Survey of Gravitationally lensed Objects in HSC Imaging (SuGOHI)

Chan, J. H. H.; Suyu, S. H.; Sonnenfeld, Alessandro; Jaelani, Anton T.; More, Anupreeta; Yonehara, A.; Kubota, Yuriko; Coupon, J.; Lee, Chien‐Hsiu; Oguri, Masamune; Rusu, Cristian E.; Wong, Kenneth C.

doi:10.1051/0004-6361/201937030

Cited by 39 publications

(54 citation statements)

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“…We compiled grade A and B systems (or equivalent) from a number of recent searches based on optical and near-infrared imaging from DES (Diehl et al 2017;Jacobs et al 2019b,a), CFHTLS (More et al 2016;Jacobs et al 2017), KiDS (Petrillo et al 2017(Petrillo et al , 2019Li et al 2020), DESI (Huang et al 2020a), and HSC (Wong et al 2018;Sonnenfeld et al 2018Sonnenfeld et al , 2020Jaelani et al 2020). Since our network may also be sensitive to lensed quasars with colors and configurations similar to our mock lenses, we also cross-matched with the all-sky database of ∼220 confirmed lensed quasars in the literature (Lemon et al 2019, and references therein) 12 , and with previously identified lensed quasars from HSC (Chan et al 2020) and from PS1 (Rusu et al 2019). For the candidates not included in those tables, we searched in the SIMBAD Astronomical Database 13 .…”

Section: Final Candidates From Visual Inspectionmentioning

confidence: 99%

Holismokes

Cañameras

Schuldt

Suyu

et al. 2020

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We present a systematic search for wide-separation (with Einstein radius θE ≳ 1.5″), galaxy-scale strong lenses in the 30 000 deg2 of the Pan-STARRS 3π survey on the Northern sky. With long time delays of a few days to weeks, these types of systems are particularly well-suited for catching strongly lensed supernovae with spatially-resolved multiple images and offer new insights on early-phase supernova spectroscopy and cosmography. We produced a set of realistic simulations by painting lensed COSMOS sources on Pan-STARRS image cutouts of lens luminous red galaxies (LRGs) with redshift and velocity dispersion known from the sloan digital sky survey (SDSS). First, we computed the photometry of mock lenses in gri bands and applied a simple catalog-level neural network to identify a sample of 1 050 207 galaxies with similar colors and magnitudes as the mocks. Second, we trained a convolutional neural network (CNN) on Pan-STARRS gri image cutouts to classify this sample and obtain sets of 105 760 and 12 382 lens candidates with scores of pCNN > 0.5 and > 0.9, respectively. Extensive tests showed that CNN performances rely heavily on the design of lens simulations and the choice of negative examples for training, but little on the network architecture. The CNN correctly classified 14 out of 16 test lenses, which are previously confirmed lens systems above the detection limit of Pan-STARRS. Finally, we visually inspected all galaxies with pCNN > 0.9 to assemble a final set of 330 high-quality newly-discovered lens candidates while recovering 23 published systems. For a subset, SDSS spectroscopy on the lens central regions proves that our method correctly identifies lens LRGs at z ∼ 0.1–0.7. Five spectra also show robust signatures of high-redshift background sources, and Pan-STARRS imaging confirms one of them as a quadruply-imaged red source at zs = 1.185, which is likely a recently quenched galaxy strongly lensed by a foreground LRG at zd = 0.3155. In the future, high-resolution imaging and spectroscopic follow-up will be required to validate Pan-STARRS lens candidates and derive strong lensing models. We also expect that the efficient and automated two-step classification method presented in this paper will be applicable to the ∼4 mag deeper gri stacks from the Rubin Observatory Legacy Survey of Space and Time (LSST) with minor adjustments.

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Section: Final Candidates From Visual Inspectionmentioning

confidence: 99%

Holismokes

Cañameras

Schuldt

Suyu

et al. 2020

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“…Since these strong lens observations are very powerful, several large surveys including the Sloan Lens ACS (SLACS) survey (Bolton et al 2006;Shu et al 2017), the CFHTLS Strong Lensing Legacy Survey (SL2S; Cabanac et al 2007;Sonnenfeld et al 2015), the Sloan WFC Edge-on Late-type Lens Survey (SWELLS; Treu et al 2011), the BOSS Emission-Line Lens Survey (BELLS; Brownstein et al 2012;Shu et al 2016;Cornachione et al 2018), the Dark Energy Survey (DES; Dark Energy Survey Collaboration 2005; Tanoglidis et al 2021), the Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI; Sonnenfeld et al 2018a;Wong et al 2018;Chan et al 2020;Jaelani et al 2020), and surveys in the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS; e.g., Lemon et al 2018;Cañameras et al 2020) have been conducted to find lenses. So far we have detected several thousand lenses, but mainly from the lower redshift regime.…”

Section: Introductionmentioning

confidence: 99%

Holismokes

Schuldt

Suyu

Meinhardt

et al. 2021

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Modeling the mass distributions of strong gravitational lenses is often necessary in order to use them as astrophysical and cosmological probes. With the large number of lens systems (≳105) expected from upcoming surveys, it is timely to explore efficient modeling approaches beyond traditional Markov chain Monte Carlo techniques that are time consuming. We train a convolutional neural network (CNN) on images of galaxy-scale lens systems to predict the five parameters of the singular isothermal ellipsoid (SIE) mass model (lens center x and y, complex ellipticity ex and ey, and Einstein radius θE). To train the network we simulate images based on real observations from the Hyper Suprime-Cam Survey for the lens galaxies and from the Hubble Ultra Deep Field as lensed galaxies. We tested different network architectures and the effect of different data sets, such as using only double or quad systems defined based on the source center and using different input distributions of θE. We find that the CNN performs well, and with the network trained on both doubles and quads with a uniform distribution of θE > 0.5″ we obtain the following median values with 1σ scatter: Δx = (0.00−0.30+0.30)″, Δy = (0.00−0.29+0.30)″, ΔθE = (0.07−0.12+0.29)″, Δex = −0.01−0.09+0.08, and Δey = 0.00−0.09+0.08. The bias in θE is driven by systems with small θE. Therefore, when we further predict the multiple lensed image positions and time-delays based on the network output, we apply the network to the sample limited to θE > 0.8″. In this case the offset between the predicted and input lensed image positions is (0.00−0.29+0.29)″ and (0.00−0.31+0.32)″ for the x and y coordinates, respectively. For the fractional difference between the predicted and true time-delay, we obtain 0.04−0.05+0.27. Our CNN model is able to predict the SIE parameter values in fractions of a second on a single CPU, and with the output we can predict the image positions and time-delays in an automated way, such that we are able to process efficiently the huge amount of expected galaxy-scale lens detections in the near future.

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“…The steps described above led us to a sample of ∼300 000 galaxies. From these, we removed 70 known lenses from the literature, mostly from our previous searches (Sonnenfeld et al 2018;Wong et al 2018;Chan et al 2020), as well as a few hundred galaxies that have already been inspected and identified as possible lenses (grade C candidates in our notation) in the aforementioned studies. Many of the known lenses were used for training purposes, which is further explained in Sect.…”

Section: The Parent Samplementioning

confidence: 99%

“…The blue portion of the histogram corresponds to lenses discovered in this study. The green part indicates BOSS galaxy lenses discovered with YattaLens, presented in Paper I, II, and IV (Sonnenfeld et al 2018;Wong et al 2019;Chan et al 2020). The cyan part shows lenses discovered by means of visual inspection of galaxy clusters, as presented in Paper V. The striped regions indicate lenses discovered independently in this study and in the study of Paper V. Grey solid lines: distribution in lens spectroscopic redshift of lenses from the Sloan ACS Lens Survey (SLACS, Auger et al 2010).…”

Section: A Diverse Population Of Lensesmentioning

confidence: 99%

Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI)

Sonnenfeld

Verma²,

More³

et al. 2020

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Context. Strong lenses are extremely useful probes of the distribution of matter on galaxy and cluster scales at cosmological distances, however, they are rare and difficult to find. The number of currently known lenses is on the order of 1000. Aims. The aim of this study is 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. Methods. Based on the S16A internal data release of the HSC survey, we chose a sample of ∼300 000 galaxies with photometric redshifts in the range of 0.2 < zphot < 1.2 and photometrically inferred stellar masses of 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 6000 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. Results. Based on a statistical analysis of classification data from the volunteers, we selected a sample of the most promising ∼1500 candidates, which we then visually inspected: half of them turned out to be possible (grade C) lenses or better. By 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 (grade A), 129 probable lenses (grade B), and 581 possible lenses. YATTALENS found half the number of lenses that were discovered via crowdsourcing. Conclusions. Crowdsourcing is able to produce samples of lens candidates with high completeness, when multiple images are clearly detected, and with higher purity compared to the currently available automated algorithms. A hybrid approach, in which the visual inspection of samples of lens candidates pre-selected by discovery algorithms or coupled to machine learning is crowdsourced, will be a viable option for lens finding in the 2020s, with forthcoming wide-area surveys such as LSST, Euclid, and WFIRST.

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Survey of Gravitationally lensed Objects in HSC Imaging (SuGOHI)

Cited by 39 publications

References 77 publications

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Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI)

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