Search Results (23)

We present a comprehensive study of host galaxies of radio sources within the 1.35$R_{200}$ of the Coma cluster by combining deep $144\mathrm{MHz}$ observations from the LOFAR Two-Metre Sky Survey (LoTSS-DR2) with optical spectroscopy and photometry from DESI and SDSS. We identify 79 spectroscopically confirmed cluster members with reliable radio emission and classify them into compact, extended, and tailed subsamples according to their radio morphologies. By combining their radio and optical properties, we find compact radio sources are predominantly associated with massive, quiescent galaxies driven by $\mathrm{AGN}$ activity, while tailed sources are largely hosted by star-forming galaxies, tracing ongoing ram pressure stripping ($\mathrm{RPS}$). Using phase-space analysis and a projected infall time proxy ($d_R$), we find that extended sources are preferentially located in the cluster outskirts ($d_R>1$), while tailed sources are concentrated in the intermediate infall region ($0.4<d_R<1.0$), highlighting the influence of the dense intracluster medium. Full article

STRange and Odd Morphology Extragalactic Radio Sources (STROMERSs) is a new category of radio galaxies that shows extremely peculiar anatomy. A purely manual visual search is carried out for the identification of such interesting sources. We reported a total of 108 STROMERS sources from the LOFAR Two-meter Sky Survey second data release (LoTSS DR2) at 144 MHz. The host galaxies are found ∼94% of the sources. We studied the radio and optical properties of the sources. Redshifts were found in 76% of sources with known host galaxies. The redshifts of STROMERS range from 0.0015 to 1.6599 and peak at 0.15. Among the reported STROMERS sources, there are 17 giant radio galaxies (GRG) with a linear size of greater than 700 kpc. Among them, only five GRGs are new, which is a small fraction of the population of GRGs from LoTSS DR2 data. The source ILTJ164117.44 +380208.4 has the highest linear size, approximately 1.8 Mpc. To study the reasons behind these interesting morphologies, we studied the galaxy cluster environment of each candidate within a 1 Mpc search radius. We found that 53% of STROMERS candidates are associated with cluster environments with known redshifts. The source ILTJ150956.65+332642.9 is associated with a high mass galaxy cluster Abell 2034 with mass a 7.57 $\times {10}^{14}{M}_{\odot }$. We also propose that the merger scenario is one of the reasons for the formation of STROMERS in the paper. Full article

We report the RAD@home citizen science discovery of two rare spiral-host radio galaxies (NGC 3898 and WISEA J221656.57-132042434.1 or RAD-“Thumbs up” galaxy), both exhibiting asymmetric radio lobes extending over 30 to 220 kiloparsec scales. We present a multi-wavelength image analysis of these two sources using radio, optical, and ultraviolet data. Both host galaxies are young, star-forming systems with asymmetric or distorted stellar disks. These disks show similarities to those in galaxies undergoing ram pressure stripping, and the radio morphologies resemble those of asymmetric or bent FR-II and wide-angle-tailed radio galaxies. We suggest that non-uniform gas density in the environment surrounding the ram pressure-stripped disks may contribute to the observed asymmetry in the size, shape, and brightness of bipolar radio lobes. Such environmental effects, when properly accounted for, could help explain many of the non-standard radio morphologies observed in Seyfert galaxies and in recently identified populations of galaxies with galaxy-scale radio jets, which are now being revealed through deep and sensitive radio surveys with uGMRT, MeerKAT, LOFAR, and, in the future, SKAO. These findings also underscore the potential of citizen science to complement professional research and data-driven approaches involving machine learning and artificial intelligence in the analysis of complex radio sources. Full article

Ship seismic wave signals represent one of the most critical physical field characteristics of vessels. To achieve the high-precision detection of ship seismic wave field signals in marine environments, an underwater seismic wave signal detection system was designed. The system adopts a three-stage architecture consisting of watertight instrument housing, a communication circuit, and a buoy to realize high-capacity real-time data transmissions. The host computer performs the collaborative optimization of multi-modal hardware architecture and adaptive signal processing algorithms, enabling the detection of ship targets in oceanic environments. Through verification in a water tank and sea trials, the system successfully measured seismic wave signals. An improved ALE-LOFAR (Adaptive Line Enhancer–Low-Frequency Analysis) joint framework, combined with DEMON (Demodulation of Envelope Modulation) demodulation technology, was proposed to conduct the spectral feature analysis of ship seismic wave signals, yielding the low-frequency signal characteristics of vessels. This scheme provides an important method for the covert monitoring of shallow-sea targets, providing early warnings of illegal fishing and ensuring underwater security. Full article

An ocean target electric field signal is an effective approach for analyzing the ocean environment and is widely used for detecting ocean targets, extracting their features, and tracking them. Low-frequency analysis and recording (LOFAR) is a commonly used time–frequency analysis tool that provides the time–frequency spectrum of a signal; however, its reliance on the Fourier transform (FT) results in a low frequency resolution and signal-to-noise ratio (SNR), which limits its target detection capabilities. To address this problem, we propose a method called low-frequency analysis and recording based on basis pursuit (LOFAR-BP) for analyzing and detecting ocean target electric field signals. LOFAR-BP uses basis pursuit (BP) with the L1 norm for frequency analysis, whereas LOFAR utilizes the FT. We demonstrate that the FT is the L2 norm mathematically. LOFAR-BP generates the time–frequency spectrum in the same way that LOFAR does. By extracting characteristic values from the time–frequency spectrum, targets can be detected using an appropriate threshold. Both simulation and ocean experiments showed that LOFAR-BP effectively enhances target signals and suppresses noise. Compared with LOFAR, LOFAR-BP improved the frequency resolution by 60% in both experiments and increased the SNR by 54.82 dB in the simulation experiment and by 39.59 dB in the ocean experiment. When applied to target detection, LOFAR-BP can detect targets 6 s earlier than LOFAR can. Full article

In complex marine environments, extracting target features from acoustic signal is very difficult, making the targets hard to be recognized. Therefore, it is necessary to perform denoising method on the acoustic signal to highlight the target features. However, training deep learning denoising models requires a large mount of acoustic data with labels and obtaining labels with real measured data is also extremely difficult. In this paper, an enhanced simulation algorithm, which considers integrating features of target line spectrum and ocean environmental noise, is proposed to construct a large-scale training sample set. Additionally, a deep convolutional denoising model is presented, which is first train on simulated data and directly applied to real measured data for denoising, enabling line spectrum to be significantly displayed in the time-frequency spectrogram. The results on simulation experiments and sea trials demonstrate that the proposed method can significantly reduce ocean noise while preserving the characteristics of target line spectrum. Furthermore, the experiments demonstrate that the proposed convolutional denoising model has transferability and generalization, making it suitable for denoising underwater acoustic signal in different marine areas. Full article

In the field of underwater acoustic recognition, machine learning methods rely on a large number of datasets to achieve high accuracy, while the actual collected signal samples are often very scarce, which has a great impact on the recognition performance. This paper presents a recognition method of an underwater acoustic target by the data augmentation technique and the residual convolutional neural network (CNN) model, which is used to expand training samples to improve recognition performance. As a representative model in residual CNN, the ResNet18 model is used for recognition. The whole process mainly includes mel-frequency cepstral coefficient (MFCC) feature extraction, data augmentation processing, and ResNet18 model recognition. On the base of the traditional data augmentation, this study used the deep convolutional generative adversarial network (DCGAN) model to realize the expansion of underwater acoustic samples and compared the recognition performance of support vector machine (SVM), common CNN, VGG19, and ResNet18. The recognition results of the MFCC, constant Q transform (CQT), and low-frequency analyzer and recorder (LOFAR) spectrum were also analyzed and compared. Experimental results showed that the recognition accuracy of the MFCC feature was better than that of other features at the same method, and using the data augmentation method could obviously improve the recognition performance. Moreover, the recognition performance of ResNet18 using data enhancement technology was better than that of other models, which was due to the combination of the data expansion advantage of data augmentation technology and the deep feature extracting ability of the residual CNN model. In addition, although this method was used for ship recognition in this paper, it is not limited to this. This method is also applicable to other target voice recognition, such as natural sound and underwater voice biometrics. Full article

In a typical underwater acoustic target detection mission, we have to estimate the target number (N), perform source separation when $N>1$, and consequently predict the motion parameters such as fundamental frequency (F₀) from separated noises for each target. Although deep learning methods have been adopted in each task, their successes strongly depend on the feed-in features. In this paper, we evaluate several time-frequency features and propose a universal feature extraction strategy for object counting and F₀ estimation simultaneously, with a convolutional recurrent neural network (CRNN) as the backbone. On one hand, LOFAR and DEMON are feasible for low-speed and high-speed analysis, respectively, and are combined (LOFAR + DEMON) to cope with full-condition estimation. On the other hand, a comb filter (COMB) is designed and applied to the combined spectrum for harmonicity enhancement, which will be further streamed into the CRNN for prediction. Experiments show that (1) in the F₀ estimation task, feeding the filtered combined feature (LOFAR + DEMON + COMB) into the CRNN achieves an accuracy of 98% in the lake trial dataset, which is superior to LOFAR + COMB (83%) or DEMON + COMB (94%) alone, demonstrating that feature combination is plausible. (2) In a counting task, the prediction accuracy of the combined feature (LOFAR + DEMON, COMB included or excluded) is comparable to the state-of-the-art on simulation dataset and dominates the rest on the lake trial dataset, indicating that LOFAR + DEMON can be used as a common feature for both tasks. (3) The inclusion of COMB accelerates the convergence speed of the F₀ estimation task, however, it penalizes the counting task by a depression of 13% on average, partly due to the merging effects brought in by the broadband filtering of COMB. Full article

We try to assess the validity of assumptions taken when deriving drift velocity. We give simple formulas for characteristics of the spatiotemporal correlation function of the observed diffraction pattern for the frozen flow and the more general Briggs model. Using Low-Frequency Array (LOFAR) Cassiopeia intensity observation, we compare the experimental velocity scaling factor with a theoretical one to show that both models do not follow observations. We also give a qualitative comparison of our drift velocity estimates with SuperDARN convection maps. The article is essentially an extended version of the conference paper: “Determining ionospheric drift and anisotropy of irregularities from LOFAR core measurements”, Signal Processing Symposium 2021 (SPSympo 2021). Full article

The noise radiated from ships can be used for their identification and classification using passive sonar systems. Several techniques have been proposed for military ship classification based on acoustic signatures, which can be acquired through controlled experiments performed in an acoustic lane. The cost for such data acquisition is a significant issue since the ship and crew have to be dislocated from the fleet. In addition, the experiments have to be repeated for different operational conditions, taking a considerable amount of time. Even with this massive effort, the scarce amount of data produced by these controlled experiments may limit further detailed analyses. In this paper, deep learning models are used for full exploitation of such acquired data, envisaging passive sonar signal classification. A drawback of such models is the large number of parameters, which requires extensive data volumes for parameter tuning along the training phase. Thus, generative adversarial networks (GANs) are used to synthesize data so that a larger data volume can be produced for training convolutional neural networks (CNNs), which are used for the classification task. Different GAN design approaches were evaluated and both maximum probability and class-expert strategies were exploited for signal classification. Special attention was paid to how the expert knowledge might give a handle on analyzing the performance of the various deep learning models through tests that mirrored actual deployment. An accuracy as high as $99.0\pm 0.4\%$ was achieved using experimental data, which improves upon previous machine learning designs in the field. Full article

The Earth’s ionosphere is a highly variable medium on a wide range of spatio-temporal scales. The responsiveness of plasma to the geomagnetic field and its changes gives rise to anisotropy, which may introduce wave-like characteristics while scanning the ionosphere with a line-of-sight towards a radio source. Previous studies of LOw Frequency ARray (LOFAR) calibration phase solutions report that the estimated beta parameter of a structure function calculated over 6–8 h of astronomical observation timespan has a range of values from 1.6 to 2.0, with an average of 1.89. Such difference between the observations could result from transient wave-like disturbances within the data. This study aims to present a method of signal processing of ionospheric calibration datasets that allows the extraction of a transient wave-like signal and discuss its possible origin. We use complex Morlet wavelet analysis applied to two 8 h observations corresponding to very quiet geomagnetic conditions. We find a wave-like signal in the interferometric Total Electron Content data even during periods of no geomagnetic activity. We suggest it results from the relative velocity changes between the LOFAR line-of-sight and a convection pattern in the ionospheric F layer. Establishing the relationship between quiet time ionosphere, geomagnetic field changes and LOFAR’s calibration solutions may prove beneficial to determination of the dominant signals in the more disturbed conditions, which we leave for future study. Full article

The radiative and jet power in active galactic nuclei is generated by accretion of material on to supermassive galactic-centre black holes. For quasars, where the radiative power is by definition very high, objects with high radio luminosities form 10 per cent of the population, although it is not clear whether this is a stable phase. Traditionally, quasars with high radio luminosities have been thought to present jets with edge-brightened morphology (Fanaroff-Riley II—FR II) due to the limitations of previous radio surveys (i.e., FRIs were not observed as part of the quasar population). The LOw Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) with its unprecedented sensitivity and resolution covering wide sky areas has enabled the first systematic selection and investigation of quasars with core-brightened morphology (Fanaroff-Riley I—FR). We carried out a Very Large Array (VLA) snapshot survey to reveal inner structures of jets in selected quasar candidates; 15 (25 per cent) out of 60 sources show clear inner jet structures that are diagnostic of FRI jets and 13 quasars (∼22 per cent) show extended structures similar to those of FRI jets. Black hole masses and Eddington ratios do not show a clear difference between FRI and FRII quasars. FRII quasars tend to have higher jet powers than FRI quasars. Our results show that the occurrence of FRI jets in powerful radiatively efficient systems is not common, probably mainly due to two factors: galaxy environment and jet power. Full article

The detection of the radio signal from filaments in the cosmic web is crucial to distinguish possible magnetogenesis scenarios. We review the status of the different attempts to detect the cosmic web at radio wavelengths. This is put into the context of the advanced simulations of cosmic magnetism carried out in the last few years by our MAGCOW project. While first attempts of imaging the cosmic web with the MWA and LOFAR have been encouraging and could discard some magnetogenesis models, the complexity behind such observations makes a definitive answer still uncertain. A combination of total intensity and polarimetric data at low radio frequencies that the SKA and LOFAR2.0 will achieve is key to removing the existing uncertainties related to the contribution of many possible sources of signal along deep lines of sight. This will make it possible to isolate the contribution from filaments, and expose its deep physical connection with the origin of extragalactic magnetism. Full article

NenuFAR, the New Extension in Nancay Upgrading LOFAR, is currently in its early science phase. It is in this context that the Cosmic Filaments and Magnetism Pilot Survey is observing sources with the array as it is still under construction—with 57 (56 core, 1 distant) out of a total planned 102 (96 core, 6 distant) mini-arrays online at the time of observation—to get a first look at the low-frequency sky with NenuFAR. One of its targets is the Coma galaxy cluster: a well-known object, host of the prototype radio halo. It also hosts other features of scientific import, including a radio relic, along with a bridge of emission connecting it with the halo. It is thus a well-studied object.In this paper, we show the first confirmed NenuFAR detection of the radio halo and radio relic of the Coma cluster at 34.4 MHz, with associated intrinsic flux density estimates: we find an integrated flux value of 106.3 ± 3.5 Jy for the radio halo, and 102.0 ± 7.4 Jy for the radio relic. These are upper bound values, as they do not include point-source subtraction. We also give an explanation of the technical difficulties encountered in reducing the data, along with steps taken to resolve them. This will be helpful for other scientific projects which will aim to make use of standalone NenuFAR imaging observations in the future. Full article

Active galactic nuclei (AGN) at the centres of galaxies can cycle between periods of activity and of quiescence. Characterising the duty-cycle of AGN is crucial for understanding their impact on the evolution of the host galaxy. For radio AGN, their evolutionary stage can be identified from a combination of morphological and spectral properties. We summarise the results we have obtained in the last few years by studying radio galaxies in various crucial phases of their lives, such as remnant and restarted sources. We used morphological information derived from LOw Frequency ARray (LOFAR) images at 150 MHz, combined with resolved spectral indices maps, obtained using recently released images at 1400 MHz from the APERture Tile In Focus (Apertif) phased-array feed system installed on the Westerbork Synthesis Radio Telescope. Our study, limited so far to the Lockman Hole region, has identified radio galaxies in the dying and restarted phases. We found large varieties in their properties, relevant for understanding their evolutionary stage. We started by quantifying their occurrences, the duration of the ‘on’ (active) and ‘off’ (dying) phase, and we compared the results with models of the evolution of radio galaxies. In addition to these extreme phases, the resolved spectral index images can also reveal interesting secrets about the evolution of apparently normal radio galaxies. The spectral information can be connected with, and used to improve, the Fanaroff–Riley classification, and we present one example of this, illustrating what the combination of the LOFAR and Apertif surveys now allow us to do routinely. Full article