M. Fogliardi

We present a strong-lensing analysis of the galaxy cluster MACS J0138.0−2155 ( z _l = 0.336), the first known cluster lens discovered to host two distinct multiply imaged type Ia supernovae (SNe): SN Requiem and SN Encore. Both transients are located in the massive, multiply imaged red galaxy MRG-M0138 at z _s = 1.949. The projected total mass distribution of this cluster has been investigated with several independent lens models, using a sample of 23 spectroscopically confirmed multiple images from eight background sources spanning 0.767 < z < 3.420, identified from Hubble Space Telescope and James Webb Space Telescope imaging, and VLT/MUSE spectroscopy. For this study, we developed a new lens model with the Bayesian parametric lens-modelling framework G RAVITY . JL , using the same strong-lensing dataset. Under a fiducial flat Lambda Cold Dark Matter cosmology, our reference mass model accurately reproduces the observed image positions, with an image-plane root-mean-square positional residual of Δ _rms = 0.15″. Assuming H ₀ = 70 km s ⁻¹ Mpc ⁻¹ , we predicted the future reappearances of highly delayed SN counter images, finding Δ t _1d,1a = 3122 ₋₇₀ ⁺⁷⁵ days (from approximately April–August 2032) for SN Encore and Δ t _2d,2a = 3122 ₋₇₂ ⁺⁶⁷ days (from approximately January–May 2027) for SN Requiem. By allowing the Hubble constant to vary and including the measured relative time delays of both SN Encore and SN Requiem, together with their statistical uncertainties, as observables, we inferred the value of H ₀ jointly with the lens-model parameters. We performed the cosmographic analysis with three alternative parametrisations of the large-scale cluster halo and combined the resulting H ₀ posteriors through Bayesian model averaging, using the corresponding Bayesian evidences as model weights. The resulting model-averaged constraint is H ₀ = 69.2 _−8.0 ^+9.6 km s ⁻¹ Mpc ⁻¹ , consistent with the value inferred from independent lens models of this cluster. The current uncertainty remains dominated by the relatively large fractional uncertainties of the measured SN time delays (≳10%), while the scatter among the mass parametrisations considered here is subdominant. The forthcoming long-baseline reappearance of SN Requiem offers an immediate opportunity to improve the statistical precision on the Hubble constant, provided that lens-model systematics are sufficiently controlled. Together, these results establish MACS J0138.0−2155 as a premier anchor for high-precision cluster-scale time-delay cosmography with multiply imaged SNe.

We present the first catalogue of strong-lensing galaxy clusters identified in the Euclid Quick Release 1 observations (covering 63.1 deg ² ). This catalogue is the result of the visual inspection of 1260 Euclid image cutouts of previously identified galaxy clusters. Each galaxy cluster was assigned a lensing score, 𝒫 _lens , derived from the aggregated votes of multiple visual inspectors, the majority of whom possess extensive expertise in strong gravitational lensing. Votes were assigned based on the assessed credibility and plausibility of the identified strong-lensing features. Specifically, we identified 83 gravitational lenses with 𝒫 _lens > 0.5, of which 14 have 𝒫 _lens = 1 and clearly exhibit secure strong-lensing features, such as giant tangential and radial arcs, and multiple images. Considering the measured number density of lensing galaxy clusters, approximately 0.3 deg ⁻² for 𝒫 _lens > 0.9, we predict that Euclid will likely observe more than 4500 strong-lensing clusters over the course of the mission. Notably, only three of the identified cluster-scale lenses had previously been observed from space. Thus, Euclid has provided the first high-resolution imaging for the remaining 80 strong-lensing galaxy cluster candidates, including those with the highest scores. The identified strong-lensing features will be used to train deep-learning models to automatically identify gravitational arcs and multiple images in the Euclid observations. This study confirms the great potential of Euclid for finding new strong-lensing clusters, enabling exciting new discoveries on the nature of dark matter and dark energy and the study of the high-redshift Universe.

Strong gravitational lensing (SL) by galaxy clusters is a powerful probe of their inner mass distribution and a key test bed for cosmological models. However, the detection of SL events in wide-field surveys such as Euclid requires robust automated methods capable of handling the immense data volume generated. In this work, we present an advanced deep learning (DL) framework based on mask region-based convolutional neural networks (Mask R-CNNs) designed to autonomously detect and segment bright strongly lensed arcs in Euclid's multi-band imaging of galaxy clusters. The model was trained on a realistic simulated dataset of cluster-scale SL events constructed by injecting mock background sources into Euclidised Space Telescope images of ten massive lensing clusters, exploiting their high-precision mass models constructed with extensive spectroscopic data. The network was trained and validated on over 4500 simulated images and tested on an independent set of 500 simulations as well as on as real Euclid Quick Data Release (Q1) observations. The trained network achieves a high performance in identifying gravitational arcs in the test set, with a precision and recall of 76% and 58%, respectively, processing 2' , is open source and is available at . Hubble 2' images in a fraction of a second. When applied to a sample of visually confirmed Euclid Q1 cluster-scale lenses, our model recovers ≈ 66% of the gravitational arcs above the area threshold used during training. While the model shows promising results, limitations include the production of some false positives and challenges in detecting smaller, fainter arcs. Our results demonstrate the potential of advanced DL computer vision techniques for efficient and scalable arc detection in the effort towards enabling automated analysis of SL systems in current and future wide-field surveys. The code, ARTEMIDE https://github.com/LBasz/ARTEMIDE