arXiv subjects

Lingyu Wang

Publications and source records attributed to Lingyu Wang.

At least 19 recordsLinked to original sources

arXiv2026

Satellite quenching by radio jets of central galaxies in galaxy groups

Feedback from active galactic nuclei (AGN) is now recognized as a key component of galaxy formation models. It plays a central role in regulating the growth and quenching of galaxies in the center of groups. However, the impact of AGN feedback from central galaxies on satellite galaxies remains largely unexplored. Here based on the largest sample to date of radio AGNs in galaxy groups (Yang et al. 2007) and a comprehensive consideration of multiple physical parameters that may influence the star formation of satellite galaxies, we demonstrate that the quiescent satellite fraction around radio AGNs is higher than that around normal galaxies. The most significant enhancement is observed around AGNs with large radio lobes. These findings demonstrate that the impact of kinetic AGN feedback beyond their host galaxies to their satellites. These results provide novel insights into the physical origins of some long-standing puzzles in extragalactic astronomy, including, e.g., galactic conformity and the strong small-scale clustering of quiescent galaxies.

astro-ph.GA↗

arXiv2026

Development of TIFUUN: Terahertz Integral Field Units with Universal Nanotechnology

TIFUUN (THz Integral Field Units with Universal Nanotechnology) is an ultra-wideband mm-submm wave imaging spectrometer that capitalizes on the highly scalable integrated superconducting spectrometer technology. TIFUUN has two slots for integral field units (IFUs), which can jointly be optimized as open-hardware for each astronomical observation in terms of spatial and spectral coverage. These IFUs can have observation frequencies in the range of 90--360 GHz, with spectral resolution up to $R\equiv F/\Delta F \le 1,000$, with up to $\sim$18,000 kinetic inductance detectors (shared by the two IFUs with a flexible ratio). The ultra-wide 4:1 (2 octave) bandwidth optics fits in a remarkably compact volume, by means of thin silicon lenses and a high chief ray angle design. The first pair of IFUs are being developed for the SUBLIME (Study of the Universe By Line Intensity Mapping Experiments) experiment that aims to map CII emission at redshift $\sim$6 to trace the cosmic large-scale structure and the buildup of galaxies during reionization, using TIFUUN on the ASTE 10-m telescope. The scalability, flexibility and compactness makes TIFUUN a highly compatible and portable system suited also for upcoming telescope facilities in the vicinity, such as FYST and AtLAST/LST.

astro-ph.IM↗

arXiv2026

Towards a consistent framework of determining active galactic nucleus contribution fraction and host galaxy properties

Decomposing active galactic nucleus (AGN) emission from host-galaxy light is essential for identifying AGN-dominated systems and accurately deriving host-galaxy physical properties. However, estimating AGN contributions from multi-wavelength photometry remains challenging due to inherent parameter degeneracies in spectral energy distribution (SED) fitting. In this work, we establish a unified framework for estimating AGN contribution fractions and host-galaxy properties by combining complementary diagnostics: SED decomposition with two independent fitting codes, CIGALE and GRAHSP, and deep-learning-based imaging decomposition. We apply this framework to galaxies in the COSMOS-Web field using multi-wavelength photometry from the ultraviolet to the far-infrared. We calculate the AGN contribution fraction in the JWST/NIRCam F150W filter and compare the SED-derived estimates with independent AGN fractions obtained from deep-learning image decomposition. Our results reveal significant degeneracies in current SED-fitting approaches based on empirical or theoretical AGN templates and demonstrate that incorporating independent morphological information can help break these degeneracies and improve the reliability of AGN and host-galaxy property estimates.

astro-ph.GA↗

arXiv2026

Needle in a Haystack: Tracking UAVs from Massive Noise in Real-World 5G-A Base Station Data

The potential usage of UAVs in daily life has made monitoring them essential. However, existing systems for monitoring UAVs typically rely on cameras, LiDARs, or radars, whose limited sensing range or high deployment cost hinder large-scale adoption. In response, we develop BSense, the first system that tracks UAVs by leveraging point clouds from commercial 5G-A base stations. The key challenge lies in the dominant number of noise points that closely resemble true UAV points, resulting in a noise-to-UAV ratio over 100:1. Therefore, identifying UAVs from the raw point clouds is like finding a needle in a haystack. To overcome this, we propose a layered framework that filters noise at the point, object, and trajectory levels. At the raw point level, we observe that noise points from different spatial regions exhibit distinguishable and consistent signal fingerprints, which we can model to identify and remove them. At the object level, we design spatial and velocity consistency checks to identify false objects, and further compute confidence scores by aggregating these checks over multiple frames for more reliable discrimination. At the final trajectory level, we propose a Transformer-based network that captures multi-frame motion patterns to filter the few remaining false trajectories. We evaluated BSense on a commercial 5G-A base station deployed in an urban environment. The UAV was instructed to fly along 25 distinct trajectories across 54 cases over 7 days, yielding 155 minutes of data with more than 14,000 frames. On this dataset, our system reduces the number of false detections from an average of 168.05 per frame to 0.04, achieving an average F1 score of 95.56% and a mean localization error of 4.9 m at ranges up to 1,000 m.

cs.NI↗

arXiv2026

Martensitic-like transition between liquid crystalline and crystalline phases of prototypical discotic organic semiconductor

Phase transitions between crystalline solids occur either through the nucleation and growth mechanism, a process that is slow and destructive or through the diffusion-less and order preserving Martensitic route. In both organic and inorganic materials, Martensitic transformations are known to occur only between phases with crystalline symmetry. We demonstrate here that for canonical discotic organic semiconductor HAT6, the transition between the liquid crystalline columnar hexagonal phase (ColH) and the crystalline solid can occur through a mechanism that exhibits the hallmarks of Martensitic transformations: orientational correlations between parent and daughter phases, structural reversibility, and ultrafast kinetics. To access Martensitic-like solidification, the ColH phase of HAT6 is biaxially aligned in lithographically defined microchannels and crystallization is induced on deep supercooling. The transition mechanism is studied using a combination of polarized optical microscopy and X-ray scattering. At the largest accessible supercooling, the ColH - Crystal phase transition occurs at speeds of ~100 micrometer/s, a value that is seven orders of magnitude greater than the theoretical prediction for growth from isotropic melts. Our work suggests that Martensitic-like transformations can occur even between liquid crystals and crystals and are therefore more general than previously believed. Further, our work demonstrates that Martensitic-like transformations of anchored liquid crystals can be used to grow biaxially aligned crystals of organic molecules over arbitrarily long distances. As lattice alignment over large areas is desirable for devices like field-effect transistors and as several high-performance molecular semiconductors exhibit a ColH phase, our results hold general significance for organic electronics.

cond-mat.mtrl-sci↗

arXiv2026

The impact of cosmic filaments on starburst galaxies across cosmic times

Cosmological simulations suggest that various galaxy properties depend on their location within the cosmic web. Yet direct observational evidence of the dependence of star formation activity on distance to filaments remains scarce and is missing at z>1. We investigate how starburst, main-sequence (MS), and quenched galaxies are distributed with respect to cosmic web filaments, and how this distribution evolves with redshift. We first use the SIMBA cosmological simulation to predict the redshift evolution of the mean distance to the closest filament from z=3 to z=0 for different galaxy populations after removing stellar-mass dependencies. We then measure the corresponding signal in the COSMOS field, using COSMOS2020 and COSMOS-Web data, where accurate photometric redshifts enable reconstruction of the projected cosmic web from z=2 to z=0.5, and starbursts are identified through far-infrared spectral energy distribution fitting. In agreement with the results from SIMBA, starburst galaxies are found closer to filaments at z>1 and at larger distances at z<1, MS galaxies occupy intermediate environments with little evolution, and quenched galaxies show progressively shorter distances to filaments toward low redshift, with a crossing between starburst and MS populations around z~1. In COSMOS-Web, the relative evolution in the average distance to filaments between starburst and MS galaxies is detected at a significance level of at least 5{\sigma}. We show that a minimal toy model in which the only environmental ingredient is the sSFR-filament distance modulation measured in simulations is sufficient to reproduce the observed differential evolution of the average filament distance between starburst and MS galaxies. These results show that the imprint of large-scale environmental effects on the star formation activity of galaxies, predicted by simulations, is detectable from z=2 down to z=0.5.

astro-ph.CO↗

arXiv2025

Super-resolving Herschel - a deep learning based deconvolution and denoising technique

Dusty star-forming galaxies (DSFGs) dominate the far-infrared and sub-millimetre number counts, but single-dish surveys suffer from poor angular resolution, complicating mult-wavelength counterpart identification. Prior-driven deblending techniques require extensive fine-tuning and struggle to process large fields. This work aims to develop a fast, reliable deep-learning based deconvolution and denoising super-resolution (SR) technique. We employ a transformer neural network to improve the resolution of Herschel/SPIRE 500 $\mu$m observations by a factor 4.5, using Spitzer/MIPS 24$\mu$m and Herschel/SPIRE 250, 350, 500$\mu$m images. Trained on SIDES and SHARK simulations, we injected instrumental noise into the input simulated images, while keeping the target images noise-free to enhance de-noising capabilities of our method. We evaluated the performance on simulated test sets and real JCMT/SCUBA-2 450 $\mu$m observations in the COSMOS field which have superior resolution compared to Herschel. Our SR method achieves an inference time of $1s/deg^2$ on consumer GPUs, much faster than traditional deblending techniques. Using the simulation test sets, we show that fluxes of the extracted sources from the super-resolved image are accurate to within 5% for sources with an intrinsic flux $\gtrsim$ 8 mJy, which is a substantial improvement compared to blind extraction on the native images. Astrometric error is low ($\lesssim$ 1" vs 12" pixel scale). Reliability is $\gtrsim$ 90% for sources $>$3 mJy and $>$90% of sources with intrinsic fluxes $\gtrsim5$ mJy are recovered. Applied to real 500 $\mu$m observations, fluxes of the extracted sources from the super-resolved map agree well with SCUBA-2 measured fluxes for sources $\geq$10 mJy. Our technique enables SR over hundreds of $deg^2$ without the need for fine-tuning, facilitating statistical analysis of DSFGs.

astro-ph.GA↗

arXiv2025

How accurately can obscured galaxy luminosities be measured using spectral energy distribution fitting of near- through far-infrared observations?

Infrared-luminous galaxies are important sites of stellar and black hole mass assembly at most redshifts. Their luminosities are often estimated by fitting spectral energy distribution (SED) models to near- to far-infrared data, but the dependence of these estimates on the data used is not well-understood. Here, using observations simulated from a well-studied local sample, we compare the effects of wavelength coverage, signal-to-noise (S/N), flux calibration, angular resolution, and redshift on the recovery of starburst, AGN, and host luminosities. We show that the most important factors are wavelength coverage that spans the peak in a SED, with dense wavelength sampling. Such observations recover starburst and AGN infrared luminosities with systematic bias below $20\%$. Starburst luminosities are best recovered with far-infrared observations while AGN luminosities are best recovered with near- and mid-infrared observations, though the recovery of both are enhanced with near/mid-infrared, and far-infrared observations, respectively. Host luminosities are best recovered with near/far-infrared observations, but are usually biased low, by $\gtrsim20\%$. The recovery of starburst and AGN luminosity is enhanced by observing at high angular resolution. Starburst-dominated systems show more biased recovery of luminosities than do AGN-dominated systems. As redshift increases, far-infrared observations become more capable, and mid-infrared observations less capable, at recovering luminosities. Our results highlight the transformative power of a far-infrared instrument with dense wavelength coverage from tens to hundreds of microns for studying infrared-luminous galaxies. We tabulate estimates of systematic bias and random error for use with JWST and other observatories.

astro-ph.GA↗

arXiv2025

SAIP: A Plug-and-Play Scale-adaptive Module in Diffusion-based Inverse Problems

Solving inverse problems with diffusion models has shown promise in tasks such as image restoration. A common approach is to formulate the problem in a Bayesian framework and sample from the posterior by combining the prior score with the likelihood score. Since the likelihood term is often intractable, estimators like DPS, DMPS, and $\pi$GDM are widely adopted. However, these methods rely on a fixed, manually tuned scale to balance prior and likelihood contributions. Such a static design is suboptimal, as the ideal balance varies across timesteps and tasks, limiting performance and generalization. To address this issue, we propose SAIP, a plug-and-play module that adaptively refines the scale at each timestep without retraining or altering the diffusion backbone. SAIP integrates seamlessly into existing samplers and consistently improves reconstruction quality across diverse image restoration tasks, including challenging scenarios.

cs.LG↗

arXiv2025

Witnessing the violent, merger-driven formation of an extremely massive galaxy 1.7 Gyr after the Big Bang

We combine near-infrared imaging in two bands from the Hubble Space Telescope (HST) with archival observations of molecular gas to study SDSS J160705.16+533558.6 (J1607), an extremely luminous broad-line quasar at $z = 3.65$ that is also bright in the submillimeter (sub-mm). Via subtraction of the quasar point spread function, we show that its host galaxy is massive, with a stellar mass of $(5.8 \pm 3.0) \times 10^{11}$ M$_{\odot}$, making it comparable to giant early-type galaxies (ETGs) at $z\sim0$. If the supermassive black hole (SMBH) in the quasar is accreting at the Eddington limit, then its mass is $3.5 \times 10^{9}$ M$_{\odot}$, which is also consistent with local massive ETGs. The host has an extremely high star formation rate (SFR) of $4300 \pm 500$\sfr and a molecular gas mass of $(2.4 \pm 0.9)\times 10^{10}$ M$_{\odot}$. The quasar has two companions: one at a projected separation of 11 kpc with a stellar mass of $(7.9 \pm 5.0) \times 10^{10}$ M$_{\odot}$ but no detected molecular gas, and one 6 kpc further away in the same direction with a molecular gas mass of $(2.6 \pm 1.3) \times 10^{10}$ M$_{\odot}$ but no detected stellar emission. Since neither companion shows evidence for AGN activity, this may represent merger-driven quenching, in which the dynamics of the merger strip molecular gas from infalling galaxies. Overall, irrespective of whether the host is merging with the companions, these properties mark J1607 as forming what will become an extremely massive ($\sim10^{12}M_{\odot}$) galaxy by $z=0$.

astro-ph.GA↗

arXiv2025

Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering

Knowledge-intensive multi-hop question answering (QA) tasks, which require integrating evidence from multiple sources to address complex queries, often necessitate multiple rounds of retrieval and iterative generation by large language models (LLMs). However, incorporating many documents and extended contexts poses challenges -such as hallucinations and semantic drift-for lightweight LLMs with fewer parameters. This work proposes a novel framework called DEC (Dynamic Enhancement Chain). DEC first decomposes complex questions into logically coherent subquestions to form a hallucination-free reasoning chain. It then iteratively refines these subquestions through context-aware rewriting to generate effective query formulations. For retrieval, we introduce a lightweight discriminative keyword extraction module that leverages extracted keywords to achieve targeted, precise document recall with relatively low computational overhead. Extensive experiments on three multi-hop QA datasets demonstrate that DEC performs on par with or surpasses state-of-the-art benchmarks while significantly reducing token consumption. Notably, our approach attains state-of-the-art results on models with 8B parameters, showcasing its effectiveness in various scenarios, particularly in resource-constrained environments.

cs.CL↗

arXiv2025

Classifying merger stages with adaptive deep learning and cosmological hydrodynamical simulations

Hierarchical merging of galaxies plays an important role in galaxy formation and evolution. Mergers could trigger key evolutionary phases such as starburst activities and active accretion periods onto supermassive black holes at the centres of galaxies. We aim to detect mergers and merger stages (pre- and post-mergers) across cosmic history and test whether it is better to detect mergers and their merger stages simultaneously or hierarchically. In addition, we want to test the impact of merger time relative to the coalescence of merging galaxies. First, we generated realistic mock JWST images of simulated galaxies selected from the IllustrisTNG cosmological hydrodynamical simulations. Then we trained deep learning (DL) models in the Zoobot Python package to classify galaxies into merging/non-merging galaxies and their merger stages. We used two different set-ups: (i) two-stage, in which we classify galaxies into mergers and non-mergers and then classify the mergers into pre-mergers and post-mergers, and (ii) one-stage, in which merger/non-merger and merger stages are classified simultaneously. We found that the one-stage classification set-up moderately outperforms the two-stage set-up, offering better overall accuracy and precision, particularly for the non-merger class. Pre-mergers can be classified with the highest precision in both set-ups, possibly due to the more recognisable merging features and the presence of merging companions. The image signal-to-noise ratio affects the performance of the DL classifiers, but not much after a certain threshold is crossed. Both precision and recall of the classifiers depend strongly on merger time, finding it more difficult to identify true mergers observed at stages that are more distant to coalescence. For pre-mergers, we recommend selecting mergers which will merge in the next 0.4 Gyrs, to achieve a good balance between precision and recall.

astro-ph.GA↗

arXiv2025

Molecular Gas Heating, Star Formation Rate Relations, and AGN Feedback in Infrared-luminous Galaxy Mergers

We examine the origin of molecular gas heating in a sample of 42 infrared-luminous galaxies at $z<0.3$ by combining two sets of archival data. First, integrated CO line luminosities in the 1-0 and 5-4 through 13-12 transitions. Second, results from radiative transfer modelling that decompose their bolometric emission into starburst, AGN, and host galaxy components. We find that the CO 1-0 and 5-4 through 9-8 lines primarily arise via radiative heating in the starburst and the host galaxy. In contrast, the CO 10-9 through 13-12 lines may arise primarily in the starburst and AGN, with an increasing contribution from mechanical heating and shocks. For the sample as a whole, we find no evidence that AGN luminosity affects the heating of molecular gas by star formation. However, for starbursts with low initial optical depths, a more luminous AGN may reduce the efficiency of starburst heating of the CO 5-4 and above lines, consistent with negative AGN feedback.

astro-ph.GA↗

arXiv2024

AGN -- host galaxy photometric decomposition using a fast, accurate and precise deep learning approach

Identifying active galactic nuclei (AGN) is extremely important for understanding galaxy evolution and its connection with the assembly of supermassive black holes (SMBH). With the advent of deep and high angular resolution imaging surveys such as those conducted with the James Webb Space Telescope (JWST), it is now possible to identify galaxies with a central point source out to the very early Universe. In this study, we develop a fast, accurate and precise method to identify galaxies which host AGNs and recover the intrinsic AGN contribution to the observed total light ($f_{AGN}$). We trained a deep learning (DL) based method Zoobot to estimate the fractional contribution of a central point source to the total light. Our training sample comprises realistic mock JWST images of simulated galaxies from the IllustrisTNG cosmological hydrodynamical simulations. We injected different amounts of the real JWST point spread function (PSF) models to represent galaxies with different levels of $f_{AGN}$. We analyse the performance of our method and compare it with results obtained from the traditional light profile fitting tool GALFIT. We find excellent performance of our DL method in recovering the injected AGN fraction $f_{AGN}$, both in terms of precision and accuracy. The mean difference between the predicted and true injected $f_{AGN}$ is -0.002 and the overall root mean square error (RMSE) is 0.013. The relative absolute error (RAE) is 0.076 and the outlier (defined as predictions with RAE >20%) fraction is 6.5%. In comparison, using GALFIT on the same dataset, we achieve a mean difference of -0.02, RMSE of 0.12, RAE of 0.19 and outlier fraction of 19%. We applied our trained DL model to real JWST observations and found that 33% of X-ray-selected AGN and 15% of MIR-selected AGN are also identified as AGN using a cut at $f_{\rm AGN} > 0.1$.

astro-ph.GA↗

arXiv2024

Probabilistic and progressive deblended far-infrared and sub-millimetre point source catalogues I. Methodology and first application in the COSMOS field

Single-dish far-infrared (far-IR) and sub-millimetre (sub-mm) point source catalogues and their connections with catalogues at other wavelengths are of paramount importance. However, due to the large mismatch in spatial resolution, cross-matching galaxies at different wavelengths is challenging. This work aims to develop the next-generation deblended far-IR and sub-mm catalogues and present the first application in the COSMOS field. Our progressive deblending used the Bayesian probabilistic framework known as XID+. The deblending started from the Spitzer/MIPS 24 micron data, using an initial prior list composed of sources selected from the COSMOS2020 catalogue and radio catalogues from the VLA and the MeerKAT surveys, based on spectral energy distribution modelling which predicts fluxes of the known sources at the deblending wavelength. To speed up flux prediction, we made use of a neural network-based emulator. After deblending the 24 micron data, we proceeded to the Herschel PACS (100 & 160 micron) and SPIRE wavebands (250, 350 & 500 micron). Each time we constructed a tailor-made prior list based on the predicted fluxes of the known sources. Using simulated far-IR and sub-mm sky, we detailed the performance of our deblending pipeline. After validation with simulations, we then deblended the real observations from 24 to 500 micron and compared with blindly extracted catalogues and previous versions of deblended catalogues. As an additional test, we deblended the SCUBA-2 850 micron map and compared our deblended fluxes with ALMA measurements, which demonstrates a higher level of flux accuracy compared to previous results.We publicly release our XID+ deblended point source catalogues. These deblended long-wavelength data are crucial for studies such as deriving the fraction of dust-obscured star formation and better separation of quiescent galaxies from dusty star-forming galaxies.

astro-ph.GA↗

arXiv2024

The TNG50-SKIRT Atlas: post-processing methodology and first data release

Galaxy morphology is a powerful diagnostic to assess the realism of cosmological hydrodynamical simulations. Determining the morphology of simulated galaxies requires the generation of synthetic images through 3D radiative transfer post-processing that properly accounts for different stellar populations and interstellar dust attenuation. We use the SKIRT code to generate the TNG50-SKIRT Atlas, a synthetic UV to near-infrared broadband image atlas for a complete stellar-mass selected sample of 1154 galaxies extracted from the TNG50 cosmological simulation at $z=0$. The images have a high spatial resolution (100 pc) and a wide field of view (160 kpc). In addition to the dust-obscured images, we also release dust-free images and physical parameter property maps with matching characteristics. As a sanity check and preview application we discuss the UVJ diagram of the galaxy sample. We investigate the effect of dust attenuation on the UVJ diagram and find that it affects both the star-forming and the quiescent galaxy populations. The quiescent galaxy region is polluted by younger and star-forming highly inclined galaxies, while dust attenuation induces a separation in inclination of the star-forming galaxy population, with low-inclination galaxies remaining at the blue side of the diagram and high-inclination galaxies systematically moving towards the red side. This image atlas can be used for a variety of other applications, including galaxy morphology studies and the investigation of local scaling relations. We publicly release the images and parameter maps, and we invite the community to use them.

astro-ph.GA↗

arXiv2024

The TNG50-SKIRT Atlas: wavelength dependence of the effective radius

Galaxy sizes correlate with many other important properties of galaxies, and the cosmic evolution of galaxy sizes is an important observational diagnostic for constraining galaxy evolution models. The effective radius is probably the most widely used indicator of galaxy size. We used the TNG50-SKIRT Atlas to investigate the wavelength dependence of the effective radius of galaxies at optical and near-infrared (NIR) wavelengths. We find that, on average, the effective radius in every band exceeds the stellar mass effective radius, and that this excess systematically decreases with increasing wavelength. The optical g-band (NIR Ks-band) effective radius is on average 58% (13%) larger than the stellar mass effective radius. Effective radii measured from dust-obscured images are systematically larger than those measured from dust-free images, although the effect is limited (8.7% in the g-band, 2.1% in the Ks-band). We find that stellar population gradients are the dominant factor (about 80%) in driving the wavelength dependence of the effective radius, and that differential dust attenuation is a secondary factor (20%). Comparing our results to recent observational data, we find offsets in the absolute values of the median effective radii, up to 50% for the population of blue galaxies. We find better agreement in the slope of the wavelength dependence of the effective radius, with red galaxies having a slightly steeper slope than green-blue galaxies. Comparing our effective radii with those of galaxies from the Siena Galaxy Atlas in separate bins in z-band absolute magnitude and g-z colour, we find excellent agreement for the reddest galaxies, but again significant offsets for the blue populations: up to 70% for galaxies around Mz=-21.5. This difference in median effective radius for the bluer galaxies is most probably due to (abridged...).

astro-ph.GA↗

arXiv2023

Characterisation of Herschel-selected strong lens candidates through HST and sub-mm/mm observations

We have carried out HST snapshot observations at 1.1 $μ$m of 281 candidate strongly lensed galaxies identified in the wide-area extragalactic surveys conducted with the Herschel space observatory. Our candidates comprise systems with flux densities at $500\,μ$m$ S_{500}\geq 80$ mJy. We model and subtract the surface brightness distribution for 130 systems, where we identify a candidate for the foreground lens candidate. After combining visual inspection, archival high-resolution observations, and lens subtraction, we divide the systems into different classes according to their lensing likelihood. We confirm 65 systems to be lensed. Of these, 30 are new discoveries. We successfully perform lens modelling and source reconstruction on 23 systems, where the foreground lenses are isolated galaxies and the background sources are detected in the HST images. All the systems are successfully modelled as a singular isothermal ellipsoid. The Einstein radii of the lenses and the magnifications of the background sources are consistent with previous studies. However, the background source circularised radii (between 0.34 kpc and 1.30 kpc) are $\sim$3 times smaller than the ones measured in the sub-mm/mm for a similarly selected and partially overlapping sample. We compare our lenses with those in the SLACS survey, confirming that our lens-independent selection is more effective at picking up fainter and diffuse galaxies and group lenses. This sample represents the first step towards characterising the near-IR properties and stellar masses of the gravitationally lensed dusty star-forming galaxies.

astro-ph.GA↗