Rubin-LSST

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:

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:

Type Ia supernova (SN Ia) standardisation parameters exhibit evidence for systematic variation across the host galaxy star-formation rate–stellar mass (SFR−M⋆) 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–M⋆ 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 ∼84 per cent of SN hosts retain identical region assignments when using radio versus FIR-constrained SED-derived SFRs. Measuring SN Ia nuisance parameters (α, β, M) within each subregion, we find consistent values between the two SFR–M⋆ plane reconstructions, indicating limited sensitivity to SFR estimator choice, with the largest deviations in Region 3 at ∼1.1σ. Across the three 1.4 GHz SFR–M⋆ subregions, we confirm the region-dependent variation in SN Ia standardisation parameters–particularly β–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 introduce a new kinematic disturbance parameter, (Formula presented) (pronounced ‘DVSG’), which takes advantage of integral field spectroscopy (IFS) to quantify differences between a galaxy’s stellar and gas velocity maps. The motivation behind (Formula presented) is to capture disturbances in the kinematics of a galaxy that might be missed by alternative methods, while also attempting to minimize bias towards galaxy properties or features of the IFS data. We first detail the reasons for introducing this parameter and explain how the (Formula presented) value of a galaxy can be calculated. We then present initial results using (Formula presented) to quantify the kinematic disturbance of obscured active galactic nuclei (AGNs) found in the MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) survey. We find that there is no statistically significant difference between the (Formula presented) distributions of AGN and a control sample (matched in mass and redshift) of inactive galaxies. This suggests that AGN triggering may not be preferentially caused by any distinct kinematic disturbance process, or combination of processes, beyond those observed in inactive galaxies.

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.