Hintze Centre for Astrophysical Surveys

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:

Abstract The interplay between atomic gas and the star-formation history 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 star formation histories (SFHs) of 203 galaxies from the MIGHTEE-Hi Survey Early Science Release data, crossmatched to with multi-wavelength photometry across the COSMOS and XMM-LSS fields. We focus on the relationships between Hi 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 Hi-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 Hi-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 Hi-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 Hi into stars.

Abstract:

Abstract Spectroscopic observations of the kilonova AT 2017gfo provide a unique opportunity to identify signatures from individual heavy elements freshly synthesised 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 ($X_{\rm {\small IN}}$) than that proposed by previous studies. Specifically, our model necessitates $X_{\rm {\small IN}} \approx 2.5 \times 10^{-3}$ in the line-forming region, a value 20 × lower than previously claimed. This substantial reduction in $X_{\rm {\small IN}}$ 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.

Abstract:

Abstract A central question regarding neutron star mergers is whether they are able to produce all the r-process elements, from first to third peak. We here study theoretical infrared signatures of first-peak elements with spectral synthesis modelling. By combining state-of-the-art NLTE physics with new radiative and collisional data for these elements, we identify several promising diagnostic lines from Ge, As, Se, Br, Kr and Zr. The models give self-consistent line luminosities and indicate specific features that probe emission volumes at early phases (∼10d), the product of ion mass and electron density in late phases (≳75d), and in some cases direct ionic masses at intermediate phases. Emission by [Se I] 5.03 μm + [Se III] 4.55 μm is the only one from the first r-process peak that could explain the Spitzer photometry of AT2017gfo. However, the models show consistently that with a Kr/Te and Se/Te ratio following the solar r-process pattern, Kr + Se emission is dominant over Te for the blend at 2.1 μm observed in both AT2017gfo and AT2023vfi. The somewhat better line profile fit with [Te III] may suggest that both AT2017gfo and AT2023vfi had a strongly sub-solar production of the light r-process elements. An alternative scenario could be that Kr + Se in an asymmetric morphological distribution generates the feature. Further JWST spectral observations holds promise to determine the light r-process production of kilonovae, and in particular whether the light elements are made in a slow disk wind or in a fast proto-NS wind. We identify specific needs for further atomic data for Z = 31 − 40 elements.

Abstract:

We introduce a new kinematic disturbance parameter, (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 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 value of a galaxy can be calculated. We then present initial results using 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 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.