arXiv subjects

D. Sluse

Publications and source records attributed to D. Sluse.

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

TDCOSMO. XXVII. JWST-based Lens Models and H$_0$ Measurement of WFI2033, HE0435, and PG1115

Time-delay cosmography offers a one-step, distance-ladder-independent route to the Hubble-Lemaitre constant, H_0. We present new cosmography-grade lens models of three quadruply imaged quasars based on JWST-NIRCam/F115W imaging (WFI2033-4723, HE0435-1223, PG1115+080). We use the STARRED modeling technique, introduced in our previous analysis of WFI2033-4723, to reconstruct the complex JWST-NIRCam Point Spread Function at high fidelity. We combine NIRCam-based lens models with improved external convergence estimates, published time delays, and aperture-integrated stellar velocity dispersions from JWST NIRSpec to infer H_0. The analysis was carried out blindly for HE0435-1223 and PG1115+080, while it was not blind for WFI2033-4723, as we build upon the previous published model. For comparison with previous HST-based work, we limit our analysis to the case of no internal mass-sheet degeneracy ($\lambda_{\rm int}=1$). We quantify the impact of improved imaging, single-aperture kinematics, and environment measurements on central values and uncertainties. Within flat $\Lambda$CDM, assuming a uniform prior on $\Omega_{\rm m}$, we find H_0 = 71.8$_{-7.0}^{+9.2}$ $\lambda_{\rm int}$ km s$^{-1}$ Mpc$^{-1}$ for PG1115+080, 74.2$_{-4.2}^{+4.2}$ $\lambda_{\rm int}$ km s$^{-1}$ Mpc$^{-1}$ for HE0435-1223, and 73.4$_{-4.4}^{+3.4}$ $\lambda_{\rm int}$ km s$^{-1}$ Mpc$^{-1}$ for WFI2033-4723. Combining the three lenses yields H_0 = 73.5$_{-2.8}^{+2.7}$ $\lambda_{\rm int}$ km s$^{-1}$ Mpc$^{-1}$, consistent with HST-based results (73.6$_{-2.6}^{+2.6}$ $\lambda_{\rm int}$ km s$^{-1}$ Mpc$^{-1}$), but with reduced scatter between the three systems. These models will be incorporated in the TDCOSMO-2026 milestone paper with free $\lambda_{\rm int}$ in a hierarchical fashion. We close by outlining how 16 forthcoming JWST NIRCam targets will further tighten uncertainties toward percent-level precision on H_0.

astro-ph.CO↗

arXiv2026

HOLISMOKES -- XVIII. Cosmology with strongly lensed type II supernovae: Effects of instrumental setups on $H_0$

The upcoming Rubin Observatory and subsequent follow-up observations should improve the determination of the Hubble constant ($H_0$) via time-delay cosmography of strongly lensed type II supernovae (LSNe II), by enabling the detection of many more such events. In our previous work, we developed a method for determining the supernova (SN) phase from spectral absorption features. Because obtaining spectra of faint targets such as distant SN II is expensive, we examined how low-resolution spectra influence the precision of time-delay retrieval, and consequently the precision on $H_0$. We considered spectral resolutions $R = \frac{\lambda}{\Delta \lambda}$ between 100 and 250, and we investigated three signal-to-noise ratio ($S/N$) values of 10, 15, and 20, for each resolution. Furthermore, we forecast the precision on $H_{0}$ achievable with $S/N=10$ and compared the observing time required to reach it with ground-based and space-based facilities. We find that the time delay can be determined without bias and with uncertainties as low as 1.3 days for the investigated resolutions and $S/N$ values when we combine time-delay measurements of multiple absorption lines. For a typical LSN II system (absolute magnitude $\sim-$17 mag in the rest-frame V band, source redshift of \zs = 0.8), the required exposure times range from multiple hours for ground-based observations to a few minutes for space-based observations with the JWST. Our predictions on the precision of $H_0$ for a single lensed SN range from 14.2\% for $R = 100$ and $S/N$ = 10 to 7.5\% for $R = 250$ and $S/N$ = 20, enabling a 1\% determination of $H_0$ from $\sim$20 lensed SNe in the coming years.

astro-ph.CO↗

arXiv2026

The free-streaming length of dark matter from JWST observations of 28 strong gravitational lenses

The formation of gravitationally bound overdensities of dark matter (DM), or \textit{halos}, is a generic prediction of theories with particle DM. We present a measurement of halo properties in 28 quadruple image strong lens systems recently observed by JWST, and use these observations to constrain the free-streaming length, $\lambda_{\rm{FS}}$, of DM, a quantity that depends on the DM particle mass and formation mechanism. We improve on previous lensing analyses by simultaneously reconstructing extended lensed arcs with image positions and relative magnifications, enhancing sensitivity to perturbations by halos. Our analysis rules out deviations from the predictions of cold dark matter (CDM) on scales above $10^{7.2} M_{\odot}$ and $10^{7.4} M_{\odot}$ for subhalo abundance predicted by cosmological $N$-body simulations and semi-analytic models, respectively. These bounds correspond to upper limits $\lambda_{\rm{FS}}<6.0 \ \rm{kpc}$ and $\lambda_{\rm{FS}}<7.0 \ \rm{kpc}$, and lower limits on the mass of a spin--1/2 thermal relic DM particle $m_{\rm{therm}}>7.4 \ \rm{keV}$ and $m_{\rm{therm}}>6.5 \ \rm{keV}$. Conversely, assuming a negligible free-streaming length, as predicted by CDM, we measure a projected mass in subhalos around elliptical galaxies $1.7_{-1.2}^{+2.6} \times 10^7 \ \mathrm{M}_{\odot} \ \rm{kpc^{-2}}$ at $95 \%$ confidence. These inferences confirm key predictions of the CDM paradigm.

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

Measurement of the minimum cold dark matter halo mass with strong gravitational lensing

We explore the lowest mass limit that can be placed on the halo mass function in CDM using 28 strong gravitational lenses. For this purpose, we study an extreme model in which the halo mass function and mass-concentration relation follow CDM, with a sharp cutoff at some mass scale, $m_{\rm{low}}$. Lensing provides a unique window into this quantity as it does not depend on the presence of baryons in dark matter halos and also allows the detection of low mass halos at cosmological distances, both in the lens galaxies and along the line-of-sight. Our model incorporates the effects of tidal stripping of subhalos, leading to the presence of many subhalos below a given model cutoff scale. We place an upper limit on the low-mass cutoff of the halo mass function of $m_{\rm{low}}<10^{8.3}$ M$_\odot$ at 10:1 odds using a prior for the normalization of the subhalo mass function from the semi-analytic model {\tt galacticus} and $m_{\rm{low}}<10^{8.2}$ M$_\odot$ at 10:1 odds using a prior from $N$-body simulations. These limits are comparable to, or stronger than, existing constraints based on Milky Way satellite galaxies. Based on these results, we forecast more than an order of magnitude improvement with a sample of 200 quadruply imaged quasar lenses. This number represents a small subset of the thousands that are anticipated to be discovered by Rubin, Euclid, and Roman. Furthermore, with this larger sample of lenses we expect to directly constrain the normalization of the subhalo mass function, thereby eliminating a major source of uncertainty in our current measurements.

astro-ph.CO↗

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

Polarization echoes from past nuclear activity in the quasi-periodic eruption source GSN 069

Context. X-ray quasi-periodic eruptions (QPEs) are repeating, high-amplitude, soft X-ray bursts observed from the nuclei of a dozen nearby low-mass galaxies. Their origin remains a puzzle in the physics of accretion variability. Observational data indicate that X-ray and/or optical tidal disruption events (TDEs) may precede QPE detections. Although both kinds of outburst are driven by supermassive black holes, they are more frequently detected in faded active galactic nuclei (AGNs), when the TDE is not happening in a dormant galaxy. In the case of the QPE discovery source, GSN 069, it remains debated whether its past activity arose from a previous AGN phase or from an enhanced TDE rate. Aims. We investigated the origin of the past nuclear activity in GSN 069. Methods. Past AGN activity imprints detectable polarization in optical light, due to the expected delay between direct and scattered light. On 6 September 2019, we targeted GSN 069 with VLT/FORS2 in both imaging polarimetry and spectropolarimetry modes so that its optical polarization could be investigated while the first detected QPE phase was still active. Results. We measured a rising polarization, from ~0% to ~1.5%, as moving away from the nucleus of GSN 069. This rise is probed to be intrinsic to the central engine, confirming the already detected extended emission line region (EELR) by integral field unit data. Conclusions. The increasing radial polarization demonstrates a switched-off nucleus. The polarization angle traces an axis aligned with elongated [OIII], [NII], and H{\alpha} gas distributions, revealing an EELR that may be consistent with relic polarization cones, therefore suggesting the presence of a torus-like structure in the past. Thus, optical polarization echoes geometrically favor a faded AGN as the origin of the EELR rather than a past elevated TDE rate, although the latter cannot be excluded.

astro-ph.GA↗

arXiv2025

JWST Lensed Quasar Dark Matter Survey III: Dark Matter Sensitive Flux Ratios and Warm Dark Matter Constraint from the Full Sample

We present the full sample of measurements of the warm dust emission of 31 strongly-lensed, multiply imaged quasars, observed with JWST MIRI multiband imaging, which we use to constrain the particle properties of dark matter. The strongly lensed warm dust region of quasars is compact and statistically sensitive to a population of dark matter halos down to masses of $10^6$ M$_\odot$. The high spatial resolution and infrared sensitivity of MIRI make it uniquely suited to measure multiply imaged warm dust emission from quasars and thus to infer the properties of low-mass dark halos. We use the measured flux ratios to test for a warm dark matter turnover in the halo mass function. To infer the dark matter parameters, we use a forward modeling pipeline which explores dark matter parameters while also accounting for tidal stripping effects on subhalos, globular clusters, and complex deflector macromodels with $m=1, m=3, \text{ and } m=4$ elliptical multipole moments. Adopting a comparable prior on the projected density of substructure to our previous analyses, the data presented here provide a factor of 2 improvement in sensitivity to a turnover in the halo mass function. Assuming subhalo abundance predicted by the semi-analytic model galacticus we infer with a Bayes factor of 10:1, a half-mode mass $m_{\rm{hm}} < 10^{7.8} M_{\odot}$ (m>5.6 keV for a thermally produced dark matter particle). If instead we use a prior from N-body simulations, we infer $m_{\rm{hm}} < 10^{7.6} M_{\odot}$ (m>6.4 keV). This is one of the strongest constraints to date on a turnover on the halo mass function, and the flux ratios and inference methodology presented here can be used to test a broad range of dark matter physics.

astro-ph.CO↗

arXiv2025

JWST lensed quasar dark matter survey IV: Stringent warm dark matter constraints from the joint reconstruction of extended lensed arcs and quasar flux ratios

We present a measurement of the free-streaming length of dark matter (DM) and subhalo abundance around 28 quadruple image strong lenses using observations from JWST MIRI presented in Paper III of this series. We improve on previous inferences on DM properties from lensed quasars by simultaneously reconstructing extended lensed arcs with image positions and relative magnifications (flux ratios). Our forward modeling framework generates full populations of subhalos, line-of-sight halos, and globular clusters, uses an accurate model for subhalo tidal evolution, and accounts for free-streaming effects on halo abundance and concentration. Modeling lensed arcs leads to more-precise model-predicted flux ratios, breaking covariance between subhalo abundance and the free-streaming scale parameterized by the half-mode mass $m_{\rm{hm}}$. Assuming subhalo abundance predicted by the semi-analytic model {\tt{galacticus}} ($N$-body simulations), we infer (Bayes factor of 10:1) $m_{\rm{hm}} < 10^{7.4} \mathrm{M}_{\odot}$ ($m_{\rm{hm}} < 10^{7.2} \mathrm{M}_{\odot}$), a 0.4 dex improvement relative to omitting lensed arcs. These bounds correspond to lower limits on thermal relic DM particle masses of $6.5$ and $7.4$ keV, respectively. Conversely, assuming DM is cold, we infer a projected mass in subhalos ($10^6 < m/M_{\odot}<10^{10.7}$) of $1.7_{-1.2}^{+2.6} \times 10^7 \ \mathrm{M}_{\odot} \ \rm{kpc^{-2}}$ at $95 \%$ confidence. This is consistent with {\tt{galacticus}} predictions ($0.9 \times 10^7 \mathrm{M}_{\odot} \ \rm{kpc^{-2}}$), but in mild tension with recent $N$-body simulations ($0.6 \times 10^7 \mathrm{M}_{\odot} \ \rm{kpc^{-2}}$). Our results are among the strongest bounds on WDM, and the most precise measurement of subhalo abundance around strong lenses. Further improvements will follow from the large sample of lenses to be discovered by Euclid, Rubin, and Roman.

astro-ph.CO↗

arXiv2025

Does Machine Learning Work? A Comparative Analysis of Strong Gravitational Lens Searches in the Dark Energy Survey

We present a systematic comparison of three independent machine learning (ML)-based searches for strong gravitational lenses applied to the Dark Energy Survey (Jacobs et al. 2019a,b; Rojas et al. 2022; Gonzalez et al. 2025). Each search employs a distinct ML architecture and training strategy, allowing us to evaluate their relative performance, completeness, and complementarity. Using a visually inspected sample of 1651 systems previously reported as lens candidates, we assess how each model scores these systems and quantify their agreement with expert classifications. The three models show progressive improvement in performance, with F1-scores of 0.31, 0.35, and 0.54 for Jacobs, Rojas, and Gonzalez, respectively. Their completeness for moderate- to high-confidence lens candidates follows a similar trend (31%, 52%, and 70%). When combined, the models recover 82% of all such systems, highlighting their strong complementarity. Additionally, we explore ensemble strategies: average, median, linear regression, decision trees, random forests, and an Independent Bayesian method. We find that all but averaging achieve higher maximum F1 scores than the best individual model, with some ensemble methods improving precision by up to a factor of six. These results demonstrate that combining multiple, diverse ML classifiers can substantially improve the completeness of lens samples while drastically reducing false positives, offering practical guidance for optimizing future ML-based strong lens searches in wide-field surveys.

astro-ph.GA↗

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

Euclid Quick Data Release (Q1). The active galaxies of Euclid

We present a catalogue of candidate active galactic nuclei (AGN) in the $Euclid$ Quick Release (Q1) fields. For each $Euclid$ source we collect multi-wavelength photometry and spectroscopy information from Galaxy Evolution Explorer (GALEX), $Gaia$, Dark Energy Survey (DES), Wise-field Infrared Survey Explorer (WISE), $Spitzer$, Dark Energy Survey (DESI), and Sloan Digital Sky Survey (SDSS), including spectroscopic redshift from public compilations. We investigate the AGN contents of the Q1 fields by applying selection criteria using $Euclid$ colours and WISE-AllWISE cuts finding respectively 292,222 and 65,131 candidates. We also create a high-purity QSO catalogue based on $Gaia$ DR3 information containing 1971 candidates. Furthermore, we utilise the collected spectroscopic information from DESI to perform broad-line and narrow-line AGN selections, leading to a total of 4392 AGN candidates in the Q1 field. We investigate and refine the Q1 probabilistic random forest QSO population, selecting a total of 180,666 candidates. Additionally, we perform SED fitting on a subset of sources with available $z_{\text{spec}}$, and by utilizing the derived AGN fraction, we identify a total of 7766 AGN candidates. We discuss purity and completeness of the selections and define two new colour selection criteria ($JH$_$I_{\text{E}}Y$ and $I_{\text{E}}H$_$gz$) to improve on purity, finding 313,714 and 267,513 candidates respectively in the Q1 data. We find a total of 229,779 AGN candidates equivalent to an AGN surface density of 3641 deg$^{-2}$ for $18<I_{\text{E}}\leq 24.5$, and a subsample of 30,422 candidates corresponding to an AGN surface density of 482 deg$^{-2}$ when limiting the depth to $18<I_{\text{E}}\leq 22$. The surface density of AGN recovered from this work is in line with predictions based on the AGN X-ray luminosity functions.

astro-ph.HE↗

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↗

arXiv2025

Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine E -- Ensemble classification of strong gravitational lenses: lessons for Data Release 1

The Euclid Wide Survey (EWS) is expected to identify of order $100\,000$ galaxy-galaxy strong lenses across $14\,000$deg$^2$. The Euclid Quick Data Release (Q1) of $63.1$deg$^2$ Euclid images provides an excellent opportunity to test our lens-finding ability, and to verify the anticipated lens frequency in the EWS. Following the Q1 data release, eight machine learning networks from five teams were applied to approximately one million images. This was followed by a citizen science inspection of a subset of around $100\,000$ images, of which $65\%$ received high network scores, with the remainder randomly selected. The top scoring outputs were inspected by experts to establish confident (grade A), likely (grade B), possible (grade C), and unlikely lenses. In this paper we combine the citizen science and machine learning classifiers into an ensemble, demonstrating that a combined approach can produce a purer and more complete sample than the original individual classifiers. Using the expert-graded subset as ground truth, we find that this ensemble can provide a purity of $52\pm2\%$ (grade A/B lenses) with $50\%$ completeness (for context, due to the rarity of lenses a random classifier would have a purity of $0.05\%$). We discuss future lessons for the first major Euclid data release (DR1), where the big-data challenges will become more significant and will require analysing more than $\sim300$ million galaxies, and thus time investment of both experts and citizens must be carefully managed.

astro-ph.GA↗