Raoul Cañameras

Strong gravitational lensing by massive galaxy clusters offers rare opportunities to observe multiple images of distant ( z ≳ 2) Type Ia supernovae (SNe) and to resolve the properties of their host galaxies. A recent outstanding example is the Type Ia SN H0pe ( z = 1.78), which the James Webb Space Telescope (JWST) discovered in NIRCam images, when the galaxy cluster PLCK G165.7+67.0 (G165, z = 0.35) still produced three images of it. In this work, we build a new strong lensing model of G165, first using only the positions of multiple images of background galaxies. We then significantly increase the number of constraints around the position of SN H0pe by modeling the extended surface brightness of the SN host galaxy. Including extended image information reduces the average uncertainty on mass model parameters by more than an order of magnitude. We also study the spatial distribution of dust in the arc to estimate the dust extinction at the position of SN H0pe. We find good statistical agreement of the extinction estimate, at ≲1 σ , with three fully independent methods based on spectral energy distribution fitting. Moreover, our extended-image lens model of G165 allows us to map the dust distribution of the host galaxy from the image plane to the source plane. Supernova H0pe exploded in a region with a relatively high extinction ( A _V ≈ 0.9 mag) at around ∼1 kpc from its host center. This work shows that extended image modeling in lensing clusters simultaneously reduces the uncertainty on lens model parameters and enables spatially resolved analyses of lensed transients’ host galaxies. Such modeling advances are expected to play an important role in future cosmological analyses using strongly lensed SNe.

Zoom sur la formation stellaire au sein des galaxies les plus brillantes de l'univers jeune découvertes avec les satellites Planck et Herschel: Les galaxies amplifiées par lentillage gravitationnel fort offrent une opportunité exceptionnelle pour caractériser la formation stellaire intense au sein des galaxies poussiéreuses les plus distantes. Dans les cas les plus favorables, il est possible d'étudier les mécanismes qui régissent la formation stellaire jusqu'aux échelles des régions de formation d'étoiles individuelles. Les alignements fortuits entre ces galaxies actives et des structures d'avant-plan produisant des facteurs d'amplification par lentillage gravitationnel >> 10 restent néanmoins très rares. L'échantillon des Planck's Dusty GEMS (Gravitationally Enhanced subMillimeter Sources), découvert par le relevé de ciel complet du satellite Planck dans le domaine sub-millimétrique, contient onze galaxies à haut décalage spectral extrêmement brillantes. Leurs densités de flux à 350 microns se situent entre 300 et 1000 mJy, au-delà de la plupart des sources lentillées précédemment découvertes par les relevés en infrarouge lointain et sub-millimétrique. Six d'entre elles dépassent la limite en complétude à 90% du catalogue de sources ponctuelles détectées par Planck (PCCS), indiquant qu'elles sont parmi les plus brillantes sources lointaines sélectionnées par leur formation stellaire intense. Cette thèse s'intègre dans le suivi multi-longueur d'onde de cet échantillon exceptionnel, destiné à sonder les propriétés globales des sources d'arrière-plan et à contraindre les configurations de lentillage. Premièrement, j'utilise de l'imagerie et de la spectroscopie en visible et en infrarouge proche et moyen pour caractériser les structures formant la lentille et pour construire des modèles de lentillage gravitationnel complets. J'en déduis que les onze GEMS sont effectivement alignées avec des surdensités de matière en avant-plan, soit des galaxies massives et isolées, soit des groupes ou amas de galaxies. Ces objets amplifiants contiennent des populations d'étoiles évoluées et âgées de plusieurs milliards d'années, indiquant qu'il s'agit de galaxies précoces. De plus, la modélisation détaillée de l'effet de lentillage vers les GEMS suggère que les amplifications atteignent systématiquement des facteurs > 10, et > 20 pour certaines lignes de visée. Deuxièmement, nous observons dans les domaines infrarouge lointain et millimétrique pour caractériser les sources d'arrière-plan. Les données en interférométrie de l'IRAM et du SMA à des résolutions inférieures à la seconde d'arc montrent que les GEMS ont des morphologies très déformées, preuve de fortes distorsions gravitationnelles. J'obtiens des températures de poussières de 33 à 50 K et des luminosités atteignant 2x10^14 luminosités solaires en infrarouge lointain, sans corriger du facteur d'amplification. La relation entre températures de poussières et luminosités infrarouge confirme également que, pour une température donnée, les GEMS sont plus brillantes que les galaxies similaires non lentillées. Je conclus qu'à ces longueurs d'onde, le chauffage des poussières semble être dominé par l'activité de formation stellaire avec une contamination par d'éventuels noyaux actifs systématiquement inférieure à 30%. Nous trouvons des décalages vers le rouge compris entre 2.2 et 3.6 grâce à la détection d'au moins deux raies d'émission du gaz atomique ou moléculaire par source. Finalement, je cible les trois sources lentillées de l'échantillon ayant les propriétés les plus remarquables. En particulier, la plus brillante d'entre elles s'avère être un sursaut présentant des densités de formation stellaire proches de la limite d'Eddington, et permet de sonder la naissance des étoiles dans ses phases les plus extrêmes.

Strong gravitationally lensed supernovae (LSNe), though rare, are exceptionally valuable probes for cosmology and astrophysics. Upcoming time-domain surveys such as the Vera Rubin Observatory’s Legacy Survey of Space and Time (LSST) offer a major opportunity to discover large number of LSNe. Early identification is crucial for timely follow-up observations. We have developed a deep learning pipeline to detect LSNe using multiband, multi-epoch image cutouts. Our model is based on a 2D convolutional long short-term memory ( ConvLSTM2D ) architecture designed to capture both spatial and temporal correlations in time-series imaging data. Predictions are made after each observation in the time series, with accuracy expected to improve progressively as additional data are processed. We trained the model on realistic simulations derived from Hyper Suprime-Cam (HSC) data, which closely matches LSST in depth and filter characteristics. In this work, we focus exclusively on Type Ia supernovae (SNe Ia). LSNe Ia were injected into HSC luminous red galaxies (LRGs) at various phases of evolution to create positive examples of LSNe Ia time series. Negative examples include variable sources observed in the HSC Transient Survey (including unclassified transients) and simulated unlensed SNe Ia in LRG and spiral galaxies. Our multiband model shows rapid classification performance improvements during the initial few observations and quickly reaches a high detection efficiency: At a fixed false-positive rate (FPR) of 0.01%, the true-positive rate (TPR) reaches ≳60% by the seventh observation and exceeds ≳70% by the ninth observation. If we relax the FPR to 0.1%, the TPR reaches close to 60% as early as the fourth observation. Although the single-band analysis performs reasonably well in isolation, the multiband model significantly outperforms it, particularly in the early stages, by building a richer memory and leveraging color information. Among the negative examples, SNe in LRGs remain the primary source of FPR, as they can resemble their lensed counterparts under certain conditions. Additionally, the model detects quads more effectively than doubles, and it performs better on systems with larger image separations. Although we trained and tested the model on HSC-like data, our approach is applicable to any cadenced imaging survey – particularly LSST, where the higher expected cadence (five to ten times that of HSC) should further boost performance.

We present imaging and spectroscopic observations of supernova SN 2025wny, associated with the lens candidate PS1 J0716+3821. Photometric monitoring from the Lulin and Maidanak observatories confirms multiple point-like images, consistent with SN 2025wny being strongly lensed by two foreground galaxies. Optical spectroscopy of the brightest image with the Nordic Optical Telescope and the University of Hawaii 88-inch Telescope allows us to determine the redshift to be z_s = 2.008 +- 0.001, based on narrow absorption lines originating in the interstellar medium of the supernova host galaxy. At this redshift, the spectra of SN 2025wny are consistent with those of superluminous supernovae of Type I. We find a high ejecta temperature and depressed spectral lines compared to other similar objects. We also measure, for the first time, the redshift of the fainter of the two lens galaxies (the "perturber") to be z_p = 0.375 +- 0.001, fully consistent with the DESI spectroscopic redshift of the main deflector at z_d = 0.3754. SN 2025wny thus represents the first confirmed galaxy-scale strongly lensed supernova with time delays likely in the range of days to weeks, as judged from the image separations. This makes SN 2025wny suitable for cosmography, offering a promising new system for independent measurements of the Hubble constant. Following a tradition in the field of strongly-lensed SNe, we give SN 2025wny the nickname SN Winny.

In the past, researchers have mostly relied on single-resolution images from individual telescopes to detect gravitational lenses. We present a search for galaxy-scale lenses that, for the first time, combines high-resolution single-band images (in our case from the Hubble Space Telescope, HST) with lower-resolution multiband images (in our case from the Legacy survey, LS) using machine learning. This methodology simulates the operational strategies employed by future missions, such as combining the images of Euclid and the Rubin Observatory's Legacy Survey of Space and Time (LSST). To compensate for the scarcity of lensed galaxy images for network training, we generated mock lenses by superimposing arc features onto HST images, saved the lens parameters, and replicated the lens system in the LS images. We tested four architectures based on ResNet-18: (1) using single-band HST images, (2) using three bands of LS images, (3) stacking these images after interpolating the LS images to HST pixel scale for simultaneous processing, and (4) merging a ResNet branch of HST with a ResNet branch of LS before the fully connected layer. We compared these architecture performances by creating receiver operating characteristic (ROC) curves for each model and comparing their output scores. At a false-positive rate of 10⁻⁴, the true-positive rate is ∼0.41, ∼0.45, ∼0.51 and ∼0.55, for HST, LS, stacked images and merged branches, respectively. Our results demonstrate that models integrating images from both the HST and LS significantly enhance the detection of galaxy-scale lenses compared to models relying on data from a single instrument. These results show the potential benefits of using both Euclid and LSST images, as wide-field imaging surveys are expected to discover approximately 100 000 lenses.

We present a comprehensive spectroscopic analysis of MACS J0138−2155, at z = 0.336. It is the first galaxy cluster known to host two strongly lensed supernovae (SNe), Requiem and Encore, and thus provides us with a chance to obtain a reliable H₀ measurement from the time delays between multiple images. We took advantage of new spectroscopic data from the Multi Unit Spectroscopic Explorer (MUSE) on the Very Large Telescope, complemented with archival imaging from the Hubble Space Telescope. The MUSE data cover a central 1 arcmin² of the lensing cluster, for a total depth of 3.7 hours, including 2.9 hours recently obtained by our Target of Opportunity programme. Based on these observations, we release a new spectroscopic catalogue containing reliable redshifts for 107 objects. This includes 50 galaxy cluster members with secure redshift values in the range 0.324 < z < 0.349, and 13 lensed multiple images from four background sources between 0.767 ≤ z ≤ 3.420, including four images of the host galaxy of the two SNe. We exploited the MUSE data to study the stellar kinematics of 14 bright cluster members and two background galaxies, obtaining reliable measurements of their line-of-sight velocity dispersion. Finally, we combined these results with measurements of the total magnitude of the cluster members in the Hubble Space Telescope F160W band to calibrate the Faber-Jackson relation between luminosity and stellar velocity dispersion (L ∝ σ_v^1/α) for the early-type cluster member galaxies, measuring a slope α = 0.25_−0.05^+0.05 and a scatter of 25₋₄⁺⁶ km s⁻¹ about the relation. We compared the calibrated relation with six other strong lensing clusters in the redshift range 0.31 ≤ z ≤ 0.59, finding a general agreement and no clear sign of evolution with redshift. A pure and complete sample of cluster member galaxies and a reliable characterisation of their total mass structure are key to building accurate total mass maps of the cluster and mitigating the impact of parametric degeneracies, which is necessary for inferring the value of H₀ from the measured time delays between the lensed images of the two SNe.

Gravitationally strongly lensed quasars (SL-QSO) offer invaluable insights into cosmological and astrophysical phenomena. With the data from ongoing and next-generation surveys, thousands of SL-QSO systems can be discovered expectedly, leading to unprecedented opportunities. However, the challenge lies in identifying SL-QSO from enormous data sets with high recall and purity in an automated and efficient manner. Hence, we developed a program based on a convolutional neural network (CNN) for finding SL-QSO from large-scale surveys and applied it to the Kilo-degree Survey Data Release 5. Our approach involves three key stages: first, we preselected 10 million bright objects (with r-band MAG_AUTO < 22), excluding stars from the data set; second, we established realistic training and test sets to train and fine-tune the CNN, resulting in the identification of 4195 machine candidates, and the false-positive rate of ∼1/2000 and recall of 0.8125 evaluated by using the real test set containing 16 confirmed lensed quasars; third, human inspections were performed for further selections, and then, 272 SL-QSO candidates were eventually found in total, including 16 high-score, 118 median-score, and 138 lower-score candidates, separately. Removing the systems already confirmed or identified in other papers, we end up with 229 SL-QSO candidates, including 7 high-score, 95 median-score, and 127 lower-score candidates, and the corresponding catalog is publicly available online (https://github.com/EigenHermit/H24). We have also included an excellent quad candidate in the Appendix, discovered serendipitously during the fine-tuning process of the CNN.

Strongly lensed quasars provide valuable insights into the rate of cosmic expansion, the distribution of dark matter in foreground deflectors, and the characteristics of quasar hosts. However, detecting them in astronomical images is difficult due to the prevalence of non-lensing objects. To address this challenge, we developed a generative deep learning model called VariLens, built upon a physics-informed variational autoencoder. This model seamlessly integrates three essential modules: image reconstruction, object classification, and lens modeling, offering a fast and comprehensive approach to strong lens analysis. VariLens is capable of rapidly determining both (1) the probability that an object is a lens system and (2) key parameters of a singular isothermal ellipsoid (SIE) mass model – including the Einstein radius (θ_E), lens center, and ellipticity – in just milliseconds using a single CPU. A direct comparison of VariLens estimates with traditional lens modeling for 20 known lensed quasars within the Subaru Hyper Suprime-Cam (HSC) footprint shows good agreement, with both results consistent within 2σ for systems with θ_E < 3″. To identify new lensed quasar candidates, we began with an initial sample of approximately 80 million sources, combining HSC data with multiwavelength information from Gaia, UKIRT, VISTA, WISE, eROSITA, and VLA. After applying a photometric preselection aimed at locating z > 1.5 sources, the number of candidates was reduced to 710 966. Subsequently, VariLens highlights 13 831 sources, each showing a high likelihood of being a lens. A visual assessment of these objects results in 42 promising candidates that await spectroscopic confirmation. These results underscore the potential of automated deep learning pipelines to efficiently detect and model strong lenses in large datasets, substantially reducing the need for manual inspection.

We performed a systematic search for strong gravitational lenses using Hyper Suprime-Cam (HSC) imaging data, focusing on galaxy-scale lenses combined with an environment analysis resulting in the identification of lensing clusters. To identify these lens candidates, we exploited our residual neural network from HOLISMOKES VI (Cañameras et al. 2021, A&A, 653, L6), trained on realistic gri mock-images as positive examples, and real HSC images as negative examples. Compared to our previous work, where we successfully applied the classifier to around 62.5 million galaxies having an i-Kron radius of ≥0.8″, we now lowered the i-Kron radius limit to ≥0.5″. The result in an increase by around 73 million sources, amounting to a total of over 135 million images. During our visual multi-stage grading of the network candidates, we also simultaneously inspected larger stamps (80″ × 80″) to identify large, extended arcs cropped in the 10″ × 10″ cutouts and also classify their overall environment. Here, we also re-inspected our previous lens candidates with i-Kron radii of ≥0.8″ and classified their environment. Using the 546 visually identified lens candidates, we further defined various criteria by exploiting extensive and complementary photometric redshift catalogs to select the candidates in overdensities. In total, we identified 24 grade A and 138 grade B exhibit either spatially-resolved multiple images or extended, distorted arcs in the new sample. Furthermore, combining our different techniques to determine overdensities, we identified a total 231/546 lens candidates by at least one of our three identification methods for overdensities. This new sample contains only 49 group- or cluster-scale re-discoveries, while 43 systems had been identified by all three procedures. Furthermore, we performed a statistical analysis by using the neural network from HOLISMOKES IX (Schuldt et al. 2023a, A&A, 671, A147) to model these systems as singular isothermal ellipsoids with external shear and to estimate their parameter values, making this the largest uniformly modeled sample to date. We find a tendency towards larger Einstein radii for galaxy-scale systems in overdense environments, while the other parameter values as well as the uncertainty distributions are consistent between those in overdense and non-overdense environments. These results demonstrate the feasibility of downloading and applying neural network classifiers to hundreds of million cutouts, which will be needed in the upcoming era of big data from deep, wide-field imaging surveys such as Euclid and the Rubin Observatory Legacy Survey of Space and Time. At the same time, it offers a sample size that can be visually inspected by humans. These deep learning pipelines, with false-positive rates of ∼0.01%, are very powerful tools to identify such rare galaxy-scale strong lensing systems, while also aiding in the discovery of new strong lensing clusters.