Dr Aprajita Verma

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:

Radio continuum emission provides a unique opportunity to study star formation unbiased by dust obscuration. However, if radio observations are to be used to accurately trace star formation to high redshifts, it is crucial that the physical processes that affect the radio emission from star-forming galaxies are well understood. While inverse Compton (IC) losses from the cosmic microwave background (CMB) are negligible in the local universe, the rapid increase in the strength of the CMB energy density with redshift [] means that this effect becomes increasingly important at . Using a sample of high-redshift () Lyman-break galaxies selected in the rest-frame ultraviolet (UV), we have stacked radio observations from the MIGHTEE survey to estimate their 1.4-GHz flux densities. We find that for a given rest-frame UV magnitude, the 1.4-GHz flux density and luminosity decrease with redshift. We compare these results to the theoretical predicted effect of energy losses due to IC scattering off the CMB, and find that the observed decrease is consistent with this explanation. We discuss other possible causes for the observed decrease in radio flux density with redshift at a given UV magnitude, such as a top-heavy initial mass function at high redshift or an evolution of the dust properties, but suggest that IC scattering is the most compelling explanation.

Abstract:

Strong gravitational lensing offers a powerful and direct probe of dark matter, galaxy evolution and cosmology, yet strong lenses are rare: only 1 in roughly 10,000 massive galaxies can lens a background source into multiple images. The European Space Agency’s Euclid telescope, with its unique combination of high-resolution imaging and wide-area sky coverage, is set to transform this field. In its first quick data release, covering just 0.45% of the full survey area, around 500 high-quality strong lens candidates have been identified using a synergy of machine learning, citizen science and expert visual inspection. This dataset includes exotic systems such as compound lenses and edge-on disk lenses, demonstrating Euclid’s capacity to probe the lens parameter space. The machine learning models developed to discover strong lenses in Euclid data are able to find lenses with high purity rates, confirming that the mission’s forecast of discovering over 100,000 strong lenses is achievable during its 6-year mission. This will increase the number of known strong lenses by two orders of magnitude, transforming the science that can be done with strong lensing.

Abstract:

Like many areas of astrophysics and cosmology, the Vera C. Rubin Observatory will be transformational for almost all the applications of strong lensing, thanks to the dramatic increase in the number of known strong lenses by two orders of magnitude or more and the readily available time-domain data for the lenses with transient sources. In this article, we provide an overview of the forecasted number of discovered lenses of different types and describe the primary science cases these large lens samples will enable. We provide an updated forecast on the joint constraint for the dark energy equation-of-state parameters, w0 and wa, from combining all strong-lensing probes of dark energy. We update the previous forecast from the Rubin Observatory Dark Energy Science Collaboration’s Science Review Document by adding two new crucial strong-lensing samples: lensed type Ia supernovae and single-deflector lenses with measured stellar kinematics. Finally, we describe the current and near-future activities and collaborative efforts within the strong-lensing community in preparation for the arrival of the first real dataset from Rubin in 2026. This article is part of the Theo Murphy meeting issue ‘Multi-messenger gravitational lensing (Part 2)’.