Gravitational Lens Candidates Research Articles

Astronomaly at scale: searching for anomalies amongst 4 million galaxies

ABSTRACT Modern astronomical surveys are producing data sets of unprecedented size and richness, increasing the potential for high-impact scientific discovery. This possibility, coupled with the challenge of exploring a large number of sources, has led to the development of novel machine-learning-based anomaly detection approaches, such as astronomaly. For the first time, we test the scalability of astronomaly by applying it to almost 4 million images of galaxies from the Dark Energy Camera Legacy Survey. We use a trained deep learning algorithm to learn useful representations of the images and pass these to the anomaly detection algorithm isolation forest, coupled with astronomaly’s active learning method, to discover interesting sources. We find that data selection criteria have a significant impact on the trade-off between finding rare sources such as strong lenses and introducing artefacts into the data set. We demonstrate that active learning is required to identify the most interesting sources and reduce artefacts, while anomaly detection methods alone are insufficient. Using astronomaly, we find 1635 anomalies among the top 2000 sources in the data set after applying active learning, including eight strong gravitational lens candidates, 1609 galaxy merger candidates, and 18 previously unidentified sources exhibiting highly unusual morphology. Our results show that by leveraging the human–machine interface, astronomaly is able to rapidly identify sources of scientific interest even in large data sets.

The impact of human expert visual inspection on the discovery of strong gravitational lenses

ABSTRACT 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 per cent 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 unlabelled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, while arcs with g-band signal to noise less than ∼25 or Einstein radii less than ∼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.

DenseLens – Using DenseNet ensembles and information criteria for finding and rank-ordering strong gravitational lenses

ABSTRACT Convolutional neural networks (CNNs) are the state-of-the-art technique for identifying strong gravitational lenses. Although they are highly successful in recovering genuine lens systems with a high true-positive rate, the unbalanced nature of the data set (lens systems are rare), still leads to a high false positive rate. For these techniques to be successful in upcoming surveys (e.g. with Euclid) most emphasis should be set on reducing false positives, rather than on reducing false negatives. In this paper, we introduce densely connected neural networks (DenseNets) as the CNN architecture in a new pipeline-ensemble model containing an ensemble of classification CNNs and regression CNNs to classify and rank-order lenses, respectively. We show that DenseNets achieve comparable true positive rates but considerably lower false positive rates (when compared to residual networks; ResNets). Thus, we recommend DenseNets for future missions involving large data sets, such as Euclid, where low false positive rates play a key role in the automated follow-up and analysis of large numbers of strong gravitational lens candidates when human vetting is no longer feasible.

Hubble Asteroid Hunter

Context. The Hubble Space Telescope (HST) archives constitute a rich dataset of high-resolution images to mine for strong gravitational lenses. While many HST programmes specifically target strong lenses, they can also be present by coincidence in other HST observations. Aims. Our aim is to identify non-targeted strong gravitational lenses, without any prior selection on the lens properties, in almost two decades of images from the ESA HST archive (eHST). Methods. We used crowdsourcing on the Hubble Asteroid Hunter (HAH) citizen science project to identify strong lenses, along with asteroid trails, in publicly available large field-of-view HST images. We visually inspected 2354 objects tagged by citizen scientists as strong lenses to clean the sample and identify the genuine lenses. Results. We report the detection of 252 strong gravitational lens candidates, which were not the primary targets of the HST observations. A total of 198 of them are new, not previously reported by other studies, consisting of 45 A grades, 74 B grades and 79 C grades. The majority are galaxy-galaxy configurations. The newly detected lenses are, on average, 1.3 magnitudes fainter than previous HST searches. This sample of strong lenses with high-resolution HST imaging is ideal to follow up with spectroscopy for lens modelling and scientific analyses. Conclusions. This paper presents the unbiased search of lenses that enabled us to find a wide variety of lens configurations, including exotic lenses. We demonstrate the power of crowdsourcing in visually identifying strong lenses and the benefits of exploring large archival datasets. This study shows the potential of using crowdsourcing in combination with artificial intelligence for the detection and validation of strong lenses in future large-scale surveys such as ESA’s Euclid mission or in James Webb Space Telescope (JWST) archival images.

SDSS-IV MaNGA: a catalogue of spectroscopically detected strong galaxy–galaxy lens candidates

ABSTRACT We spectroscopically detected candidate emission-lines of 8 likely, 17 probable, and 69 possible strong galaxy–galaxy gravitational lens candidates found within the spectra of $\approx 10\, 000$ galaxy targets contained within the completed Mapping of Nearby Galaxies at Apache Point Observatory survey. This search is based upon the methodology of the Spectroscopic Identification of Lensing Objects project, which extends the spectroscopic detection methods of the BOSS Emission-Line Lensing Survey and the Sloan Lens ACS Survey. We scanned the co-added residuals that we constructed from stacks of foreground subtracted row-stacked-spectra so a sigma-clipping method can be used to reject cosmic rays and other forms of transients that impact only a small fraction of the combined exposures. We also constructed narrow-band images from the signal to noise of the co-added residuals to observe signs of lensed source images. We also use several methods to compute the probable strong lensing regime for each candidate lens to determine which candidate background galaxies may reside sufficiently near the galaxy centre for strong lensing to occur. We present the spectroscopic redshifts within a value-added catalogue (VAC) for data release 17 (DR17) of SDSS-IV. We also present the lens candidates, spectroscopic data, and narrow-band images within a VAC for DR17. High resolution follow-up imaging of these lens candidates are expected to yield a sample of confirmed grade-A lenses with sufficient angular size to probe possible discrepancies between the mass derived from a best-fitting lens model, and the dynamical mass derived from the observed stellar velocities.

Mining for Strong Gravitational Lenses with Self-supervised Learning

We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys’ Data Release 9. Targeting the identification of new strong gravitational lens candidates, we first create a rapid similarity search tool to discover new strong lenses given only a single labeled example. We then show how training a simple linear classifier on the self-supervised representations, requiring only a few minutes on a CPU, can automatically classify strong lenses with great efficiency. We present 1192 new strong lens candidates that we identified through a brief visual identification campaign and release an interactive web-based similarity search tool and the top network predictions to facilitate crowd-sourcing rapid discovery of additional strong gravitational lenses and other rare objects: github.com/georgestein/ssl-legacysurvey.

Deep Extragalactic VIsible Legacy Survey: Data Release 1 blended spectra search for candidate strong gravitational lenses

ABSTRACT Here, we present a catalogue of blended spectra in Data Release 1 of the Deep Extragalactic VIsible Legacy Survey (DEVILS) on the Anglo-Australian Telescope. Of the 23 197 spectra, 181 showed signs of a blend of redshifts and spectral templates. We examine these blends in detail for signs of either a candidate strong lensing galaxy or a useful overlapping galaxy pair. One of the three DEVILS target fields, COSMOS (D10), is close to complete and it is fully imaged with Hubble Space Telescope Advanced Camera for Surveys, and we visually examine the 57 blended spectra in this field in the F814W postage stamps. Nine are classical strong lensing candidates with an elliptical as the lens, out to higher redshifts than any previous search with spectroscopic surveys such as Sloan Digital Sky Survey (SDSS) or Galaxy And Mass Assembly. The gravitational lens candidate success rate is similar to earlier such searches (0.1 per cent). Strong gravitational lenses identified with blended spectroscopy have typically shown a high success rate (>70 per cent), which make these interesting targets for future higher resolution lensing studies, monitoring for supernova cosmography, or searches for magnified atomic hydrogen signal.

A Catalog of the Most Optically Luminous Galaxies at z < 0.3: Super Spirals, Super Lenticulars, Super Post-mergers, and Giant Ellipticals

We present a catalog of the 1525 most optically luminous galaxies from the Sloan Digital Sky Survey (SDSS) with r-band luminosity L_r > 8L* and redshift z<0.3, including 84 super spirals, 15 super lenticulars, 14 super post-merger galaxies, and 1400 giant ellipticals. With mass in stars of 10^{11.3}-10^{12} M_sun, super spirals and lenticulars are the most massive disk galaxies currently known. The specific star formation rates of super spirals place them on or below the star-forming main sequence. They must have formed stars at a high rate throughout their history in order to grow their massive, gigantic stellar disks and maintain their blue u-r integrated colors. Their disks are red on the inside and blue on the outside, consistent with inside-out growth. They tend to have small bulge-to-total (B/T) r-band luminosity ratios, characteristic of disk building via minor mergers and cold accretion. A large percentage of super disk galaxies (41%) have double nuclei, double disks, or other signatures of ongoing mergers. Most (72%) are found in moderate to low density environments, while the rest are found at the outskirts of clusters. It is likely that super spirals survive in these environments because they continue to accrete cold gas and experience only minor mergers at late times, by virtue of their enormous masses and angular momenta. We suggest that super post-mergers are the product of super-spiral major mergers and may be the precursors of some giant elliptical galaxies found in low density environments. We present two new gravitational lens candidates in the Appendix.

Gaia GraL: Gaia DR2 Gravitational Lens Systems

Aims. In this work, we aim to provide a reliable list of gravitational lens candidates based on a search performed over the entire Gaia Data Release 2 (Gaia DR2). We also aim to show that the astrometric and photometric information coming from the Gaia satellite yield sufficient insights for supervised learning methods to automatically identify strong gravitational lens candidates with an efficiency that is comparable to methods based on image processing. Methods. We simulated 106 623 188 lens systems composed of more than two images, based on a regular grid of parameters characterizing a non-singular isothermal ellipsoid lens model in the presence of an external shear. These simulations are used as an input for training and testing our supervised learning models consisting of extremely randomized trees (ERTs). These trees are finally used to assign to each of the 2 129 659 clusters of celestial objects extracted from the Gaia DR2 a discriminant value that reflects the ability of our simulations to match the observed relative positions and fluxes from each cluster. Once complemented with additional constraints, these discriminant values allow us to identify strong gravitational lens candidates out of the list of clusters. Results. We report the discovery of 15 new quadruply-imaged lens candidates with angular separations of less than 6″ and assess the performance of our approach by recovering 12 of the 13 known quadruply-imaged systems with all their components detected in Gaia DR2 with a misclassification rate of fortuitous clusters of stars as lens systems that is below 1%. Similarly, the identification capability of our method regarding quadruply-imaged systems where three images are detected in Gaia DR2 is assessed by recovering 10 of the 13 known quadruply-imaged systems having one of their constituting images discarded. The associated misclassification rate varies between 5.83% and 20%, depending on the image we decided to remove.

Testing Convolutional Neural Networks for finding strong gravitational lenses in KiDS

Convolutional Neural Networks (ConvNets) are one of the most promising methods for identifying strong gravitational lens candidates in survey data. We present two ConvNet lens-finders which we have trained with a dataset composed of real galaxies from the Kilo Degree Survey (KiDS) and simulated lensed sources. One ConvNet is trained with single \textit{r}-band galaxy images, hence basing the classification mostly on the morphology. While the other ConvNet is trained on \textit{g-r-i} composite images, relying mostly on colours and morphology. We have tested the ConvNet lens-finders on a sample of 21789 Luminous Red Galaxies (LRGs) selected from KiDS and we have analyzed and compared the results with our previous ConvNet lens-finder on the same sample. The new lens-finders achieve a higher accuracy and completeness in identifying gravitational lens candidates, especially the single-band ConvNet. Our analysis indicates that this is mainly due to improved simulations of the lensed sources. In particular, the single-band ConvNet can select a sample of lens candidates with $\sim40\%$ purity, retrieving 3 out of 4 of the confirmed gravitational lenses in the LRG sample. With this particular setup and limited human intervention, it will be possible to retrieve, in future surveys such as Euclid, a sample of lenses exceeding in size the total number of currently known gravitational lenses.

Quasars Probing Quasars. X. The Quasar Pair Spectral Database

The rare close projection of two quasars on the sky provides the opportunity to study the host galaxy environment of a foreground quasar in absorption against the continuum emission of a background quasar. For over a decade the "Quasars probing quasars" series has utilized this technique to further the understanding of galaxy formation and evolution in the presence of a quasar at z>2, resolving scales as small as a galactic disc and from bound gas in the circumgalactic medium to the diffuse environs of intergalactic space. Presented here, is the public release of the quasar pair spectral database utilized in these studies. In addition to projected pairs at z>2, the database also includes quasar pair members at z<2, gravitational lens candidates and quasars closely separated in redshift that are useful for small-scale clustering studies. In total the database catalogs 5627 distinct objects, with 4083 lying within 5' of at least one other source. A spectral library contains 3582 optical and near-infrared spectra for 3028 of the cataloged sources. As well as reporting on 54 newly discovered quasar pairs, we outline the key contributions made by this series over the last ten years, summarize the imaging and spectroscopic data used for target selection, discuss the target selection methodologies, describe the database content and explore some avenues for future work. Full documentation for the spectral database, including download instructions, are supplied at the following URL http://specdb.readthedocs.io/en/latest/

LensFlow: A Convolutional Neural Network in Search of Strong Gravitational Lenses

Abstract In this work, we present our machine learning classification algorithm for identifying strong gravitational lenses from wide-area surveys using convolutional neural networks; LensFlow. We train and test the algorithm using a wide variety of strong gravitational lens configurations from simulations of lensing events. Images are processed through multiple convolutional layers that extract feature maps necessary to assign a lens probability to each image. LensFlow provides a ranking scheme for all sources that could be used to identify potential gravitational lens candidates by significantly reducing the number of images that have to be visually inspected. We apply our algorithm to the HST/ACS i-band observations of the COSMOS field and present our sample of identified lensing candidates. The developed machine learning algorithm is more computationally efficient and complimentary to classical lens identification algorithms and is ideal for discovering such events across wide areas from current and future surveys such as LSST and WFIRST.

SDSS-IV MaNGA: the spectroscopic discovery of strongly lensed galaxies

Abstract 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 eight of them demonstrating evidence of multiple images. Two of our systems are compound lens candidates, each with two 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 ratio background emission-lines or a resolved [O ii] 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 arcsec, 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.

Models of gravitational lens candidates from Space Warps CFHTLS

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.

The DES Bright Arcs Survey: Hundreds of Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey Science Verification and Year 1 Observations

Abstract We report the results of searches for strong gravitational lens systems in the Dark Energy Survey (DES) Science Verification and Year 1 observations. The Science Verification data span approximately 250 sq. deg. with a median i-band limiting magnitude for extended objects (10σ) of 23.0. The Year 1 data span approximately 2000 sq. deg. and have an i-band limiting magnitude for extended objects (10σ) of 22.9. As these data sets are both wide and deep, they are particularly useful for identifying strong gravitational lens candidates. Potential strong gravitational lens candidate systems were initially identified based on a color and magnitude selection in the DES object catalogs or because the system is at the location of a previously identified galaxy cluster. Cutout images of potential candidates were then visually scanned using an object viewer and numerically ranked according to whether or not we judged them to be likely strong gravitational lens systems. Having scanned nearly 400,000 cutouts, we present 374 candidate strong lens systems, of which 348 are identified for the first time. We provide the R.A. and decl., the magnitudes and photometric properties of the lens and source objects, and the distance (radius) of the source(s) from the lens center for each system.

THE BOSS EMISSION-LINE LENS SURVEY. III. STRONG LENSING OF Lyα EMITTERS BY INDIVIDUAL GALAXIES

ABSTRACT We introduce the Baryon Oscillation Spectroscopic Survey (BOSS) Emission-Line Lens Survey GALaxy-Lyα EmitteR sYstems (BELLS GALLERY) Survey, which is a Hubble Space Telescope program to image a sample of galaxy-scale strong gravitational lens candidate systems with high-redshift Lyα emitters (LAEs) as the background sources. The goal of the BELLS GALLERY Survey is to illuminate dark substructures in galaxy-scale halos by exploiting the small-scale clumpiness of rest-frame far-UV emission in lensed LAEs, and to thereby constrain the slope and normalization of the substructure-mass function. In this paper, we describe in detail the spectroscopic strong-lens selection technique, which is based on methods adopted in the previous Sloan Lens ACS (SLACS) Survey, BELLS, and SLACS for the Masses Survey. We present the BELLS GALLERY sample of the 21 highest-quality galaxy–LAE candidates selected from galaxy spectra in the BOSS of the Sloan Digital Sky Survey III. These systems consist of massive galaxies at redshifts of approximately 0.5 strongly lensing LAEs at redshifts from 2–3. The compact nature of LAEs makes them an ideal probe of dark substructures, with a substructure-mass sensitivity that is unprecedented in other optical strong-lens samples. The magnification effect from lensing will also reveal the structure of LAEs below 100 pc scales, providing a detailed look at the sites of the most concentrated unobscured star formation in the universe. The source code used for candidate selection is available for download as a part of this release.

Space Warps– II. New gravitational lens candidates from the CFHTLS discovered through citizen science

We report the discovery of 29 promising (and 59 total) new lens candidates from the CFHT Legacy Survey (CFHTLS) based on about 11 million classifications performed by citizen scientists as part of the first Space Warps lens search. The goal of the blind lens search was to identify lens candidates missed by robots (the RingFinder on galaxy scales and ArcFinder on group/cluster scales) which had been previously used to mine the CFHTLS for lenses. We compare some properties of the samples detected by these algorithms to the Space Warps sample and find them to be broadly similar. The image separation distribution calculated from the Space Warps sample shows that previous constraints on the average density profile of lens galaxies are robust. SpaceWarps recovers about 65% of known lenses, while the new candidates show a richer variety compared to those found by the two robots. This detection rate could be increased to 80% by only using classifications performed by expert volunteers (albeit at the cost of a lower purity), indicating that the training and performance calibration of the citizen scientists is very important for the success of Space Warps. In this work we present the SIMCT pipeline, used for generating in situ a sample of realistic simulated lensed images. This training sample, along with the false positives identified during the search, has a legacy value for testing future lens finding algorithms. We make the pipeline and the training set publicly available.

Spectroscopic identification of 25 disk galaxy candidate gravitational lenses in the SDSS

Context. Galaxy-scale gravitational lenses are powerful tools, which can be used to address major astrophysical questions that are still open. They can be identified either through imaging or through spectroscopy, which is less efficient than imaging but offers the major advantage of having both source and deflector red shift previously measured at discovery. Spectroscopic identification requires huge data sets of high spectral quality, such as the SDSS, and has so far focused on early-type galaxies, as the most massive galaxies are found among them. Aims. We aimed to perform spectroscopic identification of disk galaxies acting as gravitational lenses. Methods. We have selected about 300 000 galaxy spectra with EW(Hα) ≤− 10 A from the SDSS DR 8. On these spectra, we ran our original code RES, which is a fast, reliable tool able to provide a red-shift measure and to identify discordant red-shift systems if present. We have required RES to identify only systems based on a minimum number of four emission lines. We have inspected all the (54) SDSS images of the double z systems identified by RES and discarded systems for which z duplicity could be easily ascribed to the presence of two distinct objects. The remaining 25 systems, for which double z is very likely to be due to the gravitational lensing phenomenon, constitute our sample. Results. For each gravitational lens candidate system, we provide SDSS identification and image emission lines detected by RES and activity classification, when derivable. The disky nature of our candidate lenses is confirmed by their images, stellar mass estimates, g − r rest-frame colours and occurrence of star burst phenomena.

Gravitational lens modelling in a citizen science context

We develop a method to enable collaborative modelling of gravitational lenses and lens candidates, that could be used by non-professional lens enthusiasts. It uses an existing free-form modelling program (glass), but enables the input to this code to be provided in a novel way, via a user-generated diagram that is essentially a sketch of an arrival-time surface. We report on an implementation of this method, SpaghettiLens, which has been tested in a modelling challenge using 29 simulated lenses drawn from a larger set created for the Space Warps citizen science strong lens search. We find that volunteers from this online community asserted the image parities and time ordering consistently in some lenses, but made errors in other lenses depending on the image morphology. While errors in image parity and time ordering lead to large errors in the mass distribution, the enclosed mass was found to be more robust: the model-derived Einstein radii found by the volunteers were consistent with those produced by one of the professional team, suggesting that given the appropriate tools, gravitational lens modelling is a data analysis activity that can be crowd-sourced to good effect. Ideas for improvement are discussed; these include (a) overcoming the tendency of the models to be shallower than the correct answer in test cases, leading to systematic overestimation of the Einstein radius by 10 per cent at present, and (b) detailed modelling of arcs.

Search for gravitational lens candidates in the XMM-LSS/CFHTLS common field

Our aim was to identify gravitational lens candidates among some 5500 optical counterparts of the X-ray point-like sources in the medium-deep ~11 sq. deg. XMM-LSS survey. We have visually inspected the optical counterparts of each QSOs/AGN using CFHTLS T006 images. We have selected compact pairs and groups of sources which could be multiply imaged QSO/AGN. We have measured the colors and characterized the morphological types of the selected sources using the multiple PSF fitting technique. We found three good gravitational lens candidates: J021511.4-034306, J022234.3-031616 and J022607.0-040301 which consist of pairs of point-like sources having similar colors. On the basis of a color-color diagram and X-ray properties we could verify that all these sources are good QSO/AGN candidates rather than stars. Additional secondary gravitational lens candidates are also reported.