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S. Schuldt

Publications and source records attributed to S. Schuldt.

At least 19 recordsLinked to original sources
arXiv2026

TDCOSMO XXVIII. The Hubble constant from the quadruply lensed quasar J1537$-$3010 with precise time delays

We present the first measurement of the Hubble constant ($H_0$) from the quadruply-lensed quasar J1537$-$3010, which has optically-measured time delays at $\sim2 \%$ precision. We combine these delays with multi-band imaging data from the Hubble Space Telescope (HST) and model the system with two independent software and teams. We adopt a mass profile that is maximally degenerate with $H_0$ to fully incorporate the mass-sheet degeneracy in the error budget, with nuisance parameters constrained by spatially resolved stellar kinematics from the Multi Unit Spectroscopic Explorer (MUSE) and a line-of-sight (LoS) analysis using the Euclid Flagship simulation. The entire analysis is performed blindly to $H_0$, distances and mass density slope of the main deflector. After unblinding, we measure $H_0 = 75.5^{+9.3}_{-5.8}\ {\rm km\,s^{-1}\,Mpc^{-1}}$, corresponding to a $10\%$ precision measurement from a single system. This precision is driven by conservative lens modeling assumptions including differences between lens modeling methods and the limited stellar kinematics constraints, from which we infer a total mass-sheet parameter $\lambda\equiv(1-\kappa_{\rm ext})\lambda_{\rm int} = 0.89^{+0.11}_{-0.06}$ ($\lambda_{\rm int}=0.89^{+0.09}_{-0.07}$) that is consistent with current results for elliptical galaxies ($\lambda \approx 1$). Our lensing constraints, stellar kinematic measurements and LoS characterization will be included in subsequent population-level measurements of $H_0$. Moreover, our lens models combined with future near-infrared spectroscopy and imaging from the James Webb Space Telescope will further reduce the uncertainties on $H_0$ from J1537$-$3010 alone, bringing it closer to the few percents precision of the time delays.

astro-ph.CO↗
arXiv2026

A new $H_0$ measurement with SNe Requiem and Encore using $\texttt{Gravity.jl}$

We present a strong-lensing (SL) analysis of the galaxy cluster MACS J0138.0-2155 (z=0.336), the first known lens cluster discovered to host two distinct multiply imaged Type Ia supernovae (SNe): SN Requiem and SN Encore. Both SNe are located in the massive, multiply imaged red galaxy MRG-M0138 at z=1.949. The projected total mass of this cluster has been investigated with several independent lens models (Suyu+26; Pierel+26), using a sample of 23 spectroscopically confirmed multiple images from 8 background sources (0.767 10%). The forthcoming reappearance of SN Requiem offers an immediate opportunity to significantly improve constraints on H0, provided that lens-model systematics are controlled. These results establish M0138 as a premier anchor for high-precision cluster-scale TDC.

astro-ph.CO↗
arXiv2026

Euclid Quick Data Release (Q1). AstroVink: A vision transformer approach to find strong gravitational lens systems

We present AstroVink, a vision transformer classifier designed for automated identification of strong lens candidates in Euclid imaging. We build upon the DINOv2 encoder, fine tuned to distinguish between lens and non-lens galaxies. Our base model, trained on simulated strong lens systems and labelled non lenses, recovers 88 of the 110 lens candidates within the top 500 ranked candidates, corresponding to an inspection efficiency of one lens per 5.7 inspected objects in our test set. After the Q1 data release, which yielded about 500 lens candidates, we retrained the model using high confidence lens candidates and new negatives, initially flagged as potential lenses by other classifiers but rejected during visual inspection. The retrained network further improves performance, achieving recovery of all 110 systems within the same ranking and reducing the inspection effort to one lens per 4.5 inspected objects, demonstrating that incorporating real examples significantly enhances model generalisation. An analysis of training subsets revealed that the inclusion of realistic negative examples played a key role in this improvement. Finally, we applied the retrained model to the Q1 original selection of 1.08M targets, followed by a new round of Space Warps citizen science inspection and expert vetting, where we identified a total of eight Grade A and 26 Grade B new lens candidates. These results demonstrate that transformer based architectures can recover strong lens candidates with high efficiency in real Euclid data, while substantially reducing the number of candidates requiring visual inspection.

astro-ph.IM↗
arXiv2026

Euclid Quick Data Release (Q1). AgileLens: A scalable CNN-based pipeline for strong gravitational lens identification

We present an end-to-end, iterative pipeline for efficient identification of strong galaxy--galaxy lensing systems, applied to the Euclid Q1 imaging data. Starting from VIS catalogues, we reject point sources, apply a magnitude cut (I$_E$ $\leq$ 24) on deflectors, and run a pixel-level artefact/noise filter to build 96 $\times$ 96 pix cutouts; VIS+NISP colour composites are constructed with a VIS-anchored luminance scheme that preserves VIS morphology and NISP colour contrast. A VIS-only seed classifier supplies clear positives and typical impostors, from which we curate a morphology-balanced negative set and augment scarce positives. Among the six CNNs studied initially, a modified VGG16 (GlobalAveragePooling + 256/128 dense layers with the last nine layers trainable) performs best; the training set grows from 27 seed lenses (augmented to 1809) plus 2000 negatives to a colour dataset of 30,686 images. After three rounds of iterative fine-tuning, human grading of the top 4000 candidates ranked by the final model yields 441 Grade A/B candidate lensing systems, including 311 overlapping with the existing Q1 strong-lens catalogue, and 130 additional A/B candidates (9 As and 121 Bs) not previously reported. Independently, the model recovers 740 out of 905 (81.8%) candidate Q1 lenses within its top 20,000 predictions, considering off-centred samples. Candidates span I$_E$ $\simeq$ 17--24 AB mag (median 21.3 AB mag) and are redder in Y$_E$--H$_E$ than the parent population, consistent with massive early-type deflectors. Each training iteration required a week for a small team, and the approach easily scales to future Euclid releases; future work will calibrate the selection function via lens injection, extend recall through uncertainty-aware active learning, explore multi-scale or attention-based neural networks with fast post-hoc vetters that incorporate lens models into the classification.

astro-ph.GA↗
arXiv2026

Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine F -- Bright and low-redshift strong lenses

We present 72 additional galaxy-galaxy strong lenses that complement the sample discovered in the Euclid Quick Release 1 data (63.1 deg^2) of the Strong Lens Discovery Engine (SLDE) papers A-E. It is shown that previous pre-selection of potential lenses, which excluded objects from the Gaia catalogue, led to missing several bright and low-redshift strong lenses, adding more than 10% new strong lens candidates compared to the previous search. In total, the catalogue includes 38 "grade A" (confident) and 34 "grade B" (probable) candidates. These lenses are identified through a combination of two independent searches for bright nearby objects: one based on machine-learning models followed by expert visual inspection, and the other based solely on expert visual inspection, targeting objects not included in the initial machine-learning selection (a limitation identified only after extensive visual inspection). With these additional strong lens candidates, we augment the expected number of high-confidence candidates in the Euclid Wide Survey from previous forecasts to 120000. Detailed semi-automated lens modelling confirms at least 41 systems out of 72, a fraction consistent with that found in SLDE A (315 out of 488). These include: multiple edge-on disc lenses; sources with arcs near the lens centre; "red sources"; and an edge-on disk galaxy lensing a galaxy merger, producing two sets of lensed features, an Einstein ring and a doubly imaged component. The median redshift of these systems is $\Delta$ z ~ 0.3 lower than that of the SLDE A sample.

astro-ph.GA↗
arXiv2026

HOLISMOKES XX. Lens models of binary lens galaxies with five images of Supernova Winny

Strongly lensed supernovae (SNe) provide a powerful way to study cosmology, SNe and galaxies. Modelling the lens system is key to extracting astrophysical and cosmological information. We present adaptive-optics-assisted high-resolution images of SN Winny (SN 2025wny) in the J and K filters obtained with the Large Binocular Telescope (LBT). The LBT imaging confirms the presence of a fifth point source, whose colour is consistent with that of the other SN images at similar phases, while lens modelling robustly supports its interpretation as an additional image of SN~Winny. We measure the positions of the five SN images with uncertainties varying between 1 and 14 milliarcseconds. We build the first mass models using lenstronomy and GLEE, and explore three classes of mass models for the two lens galaxies G1 and G2. The optimal model class of the three is a singular isothermal ellipsoid for G1, a singular isothermal sphere for G2, and an external shear. We infer the enclosed masses within the Einstein radius as 4.61^{+0.06}_{-0.04} \times 10^{11}\,M_\odot for G1 and 1.01\pm0.02 \times 10^{11}\,M_\odot for G2. The lensing configuration by the two lens galaxies can produce two additional magnified SN images beyond the five observed ones; the exclusion of such model configurations further constrains the lens model parameters. Our model fits to the observed image positions with an RMS of ~0.0012" - 0.0025", within the observed positional uncertainties. The predicted magnifications of the multiple images vary between ~1.6 (for the faintest fifth image E) to ~10 (for the brightest image A). The predicted relative lensing magnifications of the multiple images do not match that of the observed within 2\sigma uncertainties. The differences in the relative magnifications could be due to millilensing/microlensing. Our mass models form the basis for future analyses of this unique system. (abridged)

astro-ph.GA↗
arXiv2026

A boost in the precision of cluster-mass models: Exploiting the extended surface brightness of the lensed supernova Refsdal host galaxy

Combining deep Hubble Space Telescope (HST) images and extensive data from the Multi-Unit Spectroscopic Explorer, we present new mass models of the cluster MACS J1149.5+2223, strongly lensing the supernova (SN) Refsdal, fully exploiting the source surface-brightness distribution of the SN host for the first time. In detail, we incorporated 77,000 HST pixels, in addition to the known 106 point-like multiple images, in our modeling. We considered four different models to explore the effect of the relative weighting of the point-like multiple image positions and flux distribution of the SN host on the model optimization. When the SN host's extended image is included, we find that the statistical uncertainties of all 34 free model parameters are reduced by factors ranging from one to two orders of magnitude compared to the statistical uncertainty of the point-like only model, irrespective of the adopted different image weights. We quantified the remarkably increased level of precision with which the cluster's total mass and the predicted time delays of the SN Refsdal multiple image positions can be reconstructed. We also show the delensed image of the SN host, a spiral galaxy at zSN = 1.49, in multiple HST bands. In all those applications, we obtain a significant reduction of the statistical uncertainty, which is now below the level of even the small systematic uncertainty on the mass model that could be assessed by the different approaches. These results demonstrate that with extended image models of lensing clusters it is possible to measure the cluster's total mass distribution, the values of the cosmological parameters, and the physical properties of high-redshift sources with an unparalleled precision, making the typically not-quantified systematic uncertainties now crucial.

astro-ph.CO↗
arXiv2025

Euclid Quick Data Release (Q1). From simulations to sky: Advancing machine-learning lens detection with real Euclid data

In the era of large-scale surveys like Euclid, machine learning has become an essential tool for identifying rare yet scientifically valuable objects, such as strong gravitational lenses. However, supervised machine-learning approaches require large quantities of labelled examples to train on, and the limited number of known strong lenses has lead to a reliance on simulations for training. A well-known challenge is that machine-learning models trained on one data domain often underperform when applied to a different domain: in the context of lens finding, this means that strong performance on simulated lenses does not necessarily translate into equally good performance on real observations. In Euclid's Quick Data Release 1 (Q1), covering 63 deg2, 500 strong lens candidates were discovered through a synergy of machine learning, citizen science, and expert visual inspection. These discoveries now allow us to quantify this performance gap and investigate the impact of training on real data. We find that a network trained only on simulations recovers up to 92% of simulated lenses with 100% purity, but only achieves 50% completeness with 24% purity on real Euclid data. By augmenting training data with real Euclid lenses and non-lenses, completeness improves by 25-30% in terms of the expected yield of discoverable lenses in Euclid DR1 and the full Euclid Wide Survey. Roughly 20% of this improvement comes from the inclusion of real lenses in the training data, while 5-10% comes from exposure to a more diverse set of non-lenses and false-positives from Q1. We show that the most effective lens-finding strategy for real-world performance combines the diversity of simulations with the fidelity of real lenses. This hybrid approach establishes a clear methodology for maximising lens discoveries in future data releases from Euclid, and will likely also be applicable to other surveys such as LSST.

astro-ph.IM↗
arXiv2025

Cosmic chronometers with galaxy clusters: a new avenue for multi-probe cosmology

We provide a new measurement of the expansion history of the Universe at $z=0.54$ with the cosmic chronometers (CC) method, exploiting the high-quality spectroscopic VLT/MUSE data for three galaxy clusters in close-by redshift bins: SDSS J2222+2745 ($z=0.49$), MACS J1149.5+2223 ($z=0.54$), and SDSS J1029+2623 ($z=0.59$). The central one, MACS J1149.5+2223, hosts the well-known supernova 'Refsdal', which allowed for $H_0$ measurements via time delay cosmography (TDC). This represents the first step for a self-consistent probe combination, where different methods are applied to the same data sample. After selecting the most passive and massive cluster members (38 CCs), we derive their age and physical parameters via full spectrum fitting. We use the code Bagpipes, specifically modified to remove the cosmological prior on ages. On average, the CC sample shows super-solar metallicities $Z/Z_{\odot} = 1.3 \pm 0.7$, low dust extinction $A_{\rm{V}} = 0.3 \pm 0.3$ mag and to have formed in short bursts $\tau = 0.6 \pm 0.2$ Gyr. We also observe both an ageing trend in redshift and a mass-downsizing pattern. From the age-redshift trend, implementing the CC method through a bootstrap approach, we derive a new $H(z)$ measurement: $H$($z$=0.542) = $66_{-29}^{+81}$ (stat) $\pm$13 (syst) km/s/Mpc. We also simulate the impact of increased statistics and extended redshift coverage, finding that $H$($z$) uncertainties can be reduced by up to a factor of 4 with $\sim$100 CCs and a slightly broader redshift range (d$z\sim$0.2).

astro-ph.CO↗
arXiv2025

Euclid Quick Data Release (Q1). Searching for giant gravitational arcs in galaxy clusters with mask region-based convolutional neural networks

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 is trained on a realistic simulated data set of cluster-scale SL events, constructed by injecting mock background sources into Euclidised Hubble Space Telescope images of 10 massive lensing clusters, exploiting their high-precision mass models constructed with extensive spectroscopic data. The network is trained and validated on over 4500 simulated images, and tested on an independent set of 500 simulations, as well as real Euclid Quick Data Release (Q1) observations. The trained network achieves high performance in identifying gravitational arcs in the test set, with a precision and recall of 76% and 58%, respectively, processing 2'x2' 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 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, enabling the automated analysis of SL systems in current and future wide-field surveys. The code, ARTEMIDE, is open source and will be available at github.com/LBasz/ARTEMIDE.

astro-ph.CO↗
arXiv2025

Strong Lensing Model and Dust Extinction Maps of the Host Galaxy of Type Ia Supernova H0pe

Strong gravitational lensing by massive galaxy clusters offers rare opportunities to observe multiple images of distant ($z \gtrsim 2$) 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 $\lesssim 1\sigma$, 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 ($A_V \approx 0.9$ mag) at around $\sim 1$ 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.

astro-ph.GA↗
arXiv2025

Cosmology with supernova Encore in the strong lensing cluster MACS J0138-2155: Time delays & Hubble constant measurement

Multiply-imaged supernovae (SNe) provide a novel means of constraining the Hubble constant ($H_0$). Such measurements require a combination of precise models of the lensing mass distribution and an accurate estimate of the relative time delays between arrival of the multiple images. Only two multiply-imaged SNe, Refsdal and H0pe, have enabled measurements of $H_0$ thus far. Here we detail the third such measurement for SN Encore, a $z=1.95$ SNIa discovered in JWST/NIRCam imaging. We measure the time delay, perform simulations of additional microlensing and millilensing systematics, and combine with the mass models of Suyu et al. in a double-blind analysis to obtain our $H_0$ constraint. Our final time-delay measurement is $\Delta t_{1b,1a}=-39.8_{-3.3}^{+3.9}$ days, which is combined with seven lens models weighted by the likelihood of the observed multiple image positions for a result of $H_0=66.9_{-8.1}^{+11.2} \rm{km} \rm{s}^{-1}\rm{Mpc}^{-1}$. The uncertainty on this measurement could be improved significantly if template imaging is obtained. Remarkably, a sibling to SN Encore (SN "Requiem") was discovered in the same host galaxy, making the MACS J0138.0-2155 cluster the first system known to produce more than one observed multiply-imaged SN. SN Requiem has a fourth image that is expected to appear within a few years, providing an unprecedented decade-long baseline for time-delay cosmography and an opportunity for a high-precision joint estimate of $H_0$.

astro-ph.CO↗
arXiv2025

Cosmology with supernova Encore in the strong lensing cluster MACS J0138-2155: Lens model comparison and H0 measurement

MACS J0138-2155 is the only known cluster to strongly lens two supernovae (SNe), Requiem and Encore, from the same host galaxy at z=1.949. We present seven independent mass models of the galaxy cluster built using six software packages. By conducting a blind analysis (no exchanges of results between modeling teams), we quantified uncertainties due to modeling and software. Through HST, JWST and MUSE observations, we assembled high-quality data products, including eight "gold" lensed image systems consisting of 23 images with secure spectroscopic redshifts, and one "silver" system with a likely redshift value. Restricting to the gold images, we obtain overall consistent model predictions of the positions, magnifications and time delays of SN Encore and SN Requiem images, especially for models with $\chi^2 \leq 25$. We predict the appearance of the next images of SNe Encore and Requiem with a time delay of >~3000 days and of ~3700 to 4000 days, respectively, based on a fiducial cosmological model of $H_0 = 70 {\rm\ km\ s^{-1}\ Mpc^{-1}}$ and $\Omega_{\rm m} = 0.3$. We obtain relations between $H_0$ and the time delays of SNe Encore and Requiem. In particular, for $H_0 = 73 {\rm\ km\ s^{-1}\ Mpc^{-1}}$, the four lowest $\chi^2$ models predict SN Requiem to reappear in ~Apr-Dec 2026; for $H_0 = 67 {\rm\ km\ s^{-1}\ Mpc^{-1}}$, in ~Mar-Nov 2027. Using the newly measured time delay between the two detected images of SN Encore by Pierel et al. (2026) and our mass models, we jointly infer $H_0 = {\rm 66.9^{+11.2}_{-8.1}\ km\ s^{-1}\ Mpc^{-1}}$, where the uncertainty is dominated by that of the time delay. The long delays of the next-appearing SN Requiem and SN Encore images provide excellent opportunities to measure $H_0$ with an uncertainty of 2-3%. Our mass models form the basis for cosmological inference from this unique lens cluster with two strongly lensed SNe. (Abridged)

astro-ph.CO↗
arXiv2025

Euclid: A machine-learning search for dual and lensed AGN at sub-arcsec separations

Cosmological models of hierarchical structure formation predict the existence of a widespread population of dual accreting supermassive black holes (SMBHs) on kpc-scale separations, corresponding to projected distances < 0".8 at redshifts higher than 0.5. However, close companions to known active galactic nuclei (AGN) or quasars (QSOs) can also be multiple images of the object itself, strongly lensed by a foreground galaxy, as well as foreground stars in a chance superposition. Thanks to its large sky coverage, sensitivity, and high spatial resolution, Euclid offers a unique opportunity to obtain a large, homogeneous sample of dual/lensed AGN candidates with sub-arcsec projected separations. Here we present a machine learning approach, in particular a Convolutional Neural Network (CNN), to identify close companions to known QSOs down to separations of $\sim\,$0".15 comparable to the Euclid VIS point spread function (PSF). We studied the effectiveness of the CNN in identifying dual AGN and demonstrated that it outperforms traditional techniques. Applying our CNN to a sample of $\sim\,$6000 QSOs from the Q1 Euclid data release, we find a fraction of about 0.25% dual AGN candidates with separation $\sim\,$0".4 (corresponding to $\sim$3 kpc at z=1). Estimating the foreground contamination from stellar objects, we find that most of the pair candidates with separation higher than 0".5 are likely contaminants, while below this limit, contamination is expected to be less than 20%. For objects at higher separation (>0".5, i.e. 4 kpc at z=1), we performed PSF subtraction and used colour-colour diagrams to constrain their nature. We present a first set of dual/lensed AGN candidates detected in the Q1 Euclid data, providing a starting point for the analysis of future data releases.

astro-ph.GA↗
arXiv2025

A long-lasting eruption heralds SN 2023ldh, a clone of SN 2009ip

We discuss the results of the spectroscopic and photometric monitoring of the type IIn supernova (SN) 2023ldh. Survey archive data show that the SN progenitor experienced some erratic outbursts in the years before exploding. From May 2023, the source shows a general slow luminosity rise lasting over four months with some superposed luminosity fluctuations. In analogy to SN 2009ip, we label this brightening as Event A. During Event A, SN 2023ldh reaches a maximum absolute magnitude of Mr = -15.52 +- 0.24 mag. Then the light curves show a luminosity decline of about 1 mag in all filters lasting about two weeks, followed by a steep brightening (Event B) to an absolute peak magnitude of Mr = -18.53 +- 0.23 mag, replicating the evolution of SN 2009ip and similar SNe IIn. Three spectra of SN 2023ldh are obtained during Event A, showing multi-component P Cygni profiles of H I and Fe II lines. During the rise to the Event B peak, the spectrum shows a blue continuum dominated by Balmer lines in emission with Lorentzian profiles, with a full width at half-maximum (FWHM) velocity of about 650 km/s. Later, in the post-peak phase, the spectrum reddens, and broader wings appear in the Halpha line profile. Metal lines are well visible with P Cygni profiles and velocities of about 2000 km/s. Beginning around three months past maximum and until very late phases, the Ca II lines become among the most prominent features, while Halpha is dominated by an intermediate-width component with a boxy profile. Although SN 2023ldh mimics the evolution of other SN 2009ip-like transients, it is slightly more luminous and has a slower photometric evolution. The surprisingly homogeneous observational properties of SN 2009ip-like events may indicate similar explosion scenarios and similar progenitor parameters.

astro-ph.SR↗
arXiv2025

Euclid Quick Data Release (Q1): The Strong Lensing Discovery Engine A -- System overview and lens catalogue

We present a catalogue of 497 galaxy-galaxy strong lenses in the Euclid Quick Release 1 data (63 deg$^2$). In the initial 0.45\% of Euclid's surveys, we double the total number of known lens candidates with space-based imaging. Our catalogue includes 250 grade A candidates, the vast majority of which (243) were previously unpublished. Euclid's resolution reveals rare lens configurations of scientific value including double-source-plane lenses, edge-on lenses, complete Einstein rings, and quadruply-imaged lenses. We resolve lenses with small Einstein radii ($\theta_{\rm E} < 1''$) in large numbers for the first time. These lenses are found through an initial sweep by deep learning models, followed by Space Warps citizen scientist inspection, expert vetting, and system-by-system modelling. Our search approach scales straightforwardly to Euclid Data Release 1 and, without changes, would yield approximately 7000 high-confidence (grade A or B) lens candidates by late 2026. Further extrapolating to the complete Euclid Wide Survey implies a likely yield of over 100000 high-confidence candidates, transforming strong lensing science.

astro-ph.GA↗
arXiv2025

Euclid Quick Data Release (Q1) The Strong Lensing Discovery Engine B -- Early strong lens candidates from visual inspection of high velocity dispersion galaxies

We present a search for strong gravitational lenses in Euclid imaging with high stellar velocity dispersion ($\sigma_\nu > 180$ km/s) reported by SDSS and DESI. We performed expert visual inspection and classification of $11\,660$ \Euclid images. We discovered 38 grade A and 40 grade B candidate lenses, consistent with an expected sample of $\sim$32. Palomar spectroscopy confirmed 5 lens systems, while DESI spectra confirmed one, provided ambiguous results for another, and help to discard one. The \Euclid automated lens modeler modelled 53 candidates, confirming 38 as lenses, failing to model 9, and ruling out 6 grade B candidates. For the remaining 25 candidates we could not gather additional information. More importantly, our expert-classified non-lenses provide an excellent training set for machine learning lens classifiers. We create high-fidelity simulations of \Euclid lenses by painting realistic lensed sources behind the expert tagged (non-lens) luminous red galaxies. This training set is the foundation stone for the \Euclid galaxy-galaxy strong lensing discovery engine.

astro-ph.GA↗
arXiv2025

Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine D -- Double-source-plane lens candidates

Strong gravitational lensing systems with multiple source planes are powerful tools for probing the density profiles and dark matter substructure of the galaxies. The ratio of Einstein radii is related to the dark energy equation of state through the cosmological scaling factor $\beta$. However, galaxy-scale double-source-plane lenses (DSPLs) are extremely rare. In this paper, we report the discovery of four new galaxy-scale double-source-plane lens candidates in the Euclid Quick Release 1 (Q1) data. These systems were initially identified through a combination of machine learning lens-finding models and subsequent visual inspection from citizens and experts. We apply the widely-used {\tt LensPop} lens forecasting model to predict that the full \Euclid survey will discover 1700 DSPLs, which scales to $6 \pm 3$ DSPLs in 63 deg$^2$, the area of Q1. The number of discoveries in this work is broadly consistent with this forecast. We present lens models for each DSPL and infer their $\beta$ values. Our initial Q1 sample demonstrates the promise of \Euclid to discover such rare objects.

astro-ph.GA↗