MeerKAT

Abstract:

Relativistic jets from supermassive black holes in active galactic nuclei are amongst the most powerful phenomena in the universe. Similar jets from stellar-mass black holes offer a chance to study the phenomena on accessible observation time scales. However, such comparative studies across black hole masses and time scales remain hampered by the long-standing perception that stellar-mass black hole jets are in a less relativistic regime. Here, we show the detection of two distinct, relativistic jet ejections from the Galactic black hole X-ray binary 4U 1543−47 during a single outburst, with radio interferometry monitoring observations. Our measurements reveal a likely Lorentz factor of approximately 8 and a minimum of 4.6 at launch with 95% confidence, demonstrating that stellar-mass black holes in X-ray binaries can launch jets as relativistic as those seen in active galactic nuclei.

Abstract:

Understanding the growth of supermassive black holes (SMBHs) requires observational constraints on how their angular momentum—or spin—varies with mass, since the relative importance of coherent accretion, chaotic accretion, and mergers will be reflected in SMBH spin populations. Here we present an updated compilation of reflection-based SMBH spin measurements from the literature and assemble a set of ancillary quantities of interest for each SMBH (including redshift, Eddington ratio, and X-ray luminosity). No obvious apparent correlation between the Eddington-scaled accretion rate and the black hole spin is seen, noting that formal statistical tests are beyond the scope of this review. We discuss the limitations of using this heterogeneous mass–spin sample to test predictions of SMBH growth from semi-analytic models and cosmological simulations, emphasizing the need for a more uniform sample. We then highlight the encouraging prospects enabled by the next-generation NewAthena X-ray flagship observatory. Finally, we summarize how hierarchical Bayesian population inference applied to observed SMBH mass–spin populations will constitute a powerful framework for confirming tentative mass–spin trends in future samples.

Abstract:

Type Ia supernova (SN Ia) standardization parameters exhibit evidence for systematic variation across the host galaxy star formation rate–stellar mass (SFR) plane, motivating the incorporation of galaxy SFR information in cosmological inference. SFRs are commonly estimated via spectral energy distribution (SED) fitting with far-infrared (FIR) measurements to account for dust-obscured star formation. Such FIR coverage will, however, be limited for upcoming time-domain surveys such as the Rubin Observatory Legacy Survey of Space and Time (LSST), necessitating the use of alternative SFR tracers. Here, we reconstruct the SFR– plane using 1.4 GHz diagnostics, to test the consistency of host classifications against FIR-constrained SED-based estimates. Within this plane, SN Ia host galaxies are divided into three regions: Region 1 (low mass), Region 2 (high-mass star forming), and Region 3 (high-mass passive). We find that per cent of SN hosts retain identical region assignments when using radio versus FIR-constrained SED-derived SFRs. Measuring SN Ia nuisance parameters () within each subregion, we find consistent values between the two SFR– plane reconstructions, indicating limited sensitivity to SFR estimator choice, with the largest deviations in Region 3 at . Across the three 1.4 GHz SFR– subregions, we confirm the region-dependent variation in SN Ia standardization parameters – particularly – reported in our earlier SED-based analysis. With near-complete radio coverage of the LSST footprint anticipated from current and forthcoming radio continuum surveys (e.g. Square Kilometre Array), radio SFR calibrations will become an increasingly useful and scalable approach to host galaxy classification, supporting the construction of robust SN Ia subsamples for precision cosmology.

Abstract:

We conduct a search for strong gravitational lenses in the Dark Energy Survey (DES) Year 6 imaging data. We implement a pre-trained Vision Transformer (ViT) for our machine learning (ML) architecture and adopt interactive machine learning to construct a training sample with multiple classes to address common types of false positives. Our ML model reduces ∼236 million DES cutout images to 22,564 targets of interest, including ∼85% of previously reported galaxy–galaxy lens candidates discovered in DES. These targets were visually inspected by citizen scientists, who ruled out ∼90% as false positives. Of the remaining 2618 candidates, 149 were expert-classified as “definite” lenses and 516 as “probable” lenses, for a total of 665 systems, with 147 of these candidates being newly identified. Additionally, we trained a second ViT to find double-source plane lens systems, finding at least one double-source system. Our main ViT excels at identifying galaxy–galaxy lenses, consistently assigning high scores to candidates with high expert assessments. The top 800 ViT-scored images include ∼100 of our “definite” lens candidates. This selection is an order of magnitude higher in purity than previous convolutional neural-network-based lens searches and demonstrates the feasibility of applying our methodology for discovering large samples of lenses in future surveys.