Extremely Large Telescope

Abstract:

We present a blind time-delay cosmography measurement of the Hubble-Lemaître constant H0 based on the quadruply imaged quasar SDSSJ1433 + 6007. Our analysis combines deep Hubble Space Telescope imaging, extended time-delay monitoring from the Wendelstein and Maidanak Observatories, and spatially resolved stellar kinematics from the Keck Cosmic Web Imager and Reionization Mapper. We build a robust lens model to reconstruct the mass distribution and high-signal-to-noise kinematic maps to break the mass-sheet degeneracy, explicitly accounting for the lens galaxy’s oblateness, rotation, and anisotropy. Furthermore, we constrain the external convergence (κext) by characterizing the line-of-sight environment using wide-field photometry from the Dark Energy Spectroscopic Instrument (DESI) Legacy Survey data release 10. We incorporate these constraints into our joint lensing and dynamical model, running multiple iterations to estimate random and systematic uncertainties. Accounting for maximal flexibility of the mass-sheet transformation, and assuming a flat Λ cold dark matter cosmology and an Ωm,0 prior from DESI data release 2, we infer H0=73.2-4.7+4.8 km s-1 Mpc-1 (a 6.5% precision) and an internal mass-sheet parameter λint=1.12-0.06+0.05. Notably, λint is 2σ away from unity for this system, highlighting the importance of treating it as a free parameter. Our H0 measurement is consistent with the result from our 2025 milestone paper, and it will be included in our next hierarchical analysis to improve the overall precision. Moving forward, the comprehensive pipeline demonstrated herein establishes a robust framework that can be readily applied to future strongly lensed systems to further refine cosmological constraints.

Abstract:

Abstract Strong gravitational lenses (SGLs) are rare systems whose discovery currently relies primarily on supervised machine learning methods trained on large simulated datasets. We present the first application of Astronomaly:PROTEGE to SGL discovery, demonstrating that a human-in-the-loop active learning framework can efficiently identify lenses in large imaging surveys without the need for simulated training data. We consider a sample of 3.7 million bright galaxies from the Kilo-Degree Survey (KiDS) DR4. Feature representations are extracted using a convolutional neural network pre-trained on the ImageNet dataset and subsequently fine-tuned on KiDS data using the self-supervised Bootstrap Your Own Latent (BYOL) framework. Within the embedding of these representations, the active learning loop of Astronomaly iteratively selects the most informative systems for expert inspection. A total of 3,000 objects are inspected across multiple rounds, yielding 34 high-quality (grade A/B) SGL candidates. On the basis that these systems occupy similar regions in the learned feature space, we expand this sample through nearest-neighbour similarity analysis. Including the active learning discoveries, we identify a total of 140 grade A/B candidates and more than 1,000 additional lower-confidence systems (grade C). Among the A/B candidates, 81 are newly identified, while ∼22% of previously known grade A/B KiDS lenses are recovered. These results demonstrate strong potential for next-generation surveys such as Euclid, Roman, and Rubin’s Legacy Survey of Space and Time. With approximately 60% of the high-quality candidates newly reported, this approach complements supervised methods by reducing reliance on simulations and enabling the discovery of a diverse population of SGLs.