Hintze Centre for Astrophysical Surveys

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.

Abstract:

The interplay between atomic gas and the star formation history (SFH) of a galaxy are intrinsically linked, and we need to decouple these dependencies to understand their role in galaxy formation and evolution. In this paper, we analyse the SFH of 203 galaxies from the MIGHTEE-H i Survey Early Science Release data, cross-matched to with multiwavelength photometry across the COSMOS and XMM-LSS fields. We focus on the relationships between H i properties and star formation, with a sample which primarily traces gas-rich, star-forming systems at low redshift, extending to low stellar masses and probing regimes that are difficult to access with optically selected samples. A strong correlation emerges between a galaxy’s H i-to-stellar mass ratio and the time of formation, alongside an inverse correlation between stellar mass and time of formation, regardless of the inferred SFH. Additionally, galaxies with lower stellar masses and higher H i-to-stellar mass ratios exhibit longer gas depletion times compared to more massive galaxies, which appear to have depleted their gas and formed stars more efficiently. This suggests that smaller, gas-rich galaxies have higher depletion times due to shallower potential wells and less efficient star formation. Within this H i-selected sample, the efficiency of star formation is regulated primarily by stellar mass and gas fraction, with low-mass galaxies retaining extended atomic reservoirs due to inefficient conversion of H i into stars.

Abstract:

Spectroscopic observations of the kilonova AT 2017gfo provide a unique opportunity to identify signatures from individual heavy elements freshly synthesized via the r-process, the nucleosynthetic channel responsible for producing half of all trans-iron-group elements. Limitations in the available atomic data have historically hampered comprehensive line identification studies; however, renewed interest has led to the generation of improved (more complete and accurately calibrated) line lists for r-process species. Here we demonstrate the utility of such data, by exploiting newly generated line lists for the lanthanides to model the photospheric-phase 3.4 d X-shooter spectrum of AT 2017gfo with the radiative transfer tool tardis. We find the data can only be reproduced by invoking a substantially diminished lanthanide mass fraction () than that proposed by previous studies. Specifically, our model necessitates in the line-forming region, a value lower than previously claimed. This substantial reduction in is driven by our inclusion of much more complete lanthanide line information that enables better estimation of their total contribution to the observations. We encourage future modelling works to exploit all atomic data advances, and also encourage continued efforts to generate the necessary data for the remaining r-process species of interest.