Hintze Centre for Astrophysical Surveys

Abstract:

Shock breakout and, in some cases, jet-driven high-energy emission are increasingly recognized as key signatures of the earliest phases of core-collapse supernovae, especially in Type IIn systems due to their dense, interaction-dominated circumstellar environments. We present a comprehensive photometric analysis of SN 2019vxm, a long-duration, luminous Type IIn supernova, MV=−21.41±0.05mag , observed from X-ray to near-infrared. SN 2019vxm is the first superluminous supernovae Type IIn to be caught with well-sampled TESS photometric data on the rise and has a convincing coincident X-ray source at the time of first light. The high-cadence TESS light curve captures the early-time rise, which is well described by a broken power law with an index of n = 1.41 ± 0.04, significantly shallower than the canonical n = 2 behavior. From this, we constrain the time of first light to within 7.2 hr. We identify a spatial and temporal coincidence between SN 2019vxm and the hard X-ray/gamma-ray transient GRB 191117A, corresponding to a 3.3σ association confidence. Both the short-duration X-ray event and the lightcurve modeling are consistent with shock breakout into a dense, asymmetric circumstellar medium, indicative of a massive, compact progenitor such as a luminous blue variable transitioning to Wolf–Rayet phase embedded in a clumpy, asymmetric environment.

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:

Context. Ultra-compact massive galaxies (UCMGs) often exhibit elevated stellar-to-dynamical mass ratios when dynamical masses are estimated using standard virial prescriptions. This discrepancy has been interpreted as evidence for structural non-homology driven primarily by their compactness. Aims. This study investigates how the stellar-to-dynamical mass ratio depends on compactness (C), internal kinematics (σ*), stellar population properties (mass-weighted age, metallicity, and [Mg/Fe]), and star formation histories. The analysis is based on a homogeneous catalogue of 482 UCMGs from the INSPIRE and E-INSPIRE surveys, extending to significantly smaller sizes than previously analysed samples. Methods. I first derive the compactness–mass relation assuming a constant virial coefficient (K = 5). I then correct stellar masses for initial mass function (IMF) variations and recompute stellar-to-dynamical mass ratios using an empirical prescription in which the virial coefficient varies as a function of radius and stellar mass. Finally, I test whether the relation is modulated by stellar kinematics and population properties, including the degree of relicness (DoR), which quantifies the extremeness of the star formation history. Results. A statistically significant anti-correlation between compactness and the IMF-corrected stellar-to-dynamical mass ratio is recovered when a constant virial coefficient is adopted, even within the relatively narrow range of C spanned by nearby UCMGs. The relation substantially flattens when a structure-dependent K is adopted, in agreement with previous literature. Beyond this one-dimensional behaviour, the data define a structural–dynamical manifold in the (logC,Clog σ*) space. Velocity dispersion sets the dominant axis of variation, and the corresponding plane accounts for ∼62% of the variance in stellar-to-dynamical mass ratio. Including stellar age increases the explained variance to ∼63%, revealing a secondary evolutionary modulation. In contrast, DoR, metallicity, and [Mg/Fe] do not retain independent explanatory power once stellar age is included. Conclusions. The stellar-to-dynamical mass ratio in UCMGs is governed primarily by the depth of the gravitational potential, traced by stellar velocity dispersion, rather than by compactness alone. At fixed size, systems with higher σ* exhibit systematically lower stellar-to-dynamical mass ratio, indicating that dynamical structure regulates the apparent mass imbalance in the ultra-compact regime. Compactness largely reflects this dynamical scaling, while stellar age introduces a coherent secondary modulation linking the structural manifold to the evolutionary state of the galaxy. Non-homology in UCMGs therefore encodes coupled dynamical and assembly processes rather than purely geometric compactness.

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.