MeerKAT

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 Disc winds from active galactic nuclei (AGN) can be launched by radiation pressure acting on spectral lines. However, launching a line-driven wind in the X-ray rich environment of AGN is challenging, as the wind easily gets over-ionized. Previous simulations suggested that X-ray self-shielding could enable line driving, though it remained unclear whether this relied on simplified treatments of radiation and ionization. Here, we revisit the X-ray shielding scenario using the first multi-frequency, multi-directional Monte-Carlo radiative photo-ionization hydrodynamical simulations of AGN line-driven winds. We find that sustaining a steady wind with mass-loss rates of ≈20% of the accretion rate requires an unrealistically weak X-ray flux (αOX < −3). For stronger X-ray emission (−3 < αOX < −1), self-shielding is only transient, leading to episodic ejections with mass-loss rates approaching the accretion rate. Our steady winds naturally produce FeLoBAL, HiBAL, and broad emission line signatures, depending on the disc spectral energy distribution and the observer’s inclination. At moderate X-ray luminosities (αOX ∼ −3), transient winds can generate short-lived BAL and ultra-fast outflow (UFO) features. At the highest X-ray luminosities (αOX ∼ −1), the winds are too ionized to form BALs, but still produce UFOs. These results imply that additional physics is required to explain BAL outflows at realistic X-ray levels and to drive winds strong enough for AGN feedback. Nonetheless, our simulations provide a new framework for interpreting the observed diversity of AGN outflow signatures with fully coupled radiation and dynamics.

Abstract:

The upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will enable astronomers to discover rare and distant astrophysical transients. Host-galaxy association is crucial for selecting the most scientifically interesting transients for follow-up. LSST deep drilling field (DDF) observations will detect distant transients occurring in galaxies below the detection limits of most all-sky catalogs. Here, we investigate the use of preexisting, field-specific catalogs for host identification in the DDFs and a ranking of their usefulness. We have compiled a database of 70 deep catalogs that overlap with the Rubin DDFs and constructed thin catalogs to be homogenized and combined for transient-host matching. A systematic ranking of their utility is discussed and applied based on the inclusion of information such as spectroscopic redshifts and morphological information. Utilizing this data against a Dark Energy Survey sample of supernovae with pre-identified hosts in the XMM-Large Scale Structure and the Extended Chandra Deep Field-South fields, we evaluate different methods for transient-host association in terms of both accuracy and processing speed. We also apply light data-cleaning techniques to identify and remove contaminants within our associations, such as diffraction spikes and blended galaxies where the correct host cannot be determined with confidence. We use a lightweight machine learning approach in the form of extreme gradient boosting to generate confidence scores in our contaminant selections and associated metrics. Finally, we discuss the computational expense of implementation within the LSST transient alert brokers, which will require efficient, fast-paced processing to handle the large stream of survey data.

Abstract:

Abstract A key question in extragalactic astronomy is how the star-formation rate density (SFRD) evolves over cosmic time. A powerful way of addressing this question is using radio-continuum observations, where the radio waves are unaffected by dust and are able to reach sufficient resolution to resolve individual galaxies. We present an investigation of the 1.4 GHz radio luminosity functions (RLFs) of star-forming galaxies (SFGs) and Active Galactic Nuclei (AGN) using deep radio continuum observations in the COSMOS and XMM–LSS fields, covering a combined area of ∼4 deg2. These data enable the most accurate measurement of the evolution in the SFRD from mid-frequency radio continuum observations. We model the total RLF as the sum of evolving SFG and AGN components, negating the need for individual source classification. We find that the SFGs have systematically higher space densities at fixed luminosity than found in previous radio studies, but consistent with more recent studies with MeerKAT. We attribute this to the excellent low-surface brightness sensitivity of MeerKAT. We then determine the evolution of the SFRD. Adopting the far-infrared – radio correlation results in a significantly higher SFRD at z > 1, compared to combined UV and far-infrared measurements. However, using more recent relations for the correlation between star-formation rate and radio luminosity, based on full spectral energy distribution modelling, can resolve this apparent discrepancy. Thus radio observations provide a powerful method of determining the total SFRD, in the absence of dust-sensitive far-infrared data.