Search

The next generation of gravitational wave detectors and electromagnetic telescopes are beckoning the onset of the multi-messenger era and the exciting science that lies ahead. Multi-messenger strong gravitational lensing will help probe some of the most important questions of the Universe in an unprecedented manner. In particular, understanding the nature of gravitational wave sources, the underlying physical processes and mechanisms that produce emissions well before or right until the time of the merger, their associations to the seemingly distinct populations of gamma ray bursts, fast radio bursts and kilonovae. Not to mention, multi-messenger lensing will offer unique probes of test of gravity models and constraints on cosmological parameters complementary to other probes. Enabling multi-messenger science calls for concerted follow-up efforts and development of new and shared resources required in the community., Comment: 19 pages, 5 figures, Review article in the Royal Society Theo Murphy Meeting on "Multi-messenger Gravitational Lensing", accepted in Philosophical Transactions A

Strongly lensed type Ia supernovae (SNe Ia) provide a unique cosmological probe to address the Hubble tension problem in cosmology. In addition to the sensitivity of the time delays to the value of the Hubble constant, the transient and standard candle nature of SNe Ia also enable valuable joint constraints on the model of the lens and the cosmological parameters. The upcoming Legacy Survey of Space and Time (LSST) with the Vera C. Rubin Observatory is expected to increase the number of observed SNe Ia by an order of magnitude in ten years of its lifetime. However, finding such systems in the LSST data is a challenge. In this work, we revisit the color-magnitude (CM) diagram used previously as a means to identify lensed SNe Ia and extend the work further as follows. We simulate LSST-like photometric data ($rizy$~bands) of lensed SNe Ia and analyze it in the CM parameter space. We find that a subset of lensed SNe Ia are redder compared to unlensed SNe Ia at a given magnitude, both in the rising and falling phases of their light curves and for SNe up to $z=3$. We propose a modified selection criterion based on these new results. We show that the contamination coming from the unlensed core-collapse (CC) SNe is negligible, whereas a small fraction of lensed CC SNe types Ib and Ic may get selected by this criterion as potential lensed SNe. Finally, we demonstrate that our criterion works well on a wide sample of observed unlensed SNe Ia, a handful of known multiply-imaged lensed SNe systems, and a representative sample of observed super-luminous supernovae.

Time delays in both galaxy- and cluster-scale strong gravitational lenses have recently attracted a lot of attention in the context of the Hubble tension. Future wide-field cadenced surveys, such as the LSST, are anticipated to discover strong lenses across various scales. We generate mock catalogs of strongly lensed QSOs and SNe on galaxy-, group-, and cluster-scales based on a halo model that incorporates dark matter halos, galaxies, and subhalos. For the upcoming LSST survey, we predict that approximately 3500 lensed QSOs and 200 lensed SNe with resolved multiple images will be discovered. Among these, about 80 lensed QSOs and 10 lensed SNe will have maximum image separations larger than 10 arcsec, which roughly correspond to cluster-scale strong lensing. We find that adopting the Chabrier stellar IMF instead of the fiducial Salpeter IMF reduces the predicted number of strong lenses approximately by half, while the distributions of lens and source redshifts and image separations are not significantly changed. In addition to mock catalogs of multiple-image lens systems, we create mock catalogs of highly magnified systems, including both multiple-image and single-image systems. We find that such highly magnified systems are typically produced by massive galaxies, but non-negligible fraction of them are located in the outskirt of galaxy groups and clusters. Furthermore, we compare subsamples of our mock catalogs with lensed QSO samples constructed from the SDSS and Gaia to find that our mock catalogs with the fiducial Salpeter IMF reproduce the observation quite well. In contrast, our mock catalogs with the Chabrier IMF predict a significantly smaller number of lensed QSOs compared with observations, which adds evidence that the stellar IMF of massive galaxies is Salpeter-like. Our python code SL-Hammocks as well as the mock catalogs are made available online. (abridged), Comment: 26 pages, 20 figures, 3 tables, accepted for publication in the Open Journal of Astrophysics, the code available at https://github.com/LSSTDESC/SL-Hammocks and the mock catalogs available at https://github.com/LSST-strong-lensing/data_public ; v4: typos in Introduction fixed

Galaxy-scale strong gravitational lenses are valuable objects for a variety of astrophysical and cosmological applications. Strong lensing galaxies are rare, so efficient search methods, such as convolutional neural networks, are often used on large imaging datasets. In this work, we apply a new technique to improve the performance of supervised neural networks by subtracting the central (lensing) galaxy light from both the training and test datasets. We use multiband imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) as our training and test datasets. By subtracting the lensing galaxy light, we increase the contrast of the lensed source compared to the original imaging data. We also apply the light subtraction to non-lenses in order to compare them to the light-subtracted lenses. Residual features resulting from poor light subtraction can adversely affect the performance of networks trained on the subtracted images alone. We find that combining the light-subtracted images with the original gri-band images for training and classification can overcome this and improve the overall classification accuracy. We find the area under the receiver operating characteristic curve can be improved to 0.841 using the combination of the fiducial images and light-subtracted images, compared to 0.808 for the fiducial imaging dataset alone. This may be a promising technique for improving future lens searches using CNNs.

Strongly lensed supernovae (SNe) are a rare class of transient that can offer tight cosmological constraints that are complementary to methods from other astronomical events. We present a follow-up study of one recently-discovered strongly lensed SN, the quadruply-imaged Type Ia SN 2022qmx (aka, "SN Zwicky") at z = 0.3544. We measure updated, template-subtracted photometry for SN Zwicky and derive improved time delays and magnifications. This is possible because SNe are transient, fading away after reaching their peak brightness. Specifically, we measure point spread function (PSF) photometry for all four images of SN Zwicky in three Hubble Space Telescope WFC3/UVIS passbands (F475W, F625W, F814W) and one WFC3/IR passband (F160W), with template images taken $\sim 11$ months after the epoch in which the SN images appear. We find consistency to within $2\sigma$ between lens model predicted time delays ($\lesssim1$ day), and measured time delays with HST colors ($\lesssim2$ days), including the uncertainty from chromatic microlensing that may arise from stars in the lensing galaxy. The standardizable nature of SNe Ia allows us to estimate absolute magnifications for the four images, with images A and C being elevated in magnification compared to lens model predictions by about $6\sigma$ and $3\sigma$ respectively, confirming previous work. We show that millilensing or differential dust extinction is unable to explain these discrepancies and find evidence for the existence of microlensing in images A, C, and potentially D, that may contribute to the anomalous magnification., Comment: Submitted to ApJ

On the 29th of May 2023, the LIGO-Virgo-KAGRA Collaboration observed a compact binary coalescence event consistent with a neutron star-black hole merger, though the heavier object of mass 2.5-4.5 $M_\odot$ would fall into the purported lower mass gap. An alternative explanation for apparent observations of events in this mass range has been suggested as strongly gravitationally lensed binary neutron stars. In this scenario, magnification would lead to the source appearing closer and heavier than it really is. Here, we investigate the chances and possible consequences for the GW230529 event to be gravitationally lensed. We find this would require high magnifications and we obtain low rates for observing such an event, with a relative fraction of lensed versus unlensed observed events of $2 \times 10^{-3}$ at most. When comparing the lensed and unlensed hypotheses accounting for the latest rates and population model, we find a 1/58 chance of lensing, disfavoring this option. Moreover, when the magnification is assumed to be strong enough to bring the mass of the heavier binary component below the standard limits on neutron star masses, we find high probability for the lighter object to have a sub-solar mass, making the binary even more exotic than a mass-gap neutron star-black hole system. Even when the secondary is not sub-solar, its tidal deformability would likely be measurable, which is not the case for GW230529. Finally, we do not find evidence for extra lensing signatures such as the arrival of additional lensed images, type-II image dephasing, or microlensing. Therefore, we conclude it is unlikely for GW230529 to be a strongly gravitationally lensed binary neutron star signal., Comment: 15 pages, 11 figures

'$ler$' is a statistics-based Python package specifically designed for computing detectable rates of both lensed and unlensed GW events, catering to the requirements of the LIGO-Virgo-KAGRA Scientific Collaboration and astrophysics research scholars. The core functionality of '$ler$' intricately hinges upon the interplay of various components which include sampling the properties of compact-binary sources, lens galaxies characteristics, solving lens equations to derive properties of resultant images, and computing detectable GW rates. This comprehensive functionality builds on the leveraging of array operations and linear algebra from the $numpy$ library, enhanced by interpolation methods from $scipy$ and Python's $multiprocessing$ capabilities. Efficiency is further boosted by the $numba$ library's Just-In-Time ($njit$) compilation, optimizing extensive numerical computations and employing the inverse transform sampling method to replace more cumbersome rejection sampling. The modular design of '$ler$' not only optimizes speed and functionality but also ensures adaptability and upgradability, supporting the integration of additional statistics as research evolves. Currently, '$ler$' is an important tool in generating simulated GW events, both lensed and unlensed, and provides astrophysically accurate distributions of event-related parameters for both detectable and non-detectable events. This functionality aids in event validation and enhances the forecasting of detection capabilities across various GW detectors to study such events. The architecture of the '$ler$' API facilitates seamless compatibility with other software packages, allowing researchers to integrate and utilize its functionalities based on specific scientific requirements., Comment: 5 pages, 1 Logo in each of the pages, this is for the JOSS publication

Efficient algorithms are being developed to search for strong gravitational lens systems owing to increasing large imaging surveys. Neural networks have been successfully used to discover galaxy-scale lens systems in imaging surveys such as the Kilo Degree Survey, Hyper-Suprime Cam (HSC) Survey and Dark Energy Survey over the last few years. Thus, it has become imperative to understand how some of these networks compare, their strengths and the role of the training datasets as most of the networks make use of supervised learning algorithms. In this work, we present the first-of-its-kind systematic comparison and benchmarking of networks from four teams that have analysed the HSC Survey data. Each team has designed their training samples and developed neural networks independently but coordinated apriori in reserving specific datasets strictly for test purposes. The test sample consists of mock lenses, real (candidate) lenses and real non-lenses gathered from various sources to benchmark and characterise the performance of each of the network. While each team's network performed much better on their own constructed test samples compared to those from others, all networks performed comparable on the test sample with real (candidate) lenses and non-lenses. We also investigate the impact of swapping the training samples amongst the teams while retaining the same network architecture. We find that this resulted in improved performance for some networks. These results have direct implications on measures to be taken for lens searches with upcoming imaging surveys such as the Rubin-Legacy Survey of Space and Time, Roman and Euclid., Comment: 13 pages, 8 figures

Strong lensing of background galaxies provides important information about the matter distribution around lens galaxies. Traditional modelling of such strong lenses is both time and resource intensive. Fast and automated analysis methods are the need of the hour given large upcoming surveys. In this work, we build and train a simple convolutional neural network with an aim of rapidly predicting model parameters of gravitational lenses. We focus on the inference of the Einstein radius, and ellipticity components of the mass distribution. We train our network on a variety of simulated data with increasing degree of realism and compare its performance on simulated test data in a quantitative manner. We also model 182 gravitational lenses from Subaru HSC survey using {\sc YattaLens} pipeline to infer their model parameters, which allow a benchmark to compare the predictions of the network. Given all considerations, we conclude that the network trained on simulated samples with lensed sources injected in empty HSC cutouts is the most robust, reproducing Einstein radii with an accuracy of about $10-20$ percent, a bias less than 5 percent, and an outlier fraction of the order of 10 percent. We argue in favour of the subtraction of the lens light before modelling the lens mass distribution. Our comparisons of the inferred parameters of 10 HSC lenses previously modelled in the literature, demonstrate agreement on the Einstein radius. However, the ellipticity components from the network as well as the individual modelling methods, seem to have systematic uncertainties beyond the quoted errors., Comment: 16 pages, 11 figures

Strongly gravitationally lensed supernovae (LSNe) are promising probes for providing absolute distance measurements using gravitational-lens time delays. Spatially unresolved LSNe offer an opportunity to enhance the sample size for precision cosmology. We predict that there will be approximately three times as many unresolved as resolved LSNe Ia in the Legacy Survey of Space and Time (LSST) by the {\it Rubin} Observatory. In this article, we explore the feasibility of detecting unresolved LSNe Ia from a pool of preclassified SNe Ia light curves using the shape of the blended light curves with deep-learning techniques. We find that $\sim 30\%$ unresolved LSNe Ia can be detected with a simple 1D convolutional neural network (CNN) using well-sampled $rizy$-band light curves (with a false-positive rate of $\sim 3\%$). Even when the light curve is well observed in only a single band among $r$, $i$, and $z$, detection is still possible with false-positive rates ranging from $\sim 4$ to $7\%$ depending on the band. Furthermore, we demonstrate that these unresolved cases can be detected at an early stage using light curves up to $\sim20$ days from the first observation with well-controlled false-positive rates, providing ample opportunity to trigger follow-up observations. Additionally, we demonstrate the feasibility of time-delay estimations using solely LSST-like data of unresolved light curves, particularly for doubles, when excluding systems with low time delays and magnification ratios. However, the abundance of such systems among those unresolved in LSST poses a significant challenge. This approach holds potential utility for upcoming wide-field surveys, and overall results could significantly improve with enhanced cadence and depth in the future surveys., Comment: 16 pages, 9 figures, published in A&A. The data products and the relevant codes are available at https://zenodo.org/records/13644602 and https://github.com/deltasata/Unresolved_LSNeIa_in_LSST respectively

By the end of the next decade, we hope to have detected strongly lensed gravitational waves by galaxies or clusters. Although there exist optimal methods for identifying lensed signal, it is shown that machine learning (ML) algorithms can give comparable performance but are orders of magnitude faster than non-ML methods. We present the SLICK pipeline which comprises a parallel network based on deep learning. We analyse the Q-transform maps (QT maps) and the Sine-Gaussian maps (SGP-maps) generated for the binary black hole signals injected in Gaussian as well as real noise. We compare our network performance with the previous work and find that the efficiency of our model is higher by a factor of 5 at a false positive rate of 0.001. Further, we show that including SGP maps with QT maps data results in a better performance than analysing QT maps alone. When combined with sky localisation constraints, we hope to get unprecedented accuracy in the predictions than previously possible. We also evaluate our model on the real events detected by the LIGO--Virgo collaboration and find that our network correctly classifies all of them, consistent with non-detection of lensing., Comment: 10 pages and 5 figures

Noise in various interferometer systems can sometimes couple non-linearly to create excess noise in the gravitational wave (GW) strain data. Third-order statistics, such as bicoherence and biphase, can identify these couplings and help discriminate those occurrences from astrophysical GW signals. However, the conventional analysis can yield large bicoherence values even when no phase-coupling is present, thereby, resulting in false identifications. Introducing artificial phase randomization in computing the bicoherence reduces such occurrences with negligible impact on its effectiveness for detecting true phase-coupled disturbances. We demonstrate this property with simulated disturbances in this work. Statistical hypothesis testing is used for distinguishing phase-coupled disturbances from non-phase coupled ones when employing the phase-randomized bicoherence. We also obtain an expression for the bicoherence value that minimizes the sum of the probabilities of false positives and false negatives. This can be chosen as a threshold for shortlisting bicoherence triggers for further scrutiny for the presence of non-linear coupling. Finally, the utility of the phase-randomized bicoherence analysis in GW time-series data is demonstrated for the following three scenarios: (1) Finding third-order statistical similarities within categories of noise transients, such as blips and koi fish. If these non-Gaussian noise transients, or glitches, have a common source, their bicoherence maps can have similarities arising from common bifrequencies related to that source. (2) Differentiating linear or non-linear phase-coupled glitches from compact binary coalescence signals through their bicoherence maps. This is explained with a simulated signal. (3) Identifying repeated bifrequencies in the second and third observation runs (i.e., O2 and O3) of LIGO and Virgo., Comment: 25 Pages, 10 figures. Reviewed by LIGO Scientific Collaboration (LSC) with LIGO Document Number P2200344

We apply a novel model based on convolutional neural networks (CNNs) to identify gravitationally-lensed galaxies in multi-band imaging of the Hyper Suprime Cam Subaru Strategic Program (HSC-SSP) Survey. The trained model is applied to a parent sample of 2 350 061 galaxies selected from the $\sim$ 800 deg$^2$ Wide area of the HSC-SSP Public Data Release 2. The galaxies in HSC Wide are selected based on stringent pre-selection criteria, such as multiband magnitudes, stellar mass, star formation rate, extendedness limit, photometric redshift range, etc. Initially, the CNNs provide a total of 20 241 cutouts with a score greater than 0.9, but this number is subsequently reduced to 1 522 cutouts by removing definite non-lenses for further inspection by human eyes. We discover 43 definite and 269 probable lenses, of which 97 are completely new. In addition, out of 880 potential lenses, we recovered 289 known systems in the literature. We identify 143 candidates from the known systems that had higher confidence in previous searches. Our model can also recover 285 candidate galaxy-scale lenses from the Survey of Gravitationally lensed Objects in HSC Imaging (SuGOHI), where a single foreground galaxy acts as the deflector. Even though group-scale and cluster-scale lens systems were not included in the training, a sample of 32 SuGOHI-c (i.e., group/cluster-scale systems) lens candidates was retrieved. Our discoveries will be useful for ongoing and planned spectroscopic surveys, such as the Subaru Prime Focus Spectrograph project, to measure lens and source redshifts in order to enable detailed lens modelling., Comment: Submitted to MNRAS, 16 pages, 13 figures. Comments welcome

The arrival of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), Euclid-Wide and Roman wide area sensitive surveys will herald a new era in strong lens science in which the number of strong lenses known is expected to rise from $\mathcal{O}(10^3)$ to $\mathcal{O}(10^5)$. However, current lens-finding methods still require time-consuming follow-up visual inspection by strong-lens experts to remove false positives which is only set to increase with these surveys. In this work we demonstrate a range of methods to produce calibrated probabilities to help determine the veracity of any given lens candidate. To do this we use the classifications from citizen science and multiple neural networks for galaxies selected from the Hyper Suprime-Cam (HSC) survey. Our methodology is not restricted to particular classifier types and could be applied to any strong lens classifier which produces quantitative scores. Using these calibrated probabilities, we generate an ensemble classifier, combining citizen science and neural network lens finders. We find such an ensemble can provide improved classification over the individual classifiers. We find a false positive rate of $10^{-3}$ can be achieved with a completeness of $46\%$, compared to $34\%$ for the best individual classifier. Given the large number of galaxy-galaxy strong lenses anticipated in LSST, such improvement would still produce significant numbers of false positives, in which case using calibrated probabilities will be essential for population analysis of large populations of lenses., Comment: 14 pages, 9 figures. Accepted for publication in MNRAS

Strong gravitational lenses provide unique laboratories for cosmological and astrophysical investigations, but they must first be discovered - a task that can be met with significant contamination by other astrophysical objects and asterisms. Here we review strong lens searches, covering various sources (quasars, galaxies, supernovae, FRBs, GRBs, and GWs), lenses (early- and late-type galaxies, groups, and clusters), datasets (imaging, spectra, and lightcurves), and wavelengths. We first present the physical characteristics of the lens and source populations, highlighting relevant details for constructing targeted searches. Search techniques are described based on the main lensing feature that is required for the technique to work, namely one of: (i) an associated magnification, (ii) multiple spatially-resolved images, (iii) multiple redshifts, or (iv) a non-zero time delay between images. To use the current lens samples for science, and for the design of future searches, we list several selection biases that exist due to these discovery techniques. We conclude by discussing the future of lens searches in upcoming surveys and the new population of lenses that will be discovered., Comment: 54 pages, 15 figures, submitted to Space Science Reviews, Topical Collection "Strong Gravitational Lensing", eds. J. Wambsganss et al

We present new lensing frequency estimates for existing and forthcoming deep near-infrared surveys, including those from JWST and VISTA. The estimates are based on the JAdes extraGalactic Ultradeep Artificial Realisations (JAGUAR) galaxy catalogue accounting for the full photometry and morphologies for each galaxy. Due to the limited area of the JAGUAR simulations, they are less suited to wide-area surveys, however we also present extrapolations to the surveys carried out by Euclid and the Nancy Grace Roman Space Telescope. The methodology does not make assumptions on the nature of the lens itself and probes a wide range of lens masses. The lenses and sources are selected from the same catalogue and extend the analysis from the visible bands into the near-infrared. After generating realistic simulated lensed sources and selecting those that are detectable with SNR>20, we verify the lensing frequency expectations against published lens samples selected in the visible, finding them to be broadly consistent. We find that JWST could yield ~ 65 lensed systems in COSMOS-Web, of which ~ 25 per cent have source redshifts >4. Deeper, narrower programs (e.g. JADES-Medium) will probe more typical source galaxies (in flux and mass) but will find fewer systems (~ 25). Of the surveys we investigate, we find 55-80 per cent have detectable multiple imaging. Forthcoming NIR surveys will likely reveal new and diverse strong lens systems including lensed sources that are at higher redshift (JWST) and dustier, more massive and older (Euclid NISP) than those typically detected in the corresponding visible surveys., Comment: 14 pages, 9 figures, accepted for publication by MNRAS

In this study, we investigate the impact of microlensing on gravitational wave (GW) signals in the LIGO$-$Virgo sensitivity band. Microlensing caused by an isolated point lens, with (redshifted) mass ranging from $M_\mathrm{Lz}\in(1,10^5){\rm M}_\odot$ and impact parameter $y\in (0.01,~5)$, can result in a maximum mismatch of $\sim 30\%$ with their unlensed counterparts. When $y<1$, it strongly anti-correlates with the luminosity distance enhancing the detection horizon and signal-to-noise ratio (SNR). Biases in inferred source parameters are assessed, with in-plane spin components being the most affected intrinsic parameters. The luminosity distance is often underestimated, while sky-localisation and trigger times are mostly well-recovered. Study of a population of microlensed signals due to an isolated point lens primarily reveals: (i) using unlensed templates during the search causes fractional loss ($20\%$ to $30\%$) of potentially identifiable microlensed signals; (ii) the observed distribution of $y$ challenges the notion of its high improbability at low values ($y\lesssim 1$), especially for $y\lesssim 0.1$; (iii) Bayes factor analysis of the population indicates that certain region in $M_\mathrm{Lz}-y$ parameter space have a higher probability of being detected and accurately identified as microlensed. Notably, the microlens parameters for the most compelling candidate identified in previous microlensing searches, GW200208_130117, fall within a 1-sigma range of the aforementioned higher probability region. Identifying microlensing signatures from $M_\mathrm{Lz}<100~$M$_\odot$ remains challenging due to small microlensing effects at typical SNR values. Additionally, we also examined how microlensing from a population of microlenses influences the detection of strong lensing signatures in pairs of GW events, particularly in the posterior-overlap analysis., Comment: 24 pages, 18 figures. Accepted in MNRAS

Along their path from source to observer, gravitational waves may be gravitationally lensed by massive objects. This results in distortions of the observed signal which can be used to extract new information about fundamental physics, astrophysics, and cosmology. Searches for these distortions amongst the observed signals from the current detector network have already been carried out, though there have as yet been no confident detections. However, predictions of the observation rate of lensing suggest detection in the future is a realistic possibility. Therefore, preparations need to be made to thoroughly investigate the candidate lensed signals. In this work, we present some of the follow-up analyses and strategies that could be applied to assess the significance of such events and ascertain what information may be extracted about the lens-source system from such candidate signals by applying them to a number of O3 candidate events, even if these signals did not yield a high significance for any of the lensing hypotheses. For strongly-lensed candidates, we verify their significance using a background of simulated unlensed events and statistics computed from lensing catalogs. We also look for potential electromagnetic counterparts. In addition, we analyse in detail a candidate for a strongly-lensed sub-threshold counterpart that is identified by a new method. For microlensing candidates, we perform model selection using a number of lens models to investigate our ability to determine the mass density profile of the lens and constrain the lens parameters. We also look for millilensing signatures in one of the lensed candidates. Applying these additional analyses does not lead to any additional evidence for lensing in the candidates that have been examined. However, it does provide important insight into potential avenues to deal with high-significance candidates in future observations., Comment: 29 pages, 27 figures

We report the discovery of new lensed quasar candidates in the imaging data of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) DR4, covering $1\,310~{\rm deg}^2$ of the sky with seeing of $\approx0.6''$. In addition to two catalogs of MILLIQUAS and AllWISEAGN, which contain confirmed and candidate quasars, we preselect quasar sources using color cuts from the HSC ($grizy$) and unWISE ($W1+W2$) photometric data based on SDSS spectroscopic catalogs. We search for the presence of multiple point sources with similar color through the convolution of the Laplacian of the preselected quasar image cutouts with the Laplacian of the point spread function, resulting in a reduction of lens candidates from $1\,652\,329$ to $121\,511$ ($7.4\%$). After visual binary classification, we grade $6\,199$ ($0.4\%$) potential lenses on a scale of 0 to 3, with 3 indicating a lens and 0 indicating a non-lens. Finally we obtain 162 lens candidates with an average grade of $\geq2$, and among them, we successfully recover 18 known lenses. By fitting the light distribution and removing the known contaminants, we discover that 57 new systems contain at least two point sources and a galaxy in between, including 10 possible quadruply lensed quasars. This new sample of lens candidates exhibits a median separation of $1.26''$ and a magnitude limit of $i\approx22$. Spectroscopic or high-resolution imaging follow up on these newly discovered lensed quasar candidates will further allow their natures to be confirmed., Comment: 25 pages, 14 figures

Since the first detection of gravitational waves in 2015, gravitational-wave astronomy has emerged as a rapidly advancing field that holds great potential for studying the cosmos, from probing the properties of black holes to testing the limits of our current understanding of gravity. One important aspect of gravitational-wave astronomy is the phenomenon of gravitational lensing, where massive intervening objects can bend and magnify gravitational waves, providing a unique way to probe the distribution of matter in the universe, as well as finding applications to fundamental physics, astrophysics, and cosmology. However, current models for gravitational-wave millilensing - a specific form of lensing where small-scale astrophysical objects can split a gravitational wave signal into multiple copies - are often limited to simple isolated lenses, which is not realistic for complex lensing scenarios. In this paper, we present a novel phenomenological approach to incorporate millilensing in data analysis in a model-independent fashion. Our approach enables the recovery of arbitrary lens configurations without the need for extensive computational lens modeling, making it a more accurate and computationally efficient tool for studying the distribution of matter in the universe using gravitational-wave signals. When gravitational-wave lensing observations become possible, our method can provide a powerful tool for studying complex lens configurations, including dark matter subhalos and MACHOs., Comment: 12 pages, 8 figures

Pre-merger gravitational-wave (GW) sky-localisation of binary neutron star (BNS) and neutron star black hole (NSBH) coalescence events, would enable telescopes to capture precursors and electromagnetic (EM) emissions around the time of the merger. We propose a novel astrophysical scenario that could provide early-warning times of hours to days before coalescence with sub-arcsecond localisation, provided that these events are gravitationally lensed. The key idea is that if the BNS/NSBH is lensed, then so must the host galaxy identified via the EM counterpart. From the angular separation of the lensed host galaxy images, as well as its redshift and the (foreground) lens redshift, we demonstrate that we can predict the time delays assuming a standard lens model. Encouraged by the non-trivial upper limits on the detection rates of lensed BNS/NSBH mergers that we estimate for upcoming observing runs of the LIGO-Virgo-Kagra and third generation networks, we assess the feasibility and benefits of our method. To that end, we study the effect of limited angular resolution of the telescopes on our ability to predict the time delays. We find that with an angular resolution of $0.05''$, we can predict time delays of $> 1$ day with $1\sigma$ error-bar of $\mathcal{O}$(hours) at best. We also construct realistic time delay distributions of detectable lensed BNSs/NSBHs to forecast the early-warning times we might expect in the observing scenarios we consider., Comment: 10 pages, 5 figures

Strong gravitational lensing and microlensing of supernovae (SNe) are emerging as a new probe of cosmology and astrophysics in recent years. We provide an overview of this nascent research field, starting with a summary of the first discoveries of strongly lensed SNe. We describe the use of the time delays between multiple SN images as a way to measure cosmological distances and thus constrain cosmological parameters, particularly the Hubble constant, whose value is currently under heated debates. New methods for measuring the time delays in lensed SNe have been developed, and the sample of lensed SNe from the upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to provide competitive cosmological constraints. Lensed SNe are also powerful astrophysical probes. We review the usage of lensed SNe to constrain SN progenitors, acquire high-z SN spectra through lensing magnifications, infer SN sizes via microlensing, and measure properties of dust in galaxies. The current challenge in the field is the rarity and difficulty in finding lensed SNe. We describe various methods and ongoing efforts to find these spectacular explosions, forecast the properties of the expected sample of lensed SNe from upcoming surveys particularly the LSST, and summarize the observational follow-up requirements to enable the various scientific studies. We anticipate the upcoming years to be exciting with a boom in lensed SN discoveries., Comment: 48 pages, 18 figures; to be submitted to Space Science Reviews, Topical Collection "Strong Gravitational Lensing", eds. J. Wambsganss et al

We investigate the ability of human 'expert' classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25$\%$ of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabeled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, whilst arcs with $g$-band signal-to-noise less than $\sim$25 or Einstein radii less than $\sim$1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier's experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies., Comment: 16 pages, 20 Figures

When traveling from their source to the observer, gravitational waves can get deflected by massive objects along their travel path. When the lens is massive enough and the source aligns closely with the line-of-sight to the lens, the wave undergoes strong lensing, leading to several images with the same frequency evolution. These images are separated in time, magnified, and can undergo an overall phase shift. Searches for strongly-lensed gravitational waves are already ongoing. In essence, such searches look for events originating from the same sky location and having the same detector frame parameters. However, the agreement between these quantities can also happen by chance, leading to an important confusion background. To reduce the overlap between background and foreground, one can include lensing models, enforcing the lensing parameters to also be consistent with our expectations. In principle, such models should decrease the confusion background. However, when doing realistic searches, one does not know which model is the correct one. The use of an incorrect model could lead to the non-detection of genuinely lensed events. In this work, we investigate under realistic conditions how one can identify lensed events in the unlensed background. We focus on the impact of the addition of a model for the lens density profile and investigate the effect of potential errors in the modelling. We show that it is extremely difficult to identify lensing confidently without the addition of a lens model. We also show that slight variations in the profile of the lens model are tolerable but a model with incorrect assumptions about the underlying lens population causes significant errors compared to assuming no lens model at all. We also suggest some strategies to improve the confidence in the detection of strong lensing for gravitational waves., Comment: 21 pages, 10 figures

With the increase in the number of observed gravitational wave (GW) signals, detecting strongly lensed GWs by galaxies has become a real possibility. Lens galaxies also contain microlenses (e.g., stars and black holes), introducing further frequency-dependent modulations in the strongly lensed GW signal within the LIGO frequency range. The multiple lensed signals in a given lens system have different underlying macro-magnifications ($|\mu|$) and are located in varied microlens densities ($\Sigma_\bullet$), leading to different levels of microlensing distortions. This work quantifies the fraction of strong lens systems affected by microlensing using realistic mock observations. We study 50 quadruply imaged systems (quads) by generating 50 realizations for each lensed signal. However, our conclusions are equally valid for lensed signals in doubly imaged systems (doubles). The lensed signals studied here have $|\mu|\sim[0.5, 10]$ and $\Sigma_\bullet\sim[10, 10^3]~{\rm M}_\odot/{\rm pc^2}$. We find that the microlensing effects are more sensitive to the macro-magnification than the underlying microlens density, even if the latter exceeds $10^3~{\rm M}_\odot/{\rm pc^2}$. The mismatch between lensed and unlensed GW signals rarely exceeds $1\%$ for nearly all binary black hole sources in the total mass range [10 M$_\odot$, 200 M$_\odot$]. This implies that microlensing is not expected to affect the detection or the parameter estimation of such signals and does not pose any further challenges in identifying the different lensed counterparts when macro-magnification is ${\leq}10$. Such a magnification cut is expected to be satisfied by ${\sim}50\%$ of the detectable pairs in quads and ${\sim}90\%$ of the doubles in the fourth observing run of the LIGO-Virgo detector network., Comment: 12 pages, 9 figures; Comments welcome

We conduct a search for galaxy-scale strong gravitational lens systems in Data Release 4 of the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP), consisting of data taken up to the S21A semester. We select 103191 luminous red galaxies from the Baryon Oscillation Spectroscopic Survey (BOSS) sample that have deep multiband imaging from the HSC SSP and use the YattaLens algorithm to automatically identify lens candidates with blue arc-like features. The candidates are visually inspected and graded based on their likelihood of being a lens. We find 8 definite lenses, 28 probable lenses, and 138 possible lenses. The new lens candidates generally have lens redshifts in the range $0.3 \lesssim z_{\mathrm{L}} \lesssim 0.9$, a key intermediate redshift range to study the evolution of galaxy structure. Follow-up spectroscopy will confirm these new lenses and measure source redshifts to enable detailed lens modeling., Comment: Accepted for publication in PASJ; 12 pages, 4 figures, 2 tables

Blip glitches, a type of short-duration noise transient in the LIGO--Virgo data, are a nuisance for the binary black hole (BBH) searches. They affect the BBH search sensitivity significantly because their time-domain morphologies are very similar, and that creates difficulty in vetoing them. In this work, we construct a deep-learning neural network to efficiently distinguish BBH signals from blip glitches. We introduce sine-Gaussian projection (SGP) maps, which are projections of GW frequency-domain data snippets on a basis of sine-Gaussians defined by the quality factor and central frequency. We feed the SGP maps to our deep-learning neural network, which classifies the BBH signals and blips. Whereas the BBH signals are simulated, the blips used are taken from real data throughout our analysis. We show that our network significantly improves the identification of the BBH signals in comparison to the results obtained using traditional-$\chi^2$ and sine-Gaussian $\chi^2$. For example, our network improves the sensitivity by 75% at a false-positive rate of $10^{-2}$ for BBHs with total mass in the range $[80,140]~M_{\odot}$ and SNR in the range $[3,8]$. Also, it correctly identifies 95% of the real GW events in GWTC-3. The computation time for classification is a few minutes for thousands of SGP maps on a single core. With further optimisation in the next version of our algorithm, we expect a further reduction in the computational cost. Our proposed method can potentially improve the veto process in the LIGO--Virgo GW data analysis and conceivably support identifying GW signals in low-latency pipelines., Comment: 11 pages, 8 figures and 2 tables. Reviewed by LIGO Scientific Collaboration (LSC) with preprint number LIGO-P2100485

We present the Strong Lensing Science Collaboration's (SLSC) recommended observing targets for the science verification and science validation phases of commissioning. Our recommendations have been developed in collaboration with the Dark Energy Science Collaboration (DESC) Strong Lensing Topical Team. In summary, our key recommendations are as follows: (1) Prioritize fields that span the full range of declination observable from Cerro Pachon during the engineering focused Science Verification phase of commissioning, before concentrating on equatorial fields for the Science Validation surveys. (2) Observe quadruply lensed quasars as the ultimate test of the Active Optics system towards the end of the Science Verification phase of commissioning. These systems are the strongest tests known for delivered image quality. (3) Prioritize science validation survey fields (both single deep pointings and wide fields) that have been searched thoroughly by precursor surveys for strong lenses. (4) The optimal wide (~100 degree^2) science validation field would include the CFHT-LS W4 field, and overlap with the SDSS Stripe 82, DES-SN, KIDS and HSC-SSP fields. (5) The optimal single pointing science validation fields are the XMM-LSS and COSMOS Deep Drilling Fields, the equatorial Hubble Frontier Fields galaxy clusters, and strongly lensed quasars with measured time delays that are well-matched to commissioning timescales., Comment: 8 pages, 1 figure

As the number of detected gravitational wave sources increase with increasing sensitivity of the gravitational wave observatories, observing strongly lensed pair of events will become a real possibility. Lensed GW events will have very accurately measured time delays, as well as magnification ratios. If the corresponding lens system can be identified electromagnetically and the redshifts of the lens and the host galaxy can be measured, such events can be used to constrain important astrophysical parameters of the lens system. As most lensing events will have image separations that will be significantly smaller than the GW event localization uncertainties, it is important that we develop diagnostics that will aid in the robust identification of such lensed events. We define a new statistic based on the joint probability of lensing observables that can be used to discriminate lensed pair of events from the unlensed ones. To this end, we carry out simulations of lensed GW events to infer the distribution of the relative time delays, relative magnifications sub-divided by the type of lensed images. We compare this distribution to a similar one obtained for random unlensed event pairs. Our improved statistic can be used to improve the existing ranking approach adopted by the search pipelines in order to down-select event pairs for joint parameter estimates. The distributions we obtain can further be used to define more informative priors in joint parameter estimation analyses for candidate lensed events., Comment: 8 pages, 6 figures

With increasing sensitivities of the current ground-based gravitational-wave (GW) detectors, the prospects of detecting a strongly lensed GW signal are going to be high in the coming years. When such a signal passes through an intervening lensing galaxy or galaxy cluster, the embedded stellar-mass microlenses lead to interference patterns in the signal that may leave observable signatures. In this work, we present an extensive study of these wave effects in the LIGO/Virgo frequency band ($10$-$10^4$ Hz) due to the presence of the microlens population in galaxy-scale lenses for the first time. We consider a wide range of strong lensing (macro) magnifications and the corresponding surface microlens densities found in lensing galaxies and use them to generate realisations of the amplification factor. The methodologies for simulating amplification curves for both types of images (minima and saddle points) are also discussed. We then study how microlensing is broadly affected by the parameters like macro-magnifications, stellar densities, the initial mass function (IMF), types of images, and microlens distribution around the source. In general, with increasing macro-magnification values, the effects of microlensing become increasingly significant regardless of other parameters. Mismatch analysis between the lensed and the unlensed GW waveforms from chirping binaries suggests that, while inferring the source parameters, microlensing can not be neglected for macro-magnification $\gtrsim 15$. Furthermore, for extremely high macro-magnifications $\gtrsim 100$, the mismatch can even exceed $5\%$, which can result in both a missed detection and, consequently, a missed lensed signal., Comment: 18 pages, 13 figures. v2: Added section 5.7; updated section 5.6; typos corrected; references updated

We present spectroscopic confirmation of three new two-image gravitationally lensed quasars, compiled from existing strong lens and X-ray catalogs. Images of HSC J091843.27$-$022007.5 show a red galaxy with two blue point sources at either side, separated by 2.26 arcsec. This system has a source and a lens redshifts $z_s=0.804$ and $z_{\ell}=0.459$, respectively, as obtained by our follow-up spectroscopic data. CXCO J100201.50$+$020330.0 shows two point sources separated by 0.85 arcsec on either side of an early-type galaxy. The follow-up spectroscopic data confirm the fainter quasar has the same redshift with the brighter quasar from the SDSS fiber spectrum at $z_s=2.016$. The deflecting foreground galaxy is a typical early-type galaxy at a redshift of $z_{\ell}=0.439$. SDSS J135944.21$+$012809.8 has two point sources with quasar spectra at the same redshift $z_s=1.096$, separated by 1.05 arcsec, and fits to the HSC images confirm the presence of a galaxy between these. These discoveries demonstrate the power of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP)'s deep imaging and wide sky coverage. Combined with existing X-ray source catalogues and follow-up spectroscopy, the HSC-SSP provides us unique opportunities to find multiple-image quasars lensed by a foreground galaxy., Comment: Accepted for publication in MNRAS, 7 pages, 3 figures (add credit to Claude Cornen)

Strong lenses are extremely useful probes of the distribution of matter on galaxy and cluster scales at cosmological distances, but are rare and difficult to find. The number of currently known lenses is on the order of 1,000. We wish to use crowdsourcing to carry out a lens search targeting massive galaxies selected from over 442 square degrees of photometric data from the Hyper Suprime-Cam (HSC) survey. We selected a sample of $\sim300,000$ galaxies with photometric redshifts in the range $0.2 < z_{phot} < 1.2$ and photometrically inferred stellar masses $\log{M_*} > 11.2$. We crowdsourced lens finding on this sample of galaxies on the Zooniverse platform, as part of the Space Warps project. The sample was complemented by a large set of simulated lenses and visually selected non-lenses, for training purposes. Nearly 6,000 citizen volunteers participated in the experiment. In parallel, we used YattaLens, an automated lens finding algorithm, to look for lenses in the same sample of galaxies. Based on a statistical analysis of classification data from the volunteers, we selected a sample of the most promising $\sim1,500$ candidates which we then visually inspected: half of them turned out to be possible (grade C) lenses or better. Including lenses found by YattaLens or serendipitously noticed in the discussion section of the Space Warps website, we were able to find 14 definite lenses, 129 probable lenses and 581 possible lenses. YattaLens found half the number of lenses discovered via crowdsourcing. Crowdsourcing is able to produce samples of lens candidates with high completeness and purity, compared to currently available automated algorithms. A hybrid approach, in which the visual inspection of samples of lens candidates pre-selected by discovery algorithms and/or coupled to machine learning is crowdsourced, will be a viable option for lens finding in the 2020s., Comment: Published version

We report the largest sample of candidate strong gravitational lenses belonging to the Survey of Gravitationally-lensed Objects in HSC Imaging for group-to-cluster scale (SuGOHI-c) systems. These candidates are compiled from the S18A data release of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) Survey. We visually inspect $\sim39,500$ galaxy clusters, selected from several catalogs, overlapping with the Wide, Deep, and UltraDeep fields, spanning the cluster redshift range $0.05

A double source plane (DSP) system is a precious probe for the density profile of distant galaxies and cosmological parameters. However, these measurements could be affected by the surrounding environment of the lens galaxy. Thus, it is important to evaluate the cluster-scale mass for detailed mass modeling. We observed the {\it Eye of Horus}, a DSP system discovered by the Subaru HSC--SSP, with XMM--Newton. We detected two X-ray extended emissions, originating from two clusters, one centered at the {\it Eye of Horus}, and the other located $\sim100$ arcsec northeast to the {\it Eye of Horus}. We determined the dynamical mass assuming hydrostatic equilibrium, and evaluated their contributions to the lens mass interior of the Einstein radius. The contribution of the former cluster is $1.1^{+1.2}_{-0.5}\times10^{12}~M_{\odot}$, which is $21-76\%$ of the total mass within the Einstein radius. The discrepancy is likely due to the complex gravitational structure along the line of sight. On the other hand, the contribution of the latter cluster is only $\sim2\%$ on the {\it Eye of Horus}. Therefore, the influence associated with this cluster can be ignored., Comment: 8 pages, 6 figures

Strong gravitationally lensed quasars provide powerful means to study galaxy evolution and cosmology. We use Chitah to hunt for new lens systems in the Hyper Suprime$-$Cam Subaru Strategic Program (HSC SSP) S16A. We present 46 lens candidates, of which 3 are previously known. Including 2 additional lenses found by YattaLens, we obtain X-shooter spectra of 6 promising candidates for lens confirmation and redshift measurements. We report new spectroscopic redshift measurements for both the lens and source galaxies in 4 lens systems. We apply the lens modeling software Glee to model our 6 X-shooter lenses uniformly. Through our analysis of the HSC images, we find that HSCJ022622$-$042522, HSCJ115252$+$004733, and HSCJ141136$-$010216 have point-like lensed images, and that the lens light distribution is well aligned with mass distribution within 6 deg. Thanks to the X-shooter spectra, we estimate fluxes on the Baldwin- Phillips-Terlevich (BPT) diagram, and find that HSCJ022622$-$042522 has a probable quasar source, based on the upper limit of the Nii flux intensity. We also measure the FWHM of Ly$\alpha$ emission of HSCJ141136$-$010216 to be $\sim$254 km/s, showing that it is a probable Lyman-$\alpha$ emitter., Comment: 13 pages, 7 figures

Gravitationally lensed quasars are useful for studying astrophysics and cosmology, and enlarging the sample size of lensed quasars is important for multiple studies. In this work, we develop a lens search algorithm for four-image (quad) lensed quasars based on their time variability. In the development of the lens search algorithm, we constructed a pipeline simulating multi-epoch images of lensed quasars in cadenced surveys, accounting for quasar variabilities, quasar hosts, lens galaxies, and the PSF variation. Applying the simulation pipeline to the Hyper Suprime-Cam (HSC) transient survey, we generated HSC-like difference images of the mock lensed quasars from Oguri & Marshall's lens catalog. We further developed a lens search algorithm that picks out variable objects as lensed quasar candidates based on their spatial extent in the difference images. We tested our lens search algorithm with the mock lensed quasars and variable objects from the HSC transient survey. Using difference images from multiple epochs, our lens search algorithm achieves a high true-positive rate (TPR) of 90.1% and a low false-positive rate (FPR) of 2.3% for the bright quads with wide separation. With a preselection of the number of blobs in the difference image, we obtain a TPR of 97.6% and a FPR of 2.6% for the bright quads with wide separation. Even when difference images are only available in one single epoch, our lens search algorithm can still detect the bright quads with wide separation at high TPR of 97.6% and low FPR of 2.4% in the optimal seeing scenario, and at TPR of $\sim94%$ and FPR of $\sim5%$ in typical scenarios. Therefore, our lens search algorithm is promising and is applicable to ongoing and upcoming cadenced surveys, particularly the HSC transient survey and the Rubin Observatory Legacy Survey of Space and Time, for finding new lensed quasar systems. [abridged], Comment: 15 pages, 11 figures

We report the serendipitous discovery of HSC J0904$-$0102, a quadruply-lensed Lyman break galaxy (LBG) in the Survey of Gravitationally-lensed Objects in Hyper Suprime-Cam Imaging (SuGOHI). Owing to its point-like appearance, the source was thought to be a lensed active galactic nucleus. We obtained follow-up spectroscopic data with the Gemini Multi-Object Spectrographs on the Gemini South Telescope, which confirmed this to be a lens system. The deflecting foreground galaxy is a typical early-type galaxy at a high redshift of $z_{\ell} = 0.957$ with stellar velocity dispersion $\sigma_v=259\pm56$ km~s$^{-1}$. The lensed source is identified as an LBG at $z_{\rm s} = 3.403$, based on the sharp drop bluewards of Ly$\alpha$ and other absorption features. A simple lens mass model for the system, assuming a singular isothermal ellipsoid, yields an Einstein radius of $\theta_{\rm Ein} = 1. 23^{\prime\prime}$ and a total mass within the Einstein radius of $M_{\rm Ein} = (5.55\pm 0.24) \times 10^{11}M_{\odot}$ corresponding to a velocity dispersion of $\sigma_{\rm SIE}= 283\pm 3$ km~s$^{-1}$, which is in good agreement with the value derived spectroscopically. The most isolated lensed LBG image has a magnification of $\sim 6.5$. In comparison with other lensed LBGs and typical $z\sim4$ LBG populations, HSC J0904$-$0102 is unusually compact, an outlier at $>2\sigma$ confidence. Together with a previously discovered SuGOHI lens, HSC J1152$+$0047, that is similarly compact, we believe that the HSC Survey is extending LBG studies down to smaller galaxy sizes., Comment: Accepted for publication on MNRAS, 10 pages, 6 figures

Context: The determination of the stellar initial mass function (IMF) of massive galaxies is one of the open problems in cosmology. Strong gravitational lensing is one of the few methods that allow us to constrain the IMF outside of the Local Group. Aims: The goal of this study is to statistically constrain the distribution in the IMF mismatch parameter, defined as the ratio between the true stellar mass of a galaxy and that inferred assuming a reference IMF, of massive galaxies from the BOSS CMASS sample. Methods: We take 23 strong lenses drawn from the CMASS sample, measure their Einstein radii and stellar masses using multi-band photometry from the Hyper Suprime-Cam survey, then fit a model distribution for the IMF mismatch parameter and dark matter halo mass to the whole sample. We use a prior on halo mass from weak lensing measurements and account for strong lensing selection effects in our model. Results: Assuming an NFW density profile for the dark matter distribution, we infer a value $\mu_{\mathrm{IMF}} = -0.04\pm0.11$ for the average base-10 logarithm of the IMF mismatch parameter, defined with respect to a Chabrier IMF. A Salpeter IMF is in tension with our measurements. Conclusions: Our results are consistent with a scenario in which the region of massive galaxies where the IMF normalization is significantly heavier than that of the Milky Way is much smaller than the scales $5\sim10$~kpc probed by the Einstein radius of the lenses in our sample, as recent spatially resolved studies of the IMF in massive galaxies suggest. The MCMC chains describing the posterior probability distribution of the model are available online, together with the code used to obtain them., Comment: Accepted for publication on A&A. 21 pages, 11 figures

Strong lensing of active galactic nuclei in the radio can result in razor-thin arcs, with a thickness of less than a milli-arcsecond, if observed at the resolution achievable with very long baseline interferometry (VLBI). Such razor-thin arcs provide a unique window on the coarseness of the matter distribution between source and observer. In this paper, we investigate to what extent such razor-thin arcs can constrain the number density and mass function of `free-floating' black holes, defined as black holes that do not, or no longer, reside at the centre of a galaxy. These can be either primordial in origin or arise as by-products of the evolution of super-massive black holes in galactic nuclei. When sufficiently close to the line of sight, free-floating black holes cause kink-like distortions in the arcs, which are detectable by eye in the VLBI images as long as the black hole mass exceeds $\sim 1000$ Solar masses. Using a crude estimate for the detectability of such distortions, we analytically compute constraints on the matter density of free-floating black holes resulting from null-detections of distortions along a realistic, fiducial arc, and find them to be comparable to those from quasar milli-lensing. We also use predictions from a large hydrodynamical simulation for the demographics of free-floating black holes that are not primordial in origin, and show that their predicted mass density is roughly four orders of magnitude below the constraints achievable with a single razor-thin arc., Comment: 17 pages, 13 figures, 1 table, comments welcome

We investigate the local and line-of-sight overdensities of strong gravitational lens galaxies using wide-area multiband imaging from the Hyper Suprime-Cam Subaru Strategic Program. We present 41 new definite or probable lens candidates discovered in Data Release 2 of the survey. Using a combined sample of 87 galaxy-scale lenses out to a lens redshift of $z_{\mathrm{L}} \sim 0.8$, we compare galaxy number counts in lines of sight toward known and newly-discovered lenses in the survey to those of a control sample consisting of random lines of sight. We also compare the local overdensity of lens galaxies to a sample of "twin" galaxies that have a similar redshift and velocity dispersion to test whether lenses lie in different environments from similar non-lens galaxies. We find that lens fields contain higher number counts of galaxies compared to the control fields, but this effect arises from the local environment of the lens. Once galaxies in the lens plane are removed, the lens lines of sight are consistent with the control sample. The local environments of the lenses are overdense compared to the control sample, and are slightly overdense compared to those of the twin sample, although the significance is marginal. There is no significant evidence of the evolution of the local overdensity of lens environments with redshift., Comment: Accepted for publication in the Astrophysical Journal; 22 pages, 10 figures, 9 tables

We present results of a systematic search for gravitationally lensed quasars in Pan-STARRS1. Our final sample of candidates comprises of 91 systems, not including 25 rediscovered lensed quasars and quasar pairs. In the absence of spectroscopy to verify the lensing nature of the candidates, the main sources of contaminants are likely to be quasar pairs, which we consider to be a byproduct of our work, and a smaller number of quasar$+$star associations. Amongst the independently discovered quads is 2M1134$-$2103, for which we obtained spectroscopy for the first time, finding a redshift of 2.77 for the quasar. There is evidence for microlensing in at least one image. We perform detailed mass modeling of this system using archival imaging data, and find that the unusually large shear responsible for the diamond-like configuration can be attributed mainly to a faint companion $\sim 4''$ away, and to a galaxy group/cluster $\sim 30''$ away. We also set limits of $z\sim0.5-1.5$ on the redshift of the lensing galaxy, based on its brightness, the image separation of the lensed images, and an analysis of the observed photometric flux ratios., Comment: Accepted by MNRAS. Based on feedback from the referee, the final sample of candidates is significantly different from the one in the previous submission. 15 pages + 4 pages of appendix, 7 figures, 3 tables

We present a catalogue of 38 spectroscopically detected strong galaxy-galaxy gravitational lens candidates identified in the Sloan Digital Sky Survey IV (SDSS-IV). We were able to simulate narrow-band images for 8 of them demonstrating evidence of multiple images. Two of our systems are compound lens candidates, each with 2 background source-planes. One of these compound systems shows clear lensing features in the narrow-band image. Our sample is based on 2812 galaxies observed by the Mapping Nearby Galaxies at APO (MaNGA) integral field unit (IFU). This Spectroscopic Identification of Lensing Objects (SILO) survey extends the methodology of the Sloan Lens ACS Survey (SLACS) and BOSS Emission-Line Survey (BELLS) to lower redshift and multiple IFU spectra. We searched ~ 1.5 million spectra, of which 3065 contained multiple high signal-to-noise background emission-lines or a resolved [OII] doublet, that are included in this catalogue. Upon manual inspection, we discovered regions with multiple spectra containing background emission-lines at the same redshift, providing evidence of a common source-plane geometry which was not possible in previous SLACS and BELLS discovery programs. We estimate more than half of our candidates have an Einstein radius > 1.7", which is significantly greater than seen in SLACS and BELLS. These larger Einstein radii produce more extended images of the background galaxy increasing the probability that a background emission-line will enter one of the IFU spectroscopic fibres, making detection more likely., Comment: Accepted for publication in MNRAS, March 8, 2018. In press. 16 pages, 5 figures, 4 tables

Strong gravitational lenses with measured time delay are a powerful tool to measure cosmological parameters, especially the Hubble constant ($H_0$). Recent studies show that by combining just three multiply-imaged AGN systems, one can determine $H_0$ to 2.4% precision. Furthermore, the number of time-delay lens systems is growing rapidly, enabling the determination of $H_0$ to 1% precision in the near future. However, as the precision increases it is important to ensure that systematic errors and biases remain subdominant. For this purpose, challenges with simulated datasets are a key component in this process. Following the experience of the past challenge on time delay, where it was shown that time delays can indeed be measured precisely and accurately at the sub-percent level, we now present the "Time Delay Lens Modeling Challenge" (TDLMC). The goal of this challenge is to assess the present capabilities of lens modeling codes and assumptions and test the level of accuracy of inferred cosmological parameters given realistic mock datasets. We invite scientists to model a set of simulated HST observations of 50 mock lens systems. The systems are organized in rungs, with the complexity and realism increasing going up the ladder. The goal of the challenge is to infer $H_0$ for each rung, given the HST images, the time delay, and stellar velocity dispersion of the deflector for a fixed background cosmology. The TDLMC challenge starts with the mock data release on 2018 January 8th. The deadline for blind submission is different for each rung. The deadline for Rung 0-1 is 2018 September 8; the deadline for Rung 2 is 2019 April 8 and the one for Rung 3 is 2019 September 8. This first paper gives an overview of the challenge including the data design, and a set of metrics to quantify the modeling performance and challenge details., Comment: 6 pages, 2 figures, submitted to MNRAS

We report modelling follow-up of recently-discovered gravitational-lens candidates in the Canada France Hawaii Telescope Legacy Survey. Lens modelling was done by a small group of specially-interested volunteers from the SpaceWarps citizen-science community who originally found the candidate lenses. Models are categorised according to seven diagnostics indicating (a) the image morphology and how clear or indistinct it is, (b) whether the mass map and synthetic lensed image appear to be plausible, and (c) how the lens-model mass compares with the stellar mass and the abundance-matched halo mass. The lensing masses range from ~10^11 Msun to >10^13 Msun. Preliminary estimates of the stellar masses show a smaller spread in stellar mass (except for two lenses): a factor of a few below or above ~10^11 Msun. Therefore, we expect the stellar-to-total mass fraction to decline sharply as lensing mass increases. The most massive system with a convincing model is J1434+522 (SW05). The two low-mass outliers are J0206-095 (SW19) and J2217+015 (SW42); if these two are indeed lenses, they probe an interesting regime of very low star-formation efficiency. Some improvements to the modelling software (SpaghettiLens), and discussion of strategies regarding scaling to future surveys with more and frequent discoveries, are included., Comment: 16 pages, 10 figures, 1 table, online supplement table_1.csv contains additional detailed numbers shown in table 1 and figure 7

We train and apply convolutional neural networks, a machine learning technique developed to learn from and classify image data, to Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging for the identification of potential strong lensing systems. An ensemble of four convolutional neural networks was trained on images of simulated galaxy-galaxy lenses. The training sets consisted of a total of 62,406 simulated lenses and 64,673 non-lens negative examples generated with two different methodologies. The networks were able to learn the features of simulated lenses with accuracy of up to 99.8% and a purity and completeness of 94-100% on a test set of 2000 simulations. An ensemble of trained networks was applied to all of the 171 square degrees of the CFHTLS wide field image data, identifying 18,861 candidates including 63 known and 139 other potential lens candidates. A second search of 1.4 million early type galaxies selected from the survey catalog as potential deflectors, identified 2,465 candidates including 117 previously known lens candidates, 29 confirmed lenses/high-quality lens candidates, 266 novel probable or potential lenses and 2097 candidates we classify as false positives. For the catalog-based search we estimate a completeness of 21-28% with respect to detectable lenses and a purity of 15%, with a false-positive rate of 1 in 671 images tested. We predict a human astronomer reviewing candidates produced by the system would identify ~20 probable lenses and 100 possible lenses per hour in a sample selected by the robot. Convolutional neural networks are therefore a promising tool for use in the search for lenses in current and forthcoming surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope., Comment: 16 pages, 8 figures. Accepted by MNRAS

The Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) is an excellent survey for the search for strong lenses, thanks to its area, image quality and depth. We use three different methods to look for lenses among 43,000 luminous red galaxies from the Baryon Oscillation Spectroscopic Survey (BOSS) sample with photometry from the S16A internal data release of the HSC SSP. The first method is a newly developed algorithm, named YATTALENS, which looks for arc-like features around massive galaxies and then estimates the likelihood of an object being a lens by performing a lens model fit. The second method, CHITAH, is a modeling-based algorithm originally developed to look for lensed quasars. The third method makes use of spectroscopic data to look for emission lines from objects at a different redshift from that of the main galaxy. We find 15 definite lenses, 36 highly probable lenses and 282 possible lenses. Among the three methods, YATTALENS, which was developed specifically for this problem, performs best in terms of both completeness and purity. Nevertheless five highly probable lenses were missed by YATTALENS but found by the other two methods, indicating that the three methods are highly complementary. Based on these numbers we expect to find $\sim$300 definite or probable lenses by the end of the HSC SSP., Comment: Published on PASJ. 17 pages, 8 figures. Image quality of Figures 6 and 7 has been degraded due to arXiv file size limit. Full quality versions can be found at http://member.ipmu.jp/alessandro.sonnenfeld/sugohi1_candidates.html

We report on SPT-CLJ2011-5228, a giant system of arcs created by a cluster at $z=1.06$. The arc system is notable for the presence of a bright central image. The source is a Lyman Break galaxy at $z_s=2.39$ and the mass enclosed within the 14 arc second radius Einstein ring is $10^{14.2}$ solar masses. We perform a full light profile reconstruction of the lensed images to precisely infer the parameters of the mass distribution. The brightness of the central image demands that the central total density profile of the lens be shallow. By fitting the dark matter as a generalized Navarro-Frenk-White profile---with a free parameter for the inner density slope---we find that the break radius is $270^{+48}_{-76}$ kpc, and that the inner density falls with radius to the power $-0.38\pm0.04$ at 68 percent confidence. Such a shallow profile is in strong tension with our understanding of relaxed cold dark matter halos; dark matter only simulations predict the inner density should fall as $r^{-1}$. The tension can be alleviated if this cluster is in fact a merger; a two halo model can also reconstruct the data, with both clumps (density going as $r^{-0.8}$ and $r^{-1.0}$) much more consistent with predictions from dark matter only simulations. At the resolution of our Dark Energy Survey imaging, we are unable to choose between these two models, but we make predictions for forthcoming Hubble Space Telescope imaging that will decisively distinguish between them., Comment: 13 Pages. Accepted for publication in ApJ