Discovery of Two Einstein Crosses from Massive Post-blue Nugget Galaxies at z &gt; 1 in KiDS*

Napolitano, N. R.; Li, R.; Spiniello, C.; Tortora, C.; Sergeyev, A.; D’Ago, G.; Guo, Xiaotong; Xie, Lin; Radovich, M.; Roy, N.; Koopmans, L. V. E.; Kuijken, Konrad; Bilicki, Maciej; Erben, T.; Getman, F.; Heymans, Catherine; Hildebrandt, Hendrik; Moya, Claudia; Shan, Huanyuan; Vernardos, G.; Wright, Angus H.

doi:10.3847/2041-8213/abc95b

Cited by 7 publications

(14 citation statements)

References 52 publications

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“…In Table 2 we list the "KiDS ID", coordinates, r-band magnitudes, probabilities from CNNs, and scores from human inspection Among these, we identify some interesting systems. J234804.98−302855.45 is a new Einstein cross, that, based on the color and size of the quadruple images, seems to be a lensed blue nugget system like the ones we have found in Napolitano et al (2020). J112610.75+033847.89 is a large arc around a close pair of galaxies.…”

Section: Visual Inspection and Selection Of Hq Candidatessupporting

confidence: 53%

“…Compared with existing lens samples imaged by the Hubble Space Telescope (e.g., SLACS, Bolton et al 2008;BELLS, Brownstein et al 2012, BELLS GALLERY, Shu et al 2016, KiDS, combined with the twin NIR VIKING survey (Edge et al 2013), provides us photometry images in 9 bands (u, g, r, i, Z, Y, J, H, Ks), which can be used for accurate stellar mass establishment of both deflectors and sources (see e.g. Napolitano et al 2020). This is a crucial ingredient to derive unbiased DM estimates of the lenses and characterize the physics of the high redshift galaxies in the background.…”

Section: The First Kids Catalog Of Hq Lens Candidatesmentioning

confidence: 99%

“…According to general relativity, the light from the source is magnified and deflected by the curved space-time generated by the lens, or deflector, to form distinctive SGL features. If the source is a compact object like a quasar (e.g., Suyu et al 2013Suyu et al , 2017 a supernova (e.g., Kelly et al 2015) or an ultra-compact galaxy (e.g., Bolton et al 2006a;More et al 2017;Napolitano et al 2020) its light is split into multiple images. If the source is an extended galaxy, it produces a stretched arc or full ring (e.g., Bolton et al 2008;Brownstein et al 2012;Sonnenfeld et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Besides cosmology-related questions, SGL can also be used for galaxy formation and evolution studies. Indeed, acting as a gravitational telescope, it magnifies high-redshift objects, which otherwise would be difficult to observe, hence permitting the study of their internal structure and stellar populations (e.g., ALMA Partnership et al 2015;Cornachione et al 2018;Claeyssens et al 2019;Chen et al 2019;Rydberg et al 2020;Napolitano et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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New High-quality Strong Lens Candidates with Deep Learning in the Kilo-Degree Survey

Li¹,

Napolitano²,

Tortora

et al. 2020

ApJ

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We report new high-quality galaxy-scale strong lens candidates found in the Kilo-Degree Survey data release 4 using machine learning. We have developed a new convolutional neural network (CNN) classifier to search for gravitational arcs, following the prescription by Petrillo et al. and using only r-band images. We have applied the CNN to two "predictive samples": a luminous red galaxy (LRG) and a "bright galaxy" (BG) sample (r<21). We have found 286 new high-probability candidates, 133 from the LRG sample and 153 from the BG sample. We have ranked these candidates based on a value that combines the CNN likelihood of being a lens and the human score resulting from visual inspection (P-value), and here we present the highest 82 ranked candidates with P-values 0.5. All of these high-quality candidates have obvious arc or pointlike features around the central red defector. Moreover, we define the best 26 objects, all with P-values 0.7, as a "golden sample" of candidates. This sample is expected to contain very few false positives; thus, it is suitable for follow-up observations. The new lens candidates come partially from the more extended footprint adopted here with respect to the previous analyses and partially from a larger predictive sample (also including the BG sample). These results show that machine-learning tools are very promising for finding strong lenses in large surveys and more candidates can be found by enlarging the predictive samples beyond the standard assumption of LRGs. In the future, we plan to apply our CNN to the data from next-generation surveys such as the Large Synoptic Survey Telescope, Euclid, and the Chinese Space Station Optical Survey.

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Section: Visual Inspection and Selection Of Hq Candidatessupporting

confidence: 53%

Section: The First Kids Catalog Of Hq Lens Candidatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

New High-quality Strong Lens Candidates with Deep Learning in the Kilo-Degree Survey

Li¹,

Napolitano²,

Tortora

et al. 2020

ApJ

Self Cite

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show abstract

“…According to general relativity, the light from the source is magnified and deflected by the curved spacetime generated by the lens, or deflector, to form distinctive SGL features. If the source is a compact object like a quasar (e.g., Suyu et al 2013Suyu et al , 2017 a supernova (e.g., Kelly et al 2015) or an ultracompact galaxy (e.g., Bolton et al 2006a;More et al 2017;Napolitano et al 2020) its light is split into multiple images. If the source is an extended galaxy, it produces a stretched arc or full ring (e.g., Bolton et al 2008;Brownstein et al 2012;Sonnenfeld et al 2013).…”

Section: Introductionmentioning

confidence: 99%

High-quality Strong Lens Candidates in the Final Kilo-Degree Survey Footprint

Napolitano

Spiniello

et al. 2021

ApJ

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We present 97 new high-quality strong lensing candidates found in the final ∼350 deg2 that complete the full ∼1350 deg2 area of the Kilo-Degree Survey (KiDS). Together with our previous findings, the final list of high-quality candidates from KiDS sums up to 268 systems. The new sample is assembled using a new convolutional neural network (CNN) classifier applied to r-band (best-seeing) and g, r, and i color-composited images separately. This optimizes the complementarity of the morphology and color information on the identification of strong lensing candidates. We apply the new classifiers to a sample of luminous red galaxies (LRGs) and a sample of bright galaxies (BGs) and select candidates that received a high probability to be a lens from the CNN (P CNN). In particular, setting P CNN > 0.8 for the LRGs, the one-band CNN predicts 1213 candidates, while the three-band classifier yields 1299 candidates, with only ∼30% overlap. For the BGs, in order to minimize the false positives, we adopt a more conservative threshold, P CNN > 0.9, for both CNN classifiers. This results in 3740 newly selected objects. The candidates from the two samples are visually inspected by seven coauthors to finally select 97 “high-quality” lens candidates which received mean scores larger than 6 (on a scale from 0 to 10). We finally discuss the effect of the seeing on the accuracy of CNN classification and possible avenues to increase the efficiency of multiband classifiers, in preparation of next-generation surveys from ground and space.

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Euclid preparation

Leuzzi,

Meneghetti,

Angora

et al. 2024

A&A

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Forthcoming imaging surveys will increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of billions of galaxies will have to be inspected to identify potential candidates. In this context, deep-learning techniques are particularly suitable for finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong-lensing systems on the basis of their morphological characteristics. In particular, we implemented a classical CNN architecture, an inception network, and a residual network. We trained and tested our networks on different subsamples of a data set of 40 000 mock images whose characteristics were similar to those expected in the wide survey planned with the ESA mission Euclid, gradually including larger fractions of faint lenses. We also evaluated the importance of adding information about the color difference between the lens and source galaxies by repeating the same training on single- and multiband images. Our models find samples of clear lenses with ≳90% precision and completeness. Nevertheless, when lenses with fainter arcs are included in the training set, the performance of the three models deteriorates with accuracy values of ~0.87 to ~0.75, depending on the model. Specifically, the classical CNN and the inception network perform similarly in most of our tests, while the residual network generally produces worse results. Our analysis focuses on the application of CNNs to high-resolution space-like images, such as those that the Euclid telescope will deliver. Moreover, we investigated the optimal training strategy for this specific survey to fully exploit the scientific potential of the upcoming observations. We suggest that training the networks separately on lenses with different morphology might be needed to identify the faint arcs. We also tested the relevance of the color information for the detection of these systems, and we find that it does not yield a significant improvement. The accuracy ranges from ~0.89 to ~0.78 for the different models. The reason might be that the resolution of the Euclid telescope in the infrared bands is lower than that of the images in the visual band.

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Discovery of Two Einstein Crosses from Massive Post-blue Nugget Galaxies at z > 1 in KiDS*

Cited by 7 publications

References 52 publications

New High-quality Strong Lens Candidates with Deep Learning in the Kilo-Degree Survey

New High-quality Strong Lens Candidates with Deep Learning in the Kilo-Degree Survey

High-quality Strong Lens Candidates in the Final Kilo-Degree Survey Footprint

Euclid preparation

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