Deep learning for strong lensing search: tests of the convolutional neural networks and new candidates from KiDS DR3

  title={Deep learning for strong lensing search: tests of the convolutional neural networks and new candidates from KiDS DR3},
  author={Zizhao He and Xinzhong Er and Qian Long and Dezi Liu and Xiangkun Liu and Ziwei Li and Yun Liu and Wenqaing Deng and Zu-hui Fan},
  journal={Monthly Notices of the Royal Astronomical Society},
  • Zizhao He, X. Er, Z. Fan
  • Published 1 July 2020
  • Physics
  • Monthly Notices of the Royal Astronomical Society
Convolutional Neutral Networks have been successfully applied in searching for strong lensing systems, leading to discoveries of new candidates from large surveys. On the other hand, systematic investigations about their robustness are still lacking. In this paper, we first construct a neutral network, and apply it to $r$-band images of Luminous Red Galaxies (LRGs) of the Kilo Degree Survey (KiDS) Data Release 3 to search for strong lensing systems. We build two sets of training samples, one… 
High-quality Strong Lens Candidates in the Final Kilo-Degree Survey Footprint
The effect of the seeing on the accuracy of CNN classification and possible avenues to increase the efficiency of multiband classifiers are discussed, in preparation of next-generation surveys from ground and space.
Lenses In VoicE (LIVE): searching for strong gravitational lenses in the VOICE@VST survey using convolutional neural networks
We present a sample of 16 likely strong gravitational lenses identified in the VST Optical Imaging of the CDFS and ES1 fields (VOICE survey) using Convolutional Neural Networks (CNNs). We train two
SILVERRUSH X: Machine Learning-aided Selection of 9318 LAEs at z = 2.2, 3.3, 4.9, 5.7, 6.6, and 7.0 from the HSC SSP and CHORUS Survey Data
We present a new catalog of 9318 Lyα emitter (LAE) candidates at z = 2.2, 3.3, 4.9, 5.7, 6.6, and 7.0 that are photometrically selected by the SILVERRUSH program with a machine learning technique
Desenvolvendo um Ensemble de Redes Profundas para identificação de Lentes Gravitacionais: Aplicação em Regime de Poucos dados
The performance of the deep networks ResNet50, EfficientNet B2 and their Ensemble is analyzed, using a image bank of simulated images over a rare physical system: Strong Gravitational Lensing to obtain the best performance with the smallest amount of training data.
Point spread function estimation for wide field small aperture telescopes with deep neural networks and calibration data
The results show that the Tel–Net can successfully reconstruct PSFs of WFSATs of any states and in any positions of the FoV, which is significantly more precise than results obtained by the compared classic method Inverse Distance Weight (IDW) interpolation.
Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting
The vast quantity of strong galaxy-galaxy gravitational lenses expected by future large-scale surveys necessitates the development of automated methods to efficiently model their mass profiles. For


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 that we have
Deep convolutional neural networks as strong gravitational lens detectors
A new strong gravitational lens finder based on convolutional neural networks (CNNs) is presented and it is found that using committees of 5 CNNs produce the best recall at zero contamination and consistenly score better AUC than a single CNN.
Finding strong gravitational lenses in the Kilo Degree Survey with Convolutional Neural Networks
The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyse sources. Indeed, this is the case for the search for strong
Using convolutional neural networks to identify gravitational lenses in astronomical images
The Euclid telescope, due for launch in 2021, will perform an imaging and slitless spectroscopy survey over half the sky, to map baryon wiggles and weak lensing. During the survey, Euclid is
The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples
We explore the effectiveness of deep learning convolutional neural networks (CNNs) for estimating strong gravitational lens mass model parameters. We have investigated a number of practicalities
LinKS: discovering galaxy-scale strong lenses in the Kilo-Degree Survey using convolutional neural networks
We present a new sample of galaxy-scale strong gravitational lens candidates, selected from 904 deg2 of Data Release 4 of the Kilo-Degree Survey, i.e. the ‘Lenses in the Kilo-Degree Survey’ (LinKS)
CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding
Galaxy-scale strong gravitational lensing is not only a valuable probe of the dark matter distribution of massive galaxies, but can also provide valuable cosmological constraints, either by studying
The strong gravitational lens finding challenge
Large-scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will
An Extended Catalog of Galaxy–Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networks
We search Dark Energy Survey (DES) Year 3 imaging for galaxy–galaxy strong gravitational lenses using convolutional neural networks, extending previous work with new training sets and covering a
Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
Deep-HiTS, a rotation invariant convolutional neural network model for classifying images of transients candidates into artifacts or real sources for the High cadence Transient Survey (HiTS) is introduced, the first time CNNs have been used to detect astronomical transient events.