the recorded features at one station, but it is difficult to include spatial information in case of dense seismic station networks. This work aims at introducing new spatial and similarity features for seismic event classification suitable to analyze DAS observations. We propose a processing chain based on the XGBoost algorithm and the use of specifically designed spatio-temporal and similarity features for the event classification, and Markov Random Field for the spatial clustering. The methodology is designated to be applied on a continuous stream of DAS observations. We tested our processing chain to detect earthquakes and quarry blasts recorded in the region by permanent seismic networks and included in the RENASS catalog. These events are part of a strain-rate seismic survey carried out during a 3 weeks campaign of DAS measurements along à 91 km fiber optic cable deployed in the central Pyrenees mountains (France). Despite the high anthropogenic activities along the fiber optic path, the proposed method succeeded in detecting earthquakes of magnitude >0.4 and quarry blasts of magnitude >1.0 while limiting the number of false alarms. This performance is particularly noteworthy for low-magnitude events, where detection is accomplished despite a lower signal-to-noise ratio compared to traditional seismometers. The methodology opens the door to real time detection and classification of seismic events measured with long-distance fiber optic systems.

Rapid wavelength interval selection technology for multi-channel intensity-interrogation surface plasmon resonance imaging sensing

Intensity-interrogation surface plasmon resonance (I-SPR) provides the fastest imaging speed among various SPR techniques, including wavelength, phase, and angle-based detection. It also integrates easily with other devices and has substantial practical application potential. However, I-SPR's reliance on direct detection of reflected light intensity renders it sensitive to light source fluctuations and detector dark noise, and its dynamic range is relatively limited. To address these challenges, we have developed a multi-channel I-SPR sensing platform featuring rapid wavelength interval selection technology. This system optimizes the sensing wavelength according to the refractive indices of different samples, mitigating issues related to inconsistent SPR signals from various biomolecules. Our improved I-SPR technology enables high-throughput biomolecular detection with a refractive index range size of 2.035 × 10−2 for dynamic monitoring, reaching a leading role among the existing I-SPR technologies. This represents the widest linear dynamic range for I-SPR to date. Experimental results on protein binding kinetic constants KD are consistent with those obtained from commercially available instruments. We believe that this study is expected to accelerate the development of SPR technology toward broader biological applications.

Holographic Method of Localization of Underwater Noise Source in Shallow Water

Holographic signal processing of noise signals by using of the vector-scalar receivers is described. The results of an experiment on the detection and direction estimation of an underwater noise source are presented. The experiment was conducted in Black Sea. The source noise emission was received by vector-scalar receivers. Holographic signal processing made it possible to detect, localize and resolve an underwater source with a low input signal/interference ratio against the background of intensive surface navigation.

Instantaneous detection and location of contaminant sources in intelligent buildings: A distributed Kalman filter scheme

Indoor Air Quality (IAQ), as a common concern of Intelligent Buildings (IBs), is easily compromised by contaminants which may result from accidents, pathogens or terrorist attacks. Once some contaminant occurs in one zone, it will be quickly diffused to other zones through windows, doors or Heating, Ventilation and Air Conditioning (HVAC) systems, which makes detecting and locating the contaminant source a significant challenge. In this paper, a distributed Kalman filter (DKF) based scheme for detecting and locating the multiple contaminant sources is developed. Firstly, a distributed state-space model of contaminant diffusion is established with the help of the multizone simulation program-CONTAM and the Multi-Chamber theory. Then, a multi-agent based distributed monitoring framework is constructed, where each agent equipped for each zone runs a Kalman filter using both the information of the local zone and the residuals of the interconnected zones. The decoupling strategy is employed to determine the filter gains to guarantee the optimal contaminant concentration estimations. Compared with existing methods, the proposed scheme can detect and locate multiple contaminant sources instantaneously. Meanwhile, it has the advantages of low computation and communication consumptions. A model of a realistic 11-zone experimental center created on CONTAM is used to demonstrate the effectiveness and superiority of the proposed scheme.

Usefulness of multigene liquid biopsy of bile for identifying driver genes of biliary duct cancers.

Liquid biopsy (LB) is an essential tool for obtaining tumor-derived materials with minimum invasion. Bile has been shown to contain much higher free nucleic acid levels than blood plasma and can be collected through endoscopic procedures. Therefore, bile possesses high potential as a source of tumor derived cell-free DNA (cfDNA) for bile duct cancers. In this study, we show that a multigene panel for plasma LB can also be applied to bile cfDNA for comparing driver gene mutation detection in other sources (plasma and tumor tissues of the corresponding patients). We collected cfDNA samples from the bile of 24 biliary tract cancer cases. These included 17 cholangiocarcinomas, three ampullary carcinoma, two pancreatic cancers, one intraductal papillary mucinous carcinoma, and one insulinoma. Seventeen plasma samples were obtained from the corresponding patients before surgical resection and subjected to the LiquidPlex multigene panel LB system. We applied a machine learning approach to classify possible tumor-derived variants among the prefiltered variant calls by a LiquidPlex analytical package with high fidelity. Among the 17 cholangiocarcinomas, we could detect cancer driver mutations in the bile of 10 cases using the LiquidPlex system. Of the biliary tract cancer cases examined with this method, 13 (54%) and 4 (17%) resulted in positive cancer driver mutation detection in the bile and plasma cfDNAs, respectively. These results suggest that bile is a more reliable source for LB than plasma for multigene panel analyses of biliary tract cancers.

Cardiac CT vs Echocardiography for Intracardiac Thrombus Detection in Ischemic Stroke: A Systematic Review and Meta-Analysis of 43 Studies.

Ischemic stroke, a leading cause of mortality, necessitates understanding its mechanism for effective prevention. Echocardiography, especially transesophageal echocardiography (TEE), is the gold standard for detection of cardiac sources of stroke including left atrial thrombus, although its invasiveness, operator skill dependence, and limited availability in some centers prompt exploration of alternatives, such as cardiac CT (CCT). We conducted a systematic review and meta-analysis assessing the ability of CCT in the detection of intracardiac thrombus compared with echocardiography. We searched 4 databases up through September 8, 2023. Major search terms included a combination of the terms "echocardiograph," "CT," "TEE," "imaging," "stroke," "undetermined," and "cryptogenic." The current systematic literature review of the English language literature was reported in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. We assessed risk of bias using the QUADAS-2 tool and used random-effects meta-analysis to calculate different diagnostic metrics. The meta-analysis investigating CCT vs echocardiography for intracardiac thrombus detection yielded a total of 43 studies of 9,552 patients. Risk-of-bias assessment revealed a predominantly low risk of bias in the flow and timing, index test, and patient selection domains and a predominantly unclear risk of bias in the reference standard domain. The analysis revealed an overall sensitivity of 98.38% (95% CI 89.2-99.78) and specificity of 96.0% (95% CI 92.55-97.88). Subgroup analyses demonstrated that delayed-phase, electrocardiogram-gated CCT had the highest sensitivity (100%; 95% CI 0-100) while early-phase, nongated CCT exhibited a sensitivity of 94.31% (95% CI 28.58-99.85). The diagnostic odds ratio was 98.59 (95% CI 44.05-220.69). Heterogeneity was observed, particularly in specificity and diagnostic odds ratio estimates. CCT demonstrates high sensitivity, specificity, and diagnostic odds ratios in detecting intracardiac thrombus compared with traditional echocardiography. Limitations include the lack of randomized controlled studies, and other cardioembolic sources of stroke such as valvular disease, cardiac function, and aortic arch disease were not examined in our analysis. Large-scale studies are warranted to further evaluate CCT as a promising alternative for identifying intracardiac thrombus and other sources of cardioembolic stroke.

Central line-associated bloodstream infection outbreak related to Ralstonia pickettii-contaminated saline in a pediatric hematopoietic stem cell transplant center.

Ralstonia pickettii is an aerobic Gram-negative non-fermentative bacillus. It is an opportunistic pathogen that has recently prompted nosocomial outbreaks. Although it has low virulence, it can cause a wide range of invasive diseases in immunosuppressive patients. The characteristics of R. pickettii-related central line-associated bloodstream infection (CLABSI) outbreak in pediatric hematopoietic stem cell transplant (HSCT) recipients are presented in this study. This was a single-center, retrospective analysis conducted at Bahcesehir University Goztepe Medicalpark Hospital . The clinical and laboratory characteristics of twelve children with Ralstonia-related CLABSIs were analyzed. Of the twelve patients with R. pickettii growth, seven were female. The median age was 12.1 (2-17) years. Autologous HSCT was performed in two of the patients and allogeneic HSCT was performed in ten patients for both malignant and non-malignant diseases. In the conditioning regimens, all patients were given myeloablative therapy. Clinical sepsis was the most common presentation. As a result of the investigations, R. pickettii growth was observed in saline solutions. All cases were successfully treated with the appropriate antibiotic regimen and the bacteria was not found in repeat cultures. Catheter removal was required in two patients. Mortality was not observed in any patient as the outcome of the infection episode. The detection and control of the infectious source are critical in pediatric HSCT patients with severe immunosuppression, as medical equipment-related outbreaks can be life-threatening.

Acoustic camera applied on sound transmission identification in closed spaces

In the ongoing context of office partitioning, users are increasingly paying attention to acoustic insulation between rooms. The issues commonly related are translated by confidentiality problems and annoying use of sound-enabled videoconferencing. In practice, when an insulation defect is perceived, standardized acoustic insulation measurements are carried out to characterize the overall insulation performance. However, it is often challenging to precisely identify by ear the various paths of sound transmission: through the partition, by poorly caulked junctions, through the ceiling, through the facade, etc. Acoustic antenna solutions employed to identify the location and frequency content of sources have existed for several decades. But, in addition of being expensive and bulky, until now their use was reserved for detection of sources on transportation vehicles (trains, cars, planes, etc.) or industrial equipment (wind turbines, boiler rooms, etc.). In recent years, more accessible and practical antenna solutions known as "acoustic camera" have become available. In this sense, we have been handling an acoustic camera to assist on the detection of the sound transmission paths between two rooms. This work address what has been experienced, outlining results obtained through the (evolving) methodology for sound transmission identification with acoustic camera in closed spaces.

Up-scattering production of a sterile fermion at DUNE: complementarity with spallation source and direct detection experiments

We investigate the possible production of a MeV-scale sterile fermion through the up-scattering of neutrinos on nuclei and atomic electrons at different facilities. We consider a phenomenological model that adds a new fermion to the particle content of the Standard Model and we allow for all possible Lorentz-invariant non-derivative interactions (scalar, pseudoscalar, vector, axial-vector and tensor) of neutrinos with electrons and first-generation quarks. We first explore the sensitivity of the DUNE experiment to this scenario, by simulating elastic neutrino-electron scattering events in the near detector. We consider both options of a standard and a tau-optimized neutrino beams, and investigate the impact of a mobile detector that can be moved off-axis with respect to the beam. Next, we infer constraints on the typical coupling, new fermion and mediator masses from elastic neutrino-electron scattering events induced by solar neutrinos in two current dark matter direct detection experiments, XENONnT and LZ. Under the assumption that the new mediators couple also to first-generation quarks, we further set constraints on the up-scattering production of the sterile fermion using coherent elastic neutrino-nucleus scattering data from the COHERENT experiment. Moreover, we set additional constraints assuming that the sterile fermion may decay within the detector. We finally compare our results and discuss how these facilities are sensitive to different regions of the relevant parameter space due to kinematics arguments and can hence provide complementary information on the up-scattering production of a sterile fermion.

Self-supervised learning on MeerKAT wide-field continuum images

Context. Self-supervised learning (SSL) applied to natural images has demonstrated a remarkable ability to learn meaningful, low-dimension representations without labels, resulting in models that are adaptable to many different tasks. Until now, applications of SSL to astronomical images have been limited to Galaxy Zoo datasets, which require a significant amount of preprocessing to prepare sparse images centered on a single galaxy. With wide-field survey instruments at the forefront of the Square Kilometer Array (SKA) era, this approach to gathering training data is impractical. Aims. We demonstrate that continuum images from surveys such as the MeerKAT Galactic Cluster Legacy Survey (MGCLS) can be successfully used with SSL, without extracting single-galaxy cutouts. Methods. Using the SSL framework DINO, we experimented with various preprocessing steps, augmentations, and architectures to determine the optimal approach for this data. We trained both ResNet50 and Vision Transformer (ViT) backbones. Results. Our models match state-of-the-art results (trained on Radio Galaxy Zoo) for FRI/FRII morphology classification. Furthermore, they predict the number of compact sources via linear regression with much higher accuracy. Open-source foundation models trained on natural images such as DINOv2 also excel at simple FRI/FRII classification; the advantage of domain-specific backbones is much smaller models trained on far less data. Smaller models are more efficient to fine-tune, and doing so results in a similar performance between our models, the state-of-the-art, and open-source models on multi-class morphology classification. Conclusions. Using source-rich crops from wide-field images to train multi-purpose models is an easily scalable approach that significantly reduces data preparation time. For the tasks evaluated in this work, twenty thousand crops is sufficient training data for models that produce results similar to state-of-the-art. In the future, complex tasks like source detection and characterization, together with domain-specific tasks, ought to demonstrate the true advantages of training models with radio astronomy data over natural-image foundation models.

YOLO-CIANNA: Galaxy detection with deep learning in radio data

Context. The upcoming Square Kilometer Array (SKA) will set a new standard regarding data volume generated by an astronomical instrument, which is likely to challenge widely adopted data-analysis tools that scale inadequately with the data size. Aims. The aim of this study is to develop a new source detection and characterization method for massive radio astronomical datasets based on modern deep-learning object detection techniques. For this, we seek to identify the specific strengths and weaknesses of this type of approach when applied to astronomical data. Methods. We introduce YOLO-CIANNA, a highly customized deep-learning object detector designed specifically for astronomical datasets. In this paper, we present the method and describe all the elements introduced to address the specific challenges of radio astronomical images. We then demonstrate the capabilities of this method by applying it to simulated 2D continuum images from the SKA observatory Science Data Challenge 1 (SDC1) dataset. Results. Using the SDC1 metric, we improve the challenge-winning score by +139% and the score of the only other post-challenge participation by +61%. Our catalog has a detection purity of 94% while detecting 40–60% more sources than previous top-score results, and exhibits strong characterization accuracy. The trained model can also be forced to reach 99% purity in post-process and still detect 10–30% more sources than the other top-score methods. It is also computationally efficient, with a peak prediction speed of 500 images of 512×512 pixels per second on a single GPU. Conclusions. YOLO-CIANNA achieves state-of-the-art detection and characterization results on the simulated SDC1 dataset and is expected to transfer well to observational data from SKA precursors.