Radio Dataset Research Articles

Radio U-Net: a convolutional neural network to detect diffuse radio sources in galaxy clusters and beyond

ABSTRACT The forthcoming generation of radio telescope arrays promises significant advancements in sensitivity and resolution, enabling the identification and characterization of many new faint and diffuse radio sources. Conventional manual cataloguing methodologies are anticipated to be insufficient to exploit the capabilities of new radio surveys. Radio interferometric images of diffuse sources present a challenge for image segmentation tasks due to noise, artifacts, and embedded radio sources. In response to these challenges, we introduce Radio U-Net, a fully convolutional neural network based on the U-Net architecture. Radio U-Net is designed to detect faint and extended sources in radio surveys, such as radio haloes, relics, and cosmic web filaments. Radio U-Net was trained on synthetic radio observations built upon cosmological simulations and then tested on a sample of galaxy clusters, where the detection of cluster diffuse radio sources relied on customized data reduction and visual inspection of Low-Frequency Array Two metre Sky Survey (LoTSS) data. The 83 per cent of clusters exhibiting diffuse radio emission were accurately identified, and the segmentation successfully recovered the morphology of the sources even in low-quality images. In a test sample comprising 246 galaxy clusters, we achieved a 73 per cent accuracy rate in distinguishing between clusters with and without diffuse radio emission. Our results establish the applicability of Radio U-Net to extensive radio survey data sets, probing its efficiency on cutting-edge high-performance computing systems. This approach represents an advancement in optimizing the exploitation of forthcoming large radio surveys for scientific exploration.

LoRa Radio Frequency Fingerprinting with Residual of Variational Mode Decomposition and Hybrid Machine-Learning/Deep-Learning Optimization

Radio Frequency Fingerprinting (RFF) refers to the technique for identifying and classifying wireless devices on the basis of their physical characteristics, which appear in the digital signal transmitted in space. Small differences in the radio frequency front-end of the wireless devices are generated across the same wireless device model during the implementation and manufacturing process. These differences create small variations in the transmitted signal, even if the wireless device is still compliant with the wireless standard. By using data analysis and machine-learning algorithms, it is possible to classify different electronic devices on the basis of these variations. This technique has been well proven in the literature, but research is continuing to improve the classification performance, robustness to noise, and computing efficiency. Recently, Deep Learning (DL) has been applied to RFF with considerable success. In particular, the combination of time-frequency representations and Convolutional Neural Networks (CNN) has been particularly effective, but this comes at a great computational cost because of the size of the time-frequency representation and the computing time of CNN. This problem is particularly challenging for wireless standards, where the data to be analyzed is extensive (e.g., long preambles) as in the case of the LoRa (Long Range) wireless standard. This paper proposes a novel approach where two pre-processing steps are adopted to (1) improve the classification performance and (2) to decrease the computing time. The steps are based on the application of Variational Mode Decomposition (VMD) where (in opposition to the known literature) the residual of the VMD application is used instead of the extracted modes. The concept is to remove the modes, which are common among the LoRa devices, and keep with the residuals the unique intrinsic features, which are related to the fingerprints. Then, the spectrogram is applied to the residual component. Even after this step, the computing complexity of applying CNN to the spectrogram is high. This paper proposes a novel step where only segments of the spectrogram are used as input to CNN. The segments are selected using a machine-learning approach applied to the features extracted from the spectrogram using the Local Binary Pattern (LBP). The approach is applied to a recent LoRa radio frequency fingerprinting public data set, where it is shown to significantly outperform the baseline approach based on the full use of the spectrogram of the original signal in terms of both classification performance and computing complexity.

Enabling unsupervised discovery in astronomical images through self-supervised representations

ABSTRACT Unsupervised learning, a branch of machine learning that can operate on unlabelled data, has proven to be a powerful tool for data exploration and discovery in astronomy. As large surveys and new telescopes drive a rapid increase in data size and richness, these techniques offer the promise of discovering new classes of objects and of efficient sorting of data into similar types. However, unsupervised learning techniques generally require feature extraction to derive simple but informative representations of images. In this paper, we explore the use of self-supervised deep learning as a method of automated representation learning. We apply the algorithm Bootstrap Your Own Latent to Galaxy Zoo DECaLS images to obtain a lower dimensional representation of each galaxy, known as features. We briefly validate these features using a small supervised classification problem. We then move on to apply an automated clustering algorithm, demonstrating that this fully unsupervised approach is able to successfully group together galaxies with similar morphology. The same features prove useful for anomaly detection, where we use the framework astronomaly to search for merger candidates. While the focus of this work is on optical images, we also explore the versatility of this technique by applying the exact same approach to a small radio galaxy data set. This work aims to demonstrate that applying deep representation learning is key to unlocking the potential of unsupervised discovery in future data sets from telescopes such as the Vera C. Rubin Observatory and the Square Kilometre Array.

Solar Radio Burst Detection Based on the MobileViT-SSDLite Lightweight Model

Real-time detection of solar radio bursts is crucial in solar physics research and space weather forecasting. However, current research on the automatic detection of solar radio bursts is limited to identifying the presence or absence of solar radio bursts or recognizing only a single type of burst, such as type II or III. Furthermore, existing methods cannot learn spectral and temporal features and often suffer from the drawbacks of large network models, resulting in slow speeds. This paper proposes an automatic recognition and localization method based on a lightweight object detection model for solar radio burst events. We collected observation data from e-CALLISTO and established a data set containing type II, III, IV, and V solar radio bursts. To address the real-time requirements of practical applications and consider the temporal and frequency domain information of spectrogram images, we improved a vision transformer with a self-attention mechanism and adopted a lightweight model for detection. The experimental results demonstrate that our proposed method achieves an average precision at a 50% intersection-over-union threshold of 78.2% and a recall rate of 92% on the established solar radio burst data set. Additionally, the model operates at a detection speed of 54.8 frames s–1, where a frame refers to a spectral image with a duration of 15 minutes, enabling efficient automated detection and localization of type II, III, IV, and V solar radio bursts.

An off-axis relativistic jet seen in the long lasting delayed radio flare of the TDE AT 2018hyz

ABSTRACT The Tidal Disruption Event (TDE) AT 2018hyz exhibited a delayed radio flare almost three years after the stellar disruption. Here, we report new radio observations of the TDE AT 2018hyz with the AMI-LA and ATCA spanning from a month to more than four years after the optical discovery and 200 d since the last reported radio observation. We detected no radio emission from 30–220 d after the optical discovery in our observations at 15.5 GHz down to a 3σ level of <0.14 mJy. The fast-rising, delayed radio flare is observed in our radio data set and continues to rise almost ∼1580 d after the optical discovery. We find that the delayed radio emission, first detected 972 d after optical discovery, evolves as t4.2 ± 0.9, at 15.5 GHz. Here, we present an off-axis jet model that can explain the full set of radio observations. In the context of this model, we require a powerful narrow jet with an isotropic equivalent kinetic energy Ek, iso ∼ 1055 erg, an opening angle of ∼7°, and a relatively large viewing angle of ∼42°, launched at the time of the stellar disruption. Within our framework, we find that the minimal collimated energy possible for an off-axis jet from AT 2018hyz is Ek ≥ 3 × 1052 erg. Finally, we provide predictions based on our model for the light curve turnover time, and for the proper motion of the radio emitting source.

New black hole spin values for Sagittarius A* obtained with the outflow method

ABSTRACT Six archival Chandra observations are matched with eight sets of radio data and studied in the context of the outflow method to measure and study the spin properties of $\rm {Sgr ~A^{*}}$. Three radio and X-ray data sets obtained simultaneously, or partially simultaneously, are identified as preferred for the purpose of measuring the spin properties of $\rm {Sgr ~A^{*}}$. Similar results are obtained with other data sets. Results obtained with the preferred data sets are combined and indicate weighted mean values of the spin function of $F = 0.62 \pm 0.10$ and dimensionless spin angular momentum of $a_* = 0.90 \pm 0.06$. The spin function translates into measurements of the black hole rotational mass, Mrot, irreducible mass, Mirr, and spin mass–energy available for extraction, Mspin, relative to the total black hole dynamical mass, Mdyn. Weighted mean values of (Mrot/Mdyn) = (0.53 ± 0.06), (Mirr/Mdyn) = (0.85 ± 0.04), (Mspin/Mdyn) = (0.15 ± 0.04), Mrot = (2.2 ± 0.3) × 106 M⊙, Mirr = (3.5 ± 0.2) × 106 M⊙, and Mspin = (6.2 ± 1.6) × 105 M⊙ are obtained; of course (Mrot/Mirr) = (0.62 ± 0.10) since (Mrot/Mirr) = F. Values obtained for $\rm {Sgr ~A^{*}}$ are compared with those obtained for M87 based on the published spin function, which indicate that M87 carries substantially more rotational energy and spin mass–energy relative to the total (i.e. dynamical) black hole mass, the irreducible black hole mass, and in absolute terms.

BEYONDPLANCK

We describe the BEYONDPLANCKproject in terms of our motivation, methodology, and main products, and provide a guide to a set of companion papers that describe each result in more detail. Building directly on experience from ESA’sPlanckmission, we implemented a complete end-to-end Bayesian analysis framework for thePlanckLow Frequency Instrument (LFI) observations. The primary product is a full joint posterior distributionP(ω ∣ d), whereωrepresents the set of all free instrumental (gain, correlated noise, bandpass, etc.), astrophysical (synchrotron, free-free, thermal dust emission, etc.), and cosmological (cosmic microwave background – CMB – map, power spectrum, etc.) parameters. Some notable advantages of this approach compared to a traditional pipeline procedure are seamless end-to-end propagation of uncertainties; accurate modeling of both astrophysical and instrumental effects in the most natural basis for each uncertain quantity; optimized computational costs with little or no need for intermediate human interaction between various analysis steps; and a complete overview of the entire analysis process within one single framework. As a practical demonstration of this framework, we focus in particular on low-ℓCMB polarization reconstruction withPlanckLFI. In this process, we identify several important new effects that have not been accounted for in previous pipelines, including gain over-smoothing and time-variable and non-1/fcorrelated noise in the 30 and 44 GHz channels. Modeling and mitigating both previously known and newly discovered systematic effects, we find that all results are consistent with the ΛCDM model, and we constrained the reionization optical depth toτ = 0.066 ± 0.013, with a low-resolution CMB-basedχ2probability to exceed of 32%. This uncertainty is about 30% larger than the official pipelines, arising from taking a more complete instrumental model into account. The marginal CMB solar dipole amplitude is 3362.7 ± 1.4 μK, where the error bar was derived directly from the posterior distribution without the need of any ad hoc instrumental corrections. We are currently not aware of any significant unmodeled systematic effects remaining in thePlanckLFI data, and, for the first time, the 44 GHz channel is fully exploited in the current analysis. We argue that this framework can play a central role in the analysis of many current and future high-sensitivity CMB experiments, including LiteBIRD, and it will serve as the computational foundation of the emerging community-wide COSMOGLOBEeffort, which aims to combine state-of-the-art radio, microwave, and submillimeter data sets into one global astrophysical model.

Bits Missing: Finding Exotic Pulsars Using bfloat16 on NVIDIA GPUs

The Fourier domain acceleration search (FDAS) is an effective technique for detecting faint binary pulsars in large radio astronomy data sets. This paper quantifies the sensitivity impact of reducing numerical precision in the graphics processing unit (GPU)-accelerated FDAS pipeline of the AstroAccelerate (AA) software package. The prior implementation used IEEE-754 single-precision in the entire binary pulsar detection pipeline, spending a large fraction of the runtime computing GPU-accelerated fast Fourier transforms. AA has been modified to use bfloat16 (and IEEE-754 double-precision to provide a “gold standard” comparison) within the Fourier domain convolution section of the FDAS routine. Approximately 20,000 synthetic pulsar filterbank files representing binary pulsars were generated using SIGPROC with a range of physical parameters. They have been processed using bfloat16, single-precision, and double-precision convolutions. All bfloat16 peaks are within 3% of the predicted signal-to-noise ratio of their corresponding single-precision peaks. Of 14,971 “bright” single-precision fundamental peaks above a power of 44.982 (our experimentally measured highest noise value), 14,602 (97.53%) have a peak in the same acceleration and frequency bin in the bfloat16 output plane, while in the remaining 369 the nearest peak is located in the adjacent acceleration bin. There is no bin drift measured between the single- and double-precision results. The bfloat16 version of FDAS achieves a speedup of approximately 1.6× compared to single-precision. A comparison between AA and the PRESTO software package is presented using observations collected with the GMRT of PSR J1544+4937, a 2.16 ms black widow pulsar in a 2.8 hr compact orbit.

Exoplanet Radio Transits as a Probe for Exoplanetary Magnetic Fields—Time-dependent MHD Simulations

We perform a series of time-dependent magnetohydrodynamic simulations of the HD 189733 star–planet system in order to predict radio transit modulations due to the interaction between the stellar wind and planetary magnetic field. The simulation combines a model for the stellar corona and wind with an exoplanet that is orbiting the star in a fully dynamic, time-dependent manner. Our simulations generate synthetic radio images that enable us to obtain synthetic radio light curves in different frequencies. We find a clear evidence for the planetary motion in the radio light curves. Moreover, we find specific repeated features in the light curves that are attributed to the passage of the planetary magnetosphere in front of the star during transit. More importantly, we find a clear dependence in magnitude and phase of these light-curve features on the strength of the planetary magnetic field. Our work demonstrates that if radio transits could be observed, they could indeed provide information about the magnetic field strength of the transiting exoplanet. Future work to parameterize these light-curve features and their dependence on the planetary field strength would provide tools to search for these features in radio observation data sets. As we only consider the thermal radio emission from the host star for our study, very sensitive radio interferometers are necessary to detect these kinds of planetary transits in radio.

ShapeNet: Shape constraint for galaxy image deconvolution

Deep learning (DL) has shown remarkable results in solving inverse problems in various domains. In particular, the Tikhonet approach is very powerful in deconvolving optical astronomical images. However, this approach only uses the ℓ2 loss, which does not guarantee the preservation of physical information (e.g., flux and shape) of the object that is reconstructed in the image. A new loss function has been proposed in the framework of sparse deconvolution that better preserves the shape of galaxies and reduces the pixel error. In this paper, we extend the Tikhonet approach to take this shape constraint into account and apply our new DL method, called ShapeNet, to a simulated optical and radio-interferometry dataset. The originality of the paper relies on i) the shape constraint we use in the neural network framework, ii) the application of DL to radio-interferometry image deconvolution for the first time, and iii) the generation of a simulated radio dataset that we make available for the community. A range of examples illustrates the results.

Radio Galaxy Zoo: using semi-supervised learning to leverage large unlabelled data sets for radio galaxy classification under data set shift

ABSTRACT In this work, we examine the classification accuracy and robustness of a state-of-the-art semi-supervised learning (SSL) algorithm applied to the morphological classification of radio galaxies. We test if SSL with fewer labels can achieve test accuracies comparable to the supervised state of the art and whether this holds when incorporating previously unseen data. We find that for the radio galaxy classification problem considered, SSL provides additional regularization and outperforms the baseline test accuracy. However, in contrast to model performance metrics reported on computer science benchmarking data sets, we find that improvement is limited to a narrow range of label volumes, with performance falling off rapidly at low label volumes. Additionally, we show that SSL does not improve model calibration, regardless of whether classification is improved. Moreover, we find that when different underlying catalogues drawn from the same radio survey are used to provide the labelled and unlabelled data sets required for SSL, a significant drop in classification performance is observed, highlighting the difficulty of applying SSL techniques under data set shift. We show that a class-imbalanced unlabelled data pool negatively affects performance through prior probability shift, which we suggest may explain this performance drop, and that using the Fréchet distance between labelled and unlabelled data sets as a measure of data set shift can provide a prediction of model performance, but that for typical radio galaxy data sets with labelled sample volumes of $\mathcal {O}(10^3)$, the sample variance associated with this technique is high and the technique is in general not sufficiently robust to replace a train–test cycle.

RadioLensfit: An HPC tool for accurate galaxy shape measurement with SKA

The new generation radio telescopes, such as the Square Kilometre Array (SKA), are expected to reach sufficient sensitivity and resolution to provide large number densities of resolved faint sources, and therefore to open weak gravitational lensing observations to the radio band. In this paper we present RadioLensfit, an open-source tool for an efficient and fast galaxy shape measurement for radio weak lensing shear. It performs a single source model fitting in the Fourier domain, after isolating the source visibilities with a sky model and a faceting technique. This approach makes real sized radio datasets accessible to an analysis in this domain, where data is not yet affected by the systematics introduced by the non-linear imaging process. We detail the implementation of the code and discuss limitations of the source extraction algorithm. We describe the hybrid parallelization MPI+OpenMP of the code, implemented to exploit multi-node HPC infrastructures for accelerating the computation and dealing with very large datasets that possibly cannot entirely be stored in the memory of a single processor. Finally, we present performance results both in terms of measurement accuracy and code scalability on SKA-MID simulated datasets. In particular, we compare shape measurements of 1000 sources at the expected source density in SKA Phase 1 with the ones obtained from the same dataset in a previous work by a joint fitting of the raw visibility data, and show that results are comparable while the computational time is highly reduced.

Processing and Analysis for Radio Science Experiments (PARSE): Graphical Interface for Bistatic Radar

Abstract Opportunistic bistatic radar (BSR) observations of planetary surfaces can probe the textural and electrical properties of several solar system bodies without needing a dedicated instrument or additional mission requirements, providing unique insights into volatile enrichment and supporting future landing, anchoring, and in situ sampling. Given their opportunistic nature, complex observation geometries, and required radiometric knowledge of the received radio signal, these data are particularly challenging to process, analyze, and interpret for most planetary science data users, who can be unfamiliar with link budget analysis of received echoes. The above impedes real-time use of BSR data to support mission operations, such as identifying safe landing locations on small bodies, as was the case for the Rosetta mission. To address this deficiency, we develop an open-source graphical user interface—Processing and Analysis for Radio Science Experiments (PARSE)—that assesses the feasibility of performing BSR observations and automates radiometric signal processing, power spectral analysis, and visualization of DSN planetary radio science data sets acquired during mission operations or archived on NASA’s Planetary Data System. In this first release, PARSE automates the processing chain developed for Dawn at Asteroid Vesta, streamlining the detection of DSN-received surface-scatter echoes generated as the spacecraft enters/exits occultations behind the target. Future releases will include support for existing Arecibo data sets and other Earth-based radio observatories. Our tool enables the broader planetary science community, beyond planetary radar signal processing experts, to utilize BSR data sets to characterize electrical and textural properties of planetary surfaces. Such tools are becoming increasingly important as the number of space missions—and subsequent opportunities for orbital radio science observations—continue to grow.

Are Delayed Radio Flares Common in Tidal Disruption Events? The Case of the TDE iPTF 16fnl

Abstract Radio emission from tidal disruption events (TDEs) originates from an interaction of an outflow with the super-massive black hole (SMBH) circumnuclear material (CNM). In turn, this radio emission can be used to probe properties of both the outflow launched at the event and the CNM. Until recently, radio emission was detected only for a relatively small number of events. While the observed radio emission pointed to either relativistic or sub-relativistic outflows of different nature, it also indicated that the outflow has been launched shortly after stellar disruption. Recently, however, delayed radio flares, several months and years after stellar disruption, were reported in the case of the TDE ASASSN-15oi. These delayed flares suggest a delay in the launching of outflows and thus may provide new insights into SMBH accretion physics. Here, we present a new radio data set of another TDE, iPTF 16fnl, and discuss the possibility that a delayed radio flare also has been observed in this case, ∼5 months after optical discovery, suggesting that this phenomenon may be common in TDEs. Unlike ASASSN-15oi, the data for iPTF 16fnl is sparse and the delayed radio flare can be explained by several alternative models: among them are a complex varying CNM density structure and a delayed outflow ejection.

Feature selection for improving Indian spoken language identification in utterance duration mismatch condition

In spoken language identification (SLID) systems, the test data may be of a sufficiently shorter duration than training data, known as duration mismatch condition. Duration normalized features are used to identify a spoken language for nine Indian languages in duration mismatch conditions. Random forest-based importance vectors of 1582 OpenSMILE features are calculated for each utterance in different duration datasets. The feature importance vectors are normalized across each dataset and later across different duration datasets. The optimal number of duration normalized features is selected to maximize SLID system accuracy. Three classifiers, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), and their fusion, weights optimized using logistic regression, are used. The speech material comprised utterances, each of 30 sec, extracted from the All India Radio dataset with nine Indian languages. Seven new datasets of smaller utterance durations were generated by carefully splitting each utterance. Experimental results showed that 150 most important duration normalized features were optimal with a relative increase in 18-80% accuracy for mismatch conditions. The accuracy decreased with increased duration mismatch.

Tumbling Dice: Radio Constraints on the Presence of Circumstellar Shells around Type Ia Supernovae with Impact Near Maximum Light

Abstract The progenitors of Type Ia supernovae (SNe Ia) are debated, particularly the evolutionary state of the binary companion that donates mass to the exploding carbon–oxygen white dwarf. In our previous work, we presented hydrodynamic models and optically thin radio synchrotron light curves of SNe Ia interacting with detached, confined shells of CSM, representing CSM shaped by novae. In this work, we extend these light curves to the optically thick regime, considering both synchrotron self-absorption and free–free absorption. We obtain simple formulae to describe the evolution of optical depth seen in the simulations, allowing optically thick light curves to be approximated for arbitrary shell properties. We then demonstrate the use of this tool by interpreting published radio data. First, we consider the nondetection of PTF11kx—an SN Ia known to have a detached, confined shell—and we find that the nondetection is consistent with current models for its CSM, and that observations at a later time would have been useful for this event. Second, we statistically analyze an ensemble of radio nondetections for SNe Ia with no signatures of interaction. We find that shells with masses (10−4–0.3) M ⊙ located (1015–1016) cm from the progenitor are currently not well constrained by radio datasets, due to their dim, rapidly evolving light curves.

Shared Spectrum Monitoring Using Deep Learning

Shared spectrum usage is inevitable due to the ongoing increase in wireless services and bandwidth requirements. Spectrum monitoring is a key enabler for efficient spectrum sharing by multiple radio access technologies (RATs). In this paper, we present signal classification using deep neural networks to identify various radio technologies and their associated interferences. We use Convolutional Neural Networks (CNN) to perform signal classification and employ six well-known CNN models to train for ten signal classes. These classes include LTE, Radar, WiFi and FBMC (Filter Bank Multicarrier) and their interference combinations, which include, LTE + Radar, LTE + WiFi, FBMC + Radar, FBMC + WiFi, WiFi + Radar and Noise. The CNN models include, AlexNet, VGG16, ResNet18, SqueezeNet, InceptionV3 and ResNet50. The radio signal data sets for training and testing of CNN-based classifiers are acquired using a USRP-based experimental setup. Extensive measurements of these radio technologies (LTE, WiFi, Radar and FBMC) are done over different locations and times to generate a robust dataset. We propose a novel (spectrogram) representation called the Quarter-spectrogram (Q-spectrogram) that squeezes temporal and frequency information for input to CNN models. While considering classification accuracy, model complexity and prediction time for a single input Q-spectrogram (image), ResNet18 (CNN model) gives the best overall performance with 98% classification accuracy. While SqueezeNet (CNN model) offers the lowest model complexity which makes it very suitable for resource-constrained radio monitoring devices and also offers the least prediction time of 110 msec. Moreover, we also propose a simple WiFi classification scheme that buffers several WiFi Q-spectrograms and then makes a decision about WiFi’s presence and also gives a quantified measure of WiFi traffic density.

PERIODICITY DETECTION IN AGN WITH THE BOOSTED TREE METHOD

We apply a machine learning algorithm called XGBoost to explore the periodicity of two radio sources: PKS 1921-293 (OV 236) and PKS 2200+420 (BL Lac), both radio frequency datasets obtained from University of Michigan Radio Astronomy Observatory (UMRAO), at 4.8 GHz, 8.0 GHz, and 14.5 GHz, between 1969 to 2012. From this methods, we find that the XGBoost provides the opportunity to use a machine learning based methodology on radio datasets and to extract information with strategies quite different from those traditionally used to treat time series, as well as to obtain periodicity through the classification of recurrent events. The results were compared with other methods that examined the same datasets and exhibit a good agreement with them.

A Non-equipartition Shock Wave Traveling in a Dense Circumstellar Environment around SN 2020oi

Abstract We report the discovery and panchromatic follow-up observations of the young Type Ic supernova (SN Ic) SN 2020oi in M100, a grand-design spiral galaxy at a mere distance of 14 Mpc. We followed up with observations at radio, X-ray, and optical wavelengths from only a few days to several months after explosion. The optical behavior of the supernova is similar to those of other normal SNe Ic. The event was not detected in the X-ray band but our radio observations revealed a bright mJy source ( L ν ≈ 1.2 × 10 27 erg s − 1 Hz − 1 ). Given the relatively small number of stripped envelope SNe for which radio emission is detectable, we used this opportunity to perform a detailed analysis of the comprehensive radio data set we obtained. The radio-emitting electrons initially experience a phase of inverse Compton cooling, which leads to steepening of the spectral index of the radio emission. Our analysis of the cooling frequency points to a large deviation from equipartition at the level of ϵ e /ϵ B ≳ 200, similar to a few other cases of stripped envelope SNe. Our modeling of the radio data suggests that the shock wave driven by the SN ejecta into the circumstellar matter (CSM) is moving at ∼ 3 × 10 4 km s − 1 . Assuming a constant mass loss from the stellar progenitor, we find that the mass-loss rate is M ̇ ≈ 1.4 × 10 − 4 M ⊙ yr − 1 for an assumed wind velocity of 1000 km s − 1 . The temporal evolution of the radio emission suggests a radial CSM density structure steeper than the standard r −2.

On the link between the topside ionospheric effective scale height and the plasma ambipolar diffusion, theory and preliminary results

Over the years, an amount of models relying on effective parameters were implemented in the challenging issue of the topside ionosphere description. These models are based on different analytical functions, but all of them depend on a parameter called effective scale height, that is deduced from topside electron density measurements. As their names state, they are effective in reproducing the topside electron density profile only when applied to the analytical function used to derive them. Then, in principle, they do not have any physical meaning. It is the goal of this paper to mathematically link the effective scale height modeled through the Epstein layer to the vertical scale height theoretically deduced from the plasma ambipolar diffusion theory. Firstly, effective and theoretical scale heights are linked through a mathematical relation by showing that they tend to each other in the topside ionosphere. Secondly, their connection is preliminarily demonstrated by calculating effective scale height values from the entire COSMIC/FORMOSAT-3 radio occultation dataset. Thirdly, a possible connection between the vertical gradient of the topside scale height (as obtained by COSMIC/FORMOSAT-3 satellites) and the electron temperature (as obtained by ESA Swarm B satellite) is studied by highlighting corresponding similarities in the diurnal, seasonal, solar activity, and latitudinal variability.