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Abstract:

Tracking the correlation between radio and X-ray luminosities during black hole X-ray binary outbursts is a key diagnostic of the coupling between accretion inflows (traced by X-rays) and relativistic jet outflows (traced by radio). We present the radio–X-ray correlation of the black hole low-mass X-ray binary Swift J1727.8–1613 during its 2023–2024 outburst. Our observations span a broad dynamic range, covering 4 orders of magnitude in radio luminosity and 6.5 in X-ray luminosity. This source follows an unusually radio-quiet track, exhibiting significantly lower radio luminosities at a given X-ray luminosity than both the standard (radio-loud) track and most previously known radio-quiet systems. Across most of the considered distance range (–4.3 kpc), Swift J1727.8–1613 appears to be the most radio-quiet black hole binary identified to date. For distances kpc, while Swift J1727 becomes comparable to one other extremely radio-quiet system, its peak X-ray luminosity ( erg s) exceeds that of any previously reported hard-state black hole low-mass X-ray binary, emphasizing the extremity of this outburst. Additionally, for the first time in a radio-quiet system, we identify the onset of X-ray spectral softening to coincide with a change in trajectory through the radio–X-ray plane. We assess several proposed explanations for radio-quiet behaviour in black hole systems in light of this data set. As with other such sources, however, no single mechanism fully accounts for the observed properties, highlighting the importance of regular monitoring and the value of comprehensive (quasi-)simultaneous data-sets.

Abstract:

We present the discovery of a correlation, in a sample of 16 gamma-ray burst 8.5 GHz radio afterglows, between the intrinsic luminosity measured at 10 d in the rest frame, , and the average rate of decay past this time, . The correlation has a Spearman’s rank coefficient of at a significance of and a linear regression fit of . This finding suggests that more luminous radio afterglows have higher average rates of decay than less luminous ones. We use a Monte Carlo simulation to show the correlation is not produced by chance or selection effects at a confidence level of . Previous studies found this relation in optical/UV, X-ray, and GeV afterglow light curves, and we have now extended it to radio light curves. The Spearman’s rank coefficients and the linear regression slopes for the correlation in each waveband are all consistent within . We discuss how these new results in the radio band 91̽�� the effects of observer viewing geometry, and time-varying microphysical parameters, as possible causes of the correlation as suggested in previous works.

Abstract:

Studying the radio spectral energy distribution (SED) of distant galaxies is essential for understanding their assembly and evolution over cosmic time. We present rest-frame radio SEDs of a sample of 160 star-forming galaxies at 1.5 < z < 3.5 in the Cosmic Evolution Survey field as part of the MeerKAT International GHz Tiered Extragalactic Exploration project. MeerKAT observations combined with archival Very Large Array and Giant Metrewave Radio Telescope data allow us to determine the integrated mid-radio (ν = 1–10 GHz) continuum (MRC) luminosity and magnetic field strength. A Bayesian method is used to model the SEDs and to separate the free–free and synchrotron emission. We also calibrate the star formation rate (SFR) in radio both directly through SED analysis and indirectly through the infrared–radio correlation (IRRC). With a mean value of αnt ≃ 0.7, the synchrotron spectral index flattens with both redshift and specific SFR, indicating that cosmic rays are more energetic in the early Universe due to higher star formation activity. The magnetic field strength increases with redshift, B ∝ (1 + z)(0.7±0.1), and SFR as B ∝ SFR0.3, suggesting a small-scale dynamo acting as its main amplification mechanism. Taking into account the evolution of the SEDs, the IRRC is redshift invariant, and it does not change with stellar mass at 1.5 < z < 3.5, although the correlation deviates from linearity. Similarly, we show that the SFR traced using the integrated MRC luminosity is redshift invariant.

Abstract:

Photonic Doppler velocimetry (PDV) is an established technique for measuring the velocities of fast-moving surfaces in high-energy-density experiments. In the standard approach to PDV analysis, the short-time Fourier transform (STFT) is used to generate a spectrogram from which the velocity history of the target is inferred. The user chooses the form, duration, and separation of the window function. Here, we present a Bayesian approach to infer the velocity directly from the PDV oscilloscope trace, without using the spectrogram for analysis. This is clearly a difficult inference problem due to the highly periodic nature of the data, but we find that with carefully chosen prior distributions for the model parameters, we can accurately recover the injected velocity from synthetic data. We validate this method using PDV data collected at the STAR two-stage light gas gun at Sandia National Laboratories, recovering shock-front velocities in quartz that are consistent with those inferred using the STFT-based approach and are interpolated across regions of low signal-to-noise data. Although this method does not rely on the same user choices as the STFT, we caution that it can be prone to misspecification if the chosen model is not sufficient to capture the velocity behavior. Analysis using posterior predictive checks can be used to establish whether a better model is required, although more complex models come with additional computational cost, often taking more than several hours to converge when sampling the Bayesian posterior. We, therefore, recommend it be viewed as a complementary method to that of the STFT-based approach.