ABSTRACT
We report the discovery of J0624–6948, a low-surface brightness radio ring, lying between the Galactic Plane and the large magellanic cloud (LMC). It was first detected at 888 MHz with the ...Australian Square Kilometre Array Pathfinder (ASKAP), and with a diameter of ∼196 arcsec. This source has phenomenological similarities to odd radio circles (ORCs). Significant differences to the known ORCs – a flatter radio spectral index, the lack of a prominent central galaxy as a possible host, and larger apparent size – suggest that J0624–6948 may be a different type of object. We argue that the most plausible explanation for J0624–6948 is an intergalactic supernova remnant due to a star that resided in the LMC outskirts that had undergone a single-degenerate type Ia supernova, and we are seeing its remnant expand into a rarefied, intergalactic environment. We also examine if a massive star or a white dwarf binary ejected from either galaxy could be the supernova progenitor. Finally, we consider several other hypotheses for the nature of the object, including the jets of an active galactic nucleus (30Dor) or the remnant of a nearby stellar super-flare.
We report the discovery of a new Small Magellanic Cloud pulsar wind nebula (PWN) at the edge of the supernova remnant (SNR) DEM S5. The pulsar powered object has a cometary morphology similar to the ...Galactic PWN analogues PSR B1951+32 and ‘the mouse’. It is travelling supersonically through the interstellar medium. We estimate the pulsar kick velocity to be in the range of 700–2000 km s^−1 for an age between 28 and 10 kyr. The radio spectral index for this SNR–PWN–pulsar system is flat (–0.29 ± 0.01) consistent with other similar objects. We infer that the putative pulsar has a radio spectral index of –1.8, which is typical for Galactic pulsars. We searched for dispersion measures up to 1000 cm^−3 pc but found no convincing candidates with an S/N greater than 8. We produce a polarization map for this PWN at 5500 MHz and find a mean fractional polarization of P ∼ 23 per cent. The X-ray power-law spectrum (Γ ∼ 2) is indicative of non-thermal synchrotron emission as is expected from PWN–pulsar system. Finally, we detect DEM S5 in infrared (IR) bands. Our IR photometric measurements strongly indicate the presence of shocked gas that is expected for SNRs. However, it is unusual to detect such IR emission in an SNR with a supersonic bow shock PWN. We also find a low-velocity H i cloud of ∼107 km s^−1 that is possibly interacting with DEM S5. SNR DEM S5 is the first confirmed detection of a pulsar-powered bow shock nebula found outside the Galaxy.
Context.
The study of active galactic nuclei (AGNs) is fundamental to discern the formation and growth of supermassive black holes (SMBHs) and their connection with star formation and galaxy ...evolution. Due to the significant kinetic and radiative energy emitted by powerful AGNs, they are prime candidates to observe the interplay between SMBH and stellar growth in galaxies.
Aims.
We aim to develop a method to predict the AGN nature of a source, its radio detectability, and redshift purely based on photometry. The use of such a method will increase the number of radio AGNs, allowing us to improve our knowledge of accretion power into an SMBH, the origin and triggers of radio emission, and its impact on galaxy evolution.
Methods.
We developed and trained a pipeline of three machine learning (ML) models than can predict which sources are more likely to be an AGN and to be detected in specific radio surveys. Also, it can estimate redshift values for predicted radio-detectable AGNs. These models, which combine predictions from tree-based and gradient-boosting algorithms, have been trained with multi-wavelength data from near-infrared-selected sources in the
Hobby-Eberly
Telescope Dark Energy Experiment (HETDEX) Spring field. Training, testing, calibration, and validation were carried out in the HETDEX field. Further validation was performed on near-infrared-selected sources in the Stripe 82 field.
Results.
In the HETDEX validation subset, our pipeline recovers 96% of the initially labelled AGNs and, from AGNs candidates, we recover 50% of previously detected radio sources. For Stripe 82, these numbers are 94% and 55%. Compared to random selection, these rates are two and four times better for HETDEX, and 1.2 and 12 times better for Stripe 82. The pipeline can also recover the redshift distribution of these sources with
σ
NMAD
= 0.07 for HETDEX (
σ
NMAD
= 0.09 for Stripe 82) and an outlier fraction of 19% (25% for Stripe 82), compatible with previous results based on broad-band photometry. Feature importance analysis stresses the relevance of near- and mid-infrared colours to select AGNs and identify their radio and redshift nature.
Conclusions.
Combining different algorithms in ML models shows an improvement in the prediction power of our pipeline over a random selection of sources. Tree-based ML models (in contrast to deep learning techniques) facilitate the analysis of the impact that features have on the predictions. This prediction can give insight into the potential physical interplay between the properties of radio AGNs (e.g. mass of black hole and accretion rate).
With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic ...and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to
$z = 3$
before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify
$\approx$
76% of the highest redshift sources—sources at redshift
$z > 2.51$
—as being in either the highest bin (
$z > 2.51$
) or second highest (
$z = 2.25$
).
We developed and trained a pipeline of three machine learning (ML) models than can predict which sources are more likely to be an AGN and to be detected in specific radio surveys. Also, it can ...estimate redshift values for predicted radio-detectable AGNs. These models, which combine predictions from tree-based and gradient-boosting algorithms, have been trained with multi-wavelength data from near-infrared-selected sources in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) Spring field. Training, testing, calibration, and validation were carried out in the HETDEX field. Further validation was performed on near-infrared-selected sources in the Stripe 82 field. In the HETDEX validation subset, our pipeline recovers 96% of the initially labelled AGNs and, from AGNs candidates, we recover 50% of previously detected radio sources. For Stripe 82, these numbers are 94% and 55%. Compared to random selection, these rates are two and four times better for HETDEX, and 1.2 and 12 times better for Stripe 82. The pipeline can also recover the redshift distribution of these sources with \(\sigma_{\mathrm{NMAD}}\) = 0.07 for HETDEX (\(\sigma_{\mathrm{NMAD}}\) = 0.09 for Stripe 82) and an outlier fraction of 19% (25% for Stripe 82), compatible with previous results based on broad-band photometry. Feature importance analysis stresses the relevance of near- and mid-infrared colours to select AGNs and identify their radio and redshift nature. Combining different algorithms in ML models shows an improvement in the prediction power of our pipeline over a random selection of sources. Tree-based ML models (in contrast to deep learning techniques) facilitate the analysis of the impact that features have on the predictions. This prediction can give insight into the potential physical interplay between the properties of radio AGNs (e.g. mass of black hole and accretion rate).
Severe ADAMTS13 deficiency is a critical component of the pathogenesis of idiopathic thrombotic thrombocytopenic purpura but is found only in about 60% of patients clinically diagnosed with this ...disease.
Over a period of 8 years and six episodes of thrombotic thrombocytopenic purpura we studied the evolution of the anti-ADAMTS13 antibody response in a patient using different ADAMTS13 assays and epitope mapping.
Anti-ADAMTS13 autoantibodies were found in all episodes but were inhibitory only in the last two episodes. In a flow-based assay, normal ADAMTS13 activity was found only during the first disease episode, while ADAMTS13 activity was normal using a static assay in episodes 1 and 3, and severely deficient in the last two episodes. Fluorescence evolution in a modified fluorescence resonance energy transfer assay using a von Willebrand factor A2 domain peptide substrate was linear in episodes 1, 5 and 6, but increased exponentially in episodes 3 and 4. Despite the variable functional characteristics of the anti-ADAMTS13 autoantibodies, their principal epitope was the ADAMTS13 spacer domain in all episodes.
The patient is unique as he displayed features of maturation or shaping of the anti-ADAMTS13 autoantibody response during the course of multiple episodes of thrombotic thrombocytopenic purpura. Anti-ADAMTS13 autoantibodies may be important in vivo despite normal ADAMTS13 activity in routine assays. Consequently, treatment decisions should not be based solely on activity assay results.
Essentials
The role of von Willebrand Factor (VWF) in the pathophysiology of sickle cell disease is unclear.
We assessed markers of VWF during admission for vaso‐occlusive crisis (VOC) and steady ...state.
VWF reactivity was higher during VOC and was associated with inflammation and neutrophil activation.
Hyper‐adhesive VWF may promote VOC in sickle cell disease.
Summary
Background
Endothelial activation plays a central role in the pathophysiology of vaso‐occlusion in sickle cell disease (SCD), facilitating adhesive interactions with circulating blood cells. Upon activation, various adhesive molecules are expressed, including von Willebrand factor (VWF). Increased VWF levels have been observed in patients with SCD during steady state. However, the role of VWF in the pathogenesis of SCD vaso‐occlusion is unclear.
Objectives
To longitudinally assess the quantity and reactivity of VWF and its regulating protease ADAMTS‐13 during vaso‐occlusive crisis (VOC).
Methods
In this observational study, we obtained sequential blood samples in adult SCD patients during VOC.
Results
VWF reactivity was significantly higher during VOC (active VWF, VWF glycoprotein Ib‐binding activity, and high molecular weight multimers), whereas platelet count and levels of ADAMTS‐13 antigen and ADAMTS‐13 activity were concomitantly lower than during steady state. Levels of VWF antigen, VWF propeptide (VWF:pp) and ADAMTS‐13 specific activity did not change during VOC. VWF reactivity correlated strongly with markers of inflammation and neutrophil activation, and was inversely correlated with the platelet count. In patients who developed acute chest syndrome, levels of VWF, VWF:pp and active, hyperadhesive VWF were significantly higher, whereas ADAMTS‐13 activity was lower, than in patients without this complication.
Conclusions
We provide the first evidence that VOC in SCD is associated with increased reactivity of VWF, without a pronounced ADAMTS‐13 deficiency. This hyper‐reactivity may be explained by resistance of VWF to proteolysis, secondary to processes such as inflammation and oxidative stress. Hyperadhesive VWF, scavenging blood cells in the microcirculation, may thereby amplify and sustain VOC in SCD.
Context. The youngest Galactic supernova remnant G1.9+0.3 is an interesting target for next generation gamma-ray observatories. So far, the remnant is only detected in the radio and the X-ray bands, ...but its young age of ~100 yrs and inferred shock speed of ~14,000 km/s could make it an efficient particle accelerator. Aims. We aim to model the observed radio and X-ray spectra together with the morphology of the remnant. At the same time, we aim to estimate the gamma-ray flux from the source and evaluated the prospects of its detection with future gamma-ray experiments. Methods. We performed spherical symmetric 1-D simulations with the RATPaC code, in which we simultaneously solve the transport equation for cosmic rays, the transport equation for magnetic turbulence, and the hydro-dynamical equations for the gas flow. Separately computed distributions of the particles accelerated at the forward and the reverse shock are then used to calculate the spectra of synchrotron, inverse Compton, and pion-decay radiation from the source. Results. The emission from G1.9+0.3 can be self-consistently explained within the test-particle limit. We find that the X-ray flux is dominated by emission from the forward shock while most of the radio emission originates near the reverse shock, which makes G1.9+0.3 the first remnant with non-thermal radiation detected from the reverse shock. The flux of very-high-energy gamma-ray emission from G1.9+0.3 is expected to be close to the sensitivity threshold of the Cherenkov Telescope Array, CTA. The limited time available to grow large-scale turbulence limits the maximum energy of particles to values below 100 TeV, hence G1.9+0.3 is not a PeVatron.
ABSTRACT
We present new radio continuum images and a source catalogue from the MeerKAT survey in the direction of the Small Magellanic Cloud. The observations, at a central frequency of 1.3 GHz ...across a bandwidth of 0.8 GHz, encompass a field of view ∼7° × 7° and result in images with resolution of 8 arcsec. The median broad-band Stokes I image Root Mean Squared noise value is ∼11 μJy beam−1. The catalogue produced from these images contains 108 330 point sources and 517 compact extended sources. We also describe a UHF (544–1088 MHz) single pointing observation. We report the detection of a new confirmed Supernova Remnant (SNR; MCSNR J0100–7211) with an X-ray magnetar at its centre and 10 new SNR candidates. This is in addition to the detection of 21 previously confirmed SNRs and two previously noted SNR candidates. Our new SNR candidates have typical surface brightness an order of magnitude below those previously known, and on the whole they are larger. The high sensitivity of the MeerKAT survey also enabled us to detect the bright end of the SMC Planetary Nebulae (PNe) sample – point-like radio emission is associated with 38 of 102 optically known PNe, of which 19 are new detections. Lastly, we present the detection of three foreground radio stars amidst 11 circularly polarized sources, and a few examples of morphologically interesting background radio galaxies from which the radio ring galaxy ESO 029–G034 may represent a new type of radio object.
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