scholarly journals Gaia GraL: Gaia DR2 gravitational lens systems

2018 ◽  
Vol 616 ◽  
pp. L11 ◽  
Author(s):  
A. Krone-Martins ◽  
L. Delchambre ◽  
O. Wertz ◽  
C. Ducourant ◽  
F. Mignard ◽  
...  
Keyword(s):  
Gravitational Lens ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Lens Model ◽  
Learning Method ◽  
Gravitational Lenses ◽  
Extremely Randomized Trees ◽  
Sky Survey ◽  
Discovery Potential ◽  
Gaia Dr2

Context. Multiply imaged gravitationally lensed quasars are among the most interesting and useful observable extragalactic phenomena. Because their study constitutes a unique tool in various fields of astronomy, they are highly sought, but difficult to find. Even in this era of all-sky surveys, discovering them remains a great challenge, with barely a few hundred systems currently known. Aims. We aim to discover new multiply imaged quasar candidates in the recently published Gaia Data Release 2 (DR2), which is the astrometric and photometric all-sky survey with the highest spatial resolution that achieves effective resolutions from 0.4″ to 2.2″. Methods. We cross-matched a merged list of quasars and candidates with Gaia DR2 and found 1 839 143 counterparts within 0.5″. We then searched matches with more than two Gaia DR2 counterparts within 6″. We further narrowed the resulting list using astrometry and photometry compatibility criteria between the Gaia DR2 counterparts. A supervised machine-learning method, called extremely randomized trees, was finally adopted to assign a probability of being lensed to each remaining system. Results. We report the discovery of two quadruply imaged quasar candidates that are fully detected in Gaia DR2. These are the most promising new quasar lens candidates from Gaia DR2 and a simple singular isothermal ellipsoid lens model is able to reproduce their image positions to within ~1 mas. This Letter demonstrates the discovery potential of Gaia for gravitational lenses.

scholarly journals Gaia GraL: Gaia DR2 Gravitational Lens Systems

2019 ◽  
Vol 622 ◽  
pp. A165 ◽  
Author(s):  
L. Delchambre ◽  
A. Krone-Martins ◽  
O. Wertz ◽  
C. Ducourant ◽  
L. Galluccio ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Gravitational Lens ◽  
Misclassification Rate ◽  
Lens Model ◽  
Learning Models ◽  
Regular Grid ◽  
Extremely Randomized Trees ◽  
Data Release ◽  
Additional Constraints ◽  
Gaia Dr2

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.

scholarly journals Gaia GraL: Gaia DR2 gravitational lens systems

2018 ◽  
Vol 618 ◽  
pp. A56 ◽  
Author(s):  
C. Ducourant ◽  
O. Wertz ◽  
A. Krone-Martins ◽  
R. Teixeira ◽  
J.-F. Le Campion ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Space Mission ◽  
Gravitational Lens ◽  
Model Parameters ◽  
Gravitational Lenses ◽  
Bayesian Modelling ◽  
Lens System ◽  
Data Release ◽  
Gaia Dr2 ◽  
Exhaustive List

Context. Thanks to its spatial resolution, the ESA/Gaia space mission offers a unique opportunity to discover new multiply imaged quasars and to study the already known lensed systems at sub-milliarcsecond astrometric precisions. Aims. In this paper, we address the detection of the known multiply imaged quasars from the Gaia Data Release 2 (DR2) and determine the astrometric and photometric properties of the individually detected images found in the Gaia DR2 catalogue. Methods. We have compiled an exhaustive list of quasar gravitational lenses from the literature to search for counterparts in the Gaia DR2. We then analysed the astrometric and photometric properties of these Gaia’s detections. To highlight the tremendous potential of Gaia at the sub-milliarcsecond level we finally performed a simple Bayesian modelling of the well-known gravitational lens system HE0435-1223, using Gaia DR2 and HST astrometry. Results. From 481 known multiply imaged quasars, 206 have at least one image found in the Gaia DR2. Among the 44 known quadruply imaged quasars of the list, 29 have at least one image in the Gaia DR2, 12 of which are fully detected (2MASX J01471020+4630433, HE 0435-1223, SDSS1004+4112, PG1115+080, RXJ1131-1231, 2MASS J11344050-2103230, 2MASS J13102005-1714579, B1422+231, J1606-2333, J1721+8842, WFI2033-4723, WGD2038-4008), eight have three counterparts, eight have two and one has only one. As expected, the modelling of HE0435-1223 shows that the model parameters are significantly better constrained when using Gaia astrometry compared to HST astrometry, in particular the relative positions of the background quasar source and the centroid of the deflector. The Gaia sub-milliarcsecond astrometry also significantly reduces the parameter correlations. Conclusions. Besides providing an up-to-date list of multiply imaged quasars and their detection in the Gaia DR2, this paper shows that more complex modelling scenarios will certainly benefit from Gaia sub-milliarcsecond astrometry.

scholarly journals Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning

2018 ◽  
Vol 11 (4) ◽  
pp. 70 ◽  
Author(s):  
Jung-sik Hong ◽  
Hyeongyu Yeo ◽  
Nam-Wook Cho ◽  
Taeuk Ahn
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Area Under The Curve ◽  
High Accuracy ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Machine Learning Technique ◽  
Novel Approach ◽  
Learning Technique

Since not all suppliers are to be managed in the same way, a purchasing strategy requires proper supplier segmentation so that the most suitable strategies can be used for different segments. Most existing methods for supplier segmentation, however, either depend on subjective judgements or require significant efforts. To overcome the limitations, this paper proposes a novel approach for supplier segmentation. The objective of this paper is to develop an automated and effective way to identify core suppliers, whose profit impact on a buyer is significant. To achieve this objective, the application of a supervised machine learning technique, Random Forests (RF), to e-invoice data is proposed. To validate the effectiveness, the proposed method has been applied to real e-invoice data obtained from an automobile parts manufacturer. Results of high accuracy and the area under the curve (AUC) attest to the applicability of our approach. Our method is envisioned to be of value for automating the identification of core suppliers. The main benefits of the proposed approach include the enhanced efficiency of supplier segmentation procedures. Besides, by utilizing a machine learning method to e-invoice data, our method results in more reliable segmentation in terms of selecting and weighting variables.

scholarly journals Single Image De-Raining using Supervised CNN Model

2021 ◽  
Vol 9 (10) ◽  
pp. 244-250
Author(s):  
Dr. Geeta Hanji
Keyword(s):  
Machine Learning ◽  
High Frequency ◽  
Accurate Result ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Single Image ◽  
Elapsed Time ◽  
Difficult Issue

Abstract: An image captured in rain reduces the visibility quality of image which affects the analytical task like detecting objects and classifying pictures. Hence, image de-raining became important in last few years. Since pictures taken in rain include rain streaks of all sizes, single image de-raining is becoming much difficult issue to solve, which may flow in different direction and the density of each rain streak is different. Rain streaks have a varied effect on various areas of picture, and hence it becomes important for removing rain streak from rainy pictures as rainy images tend to lose its high frequency information; previously many methods were proposed for this purpose but they failed to provide accurate results. Hence we have studied and implemented a supervised machine learning method using convolutional neural network (CNN) algorithm to get more accurate result of rain streak removal from an image captured during rain and in less elapsed time by preserving high rated information of image during removal of rain streak. Keywords: CNN, elapsed time, single image de-raining, supervised machine learning, rain streaks.

scholarly journals Prognostic Value of Multiple Circulating Biomarkers for 2-Year Death in Acute Heart Failure With Preserved Ejection Fraction

2021 ◽  
Vol 8 ◽  
Author(s):  
Yan Gao ◽  
Xueke Bai ◽  
Jiapeng Lu ◽  
Lihua Zhang ◽  
Xiaofang Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Ejection Fraction ◽  
Risk Stratification ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Preserved Ejection Fraction ◽  
Circulating Biomarkers ◽  
Svm Model

Background: Heart failure with preserved ejection fraction (HFpEF) is increasingly recognized as a major global public health burden and lacks effective risk stratification. We aimed to assess a multi-biomarker model in improving risk prediction in HFpEF.Methods: We analyzed 18 biomarkers from the main pathophysiological domains of HF in 380 patients hospitalized for HFpEF from a prospective cohort. The association between these biomarkers and 2-year risk of all-cause death was assessed by Cox proportional hazards model. Support vector machine (SVM), a supervised machine learning method, was used to develop a prediction model of 2-year all-cause and cardiovascular death using a combination of 18 biomarkers and clinical indicators. The improvement of this model was evaluated by c-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).Results: The median age of patients was 71-years, and 50.5% were female. Multiple biomarkers independently predicted the 2-year risk of death in Cox regression model, including N-terminal pro B-type brain-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-TnT), growth differentiation factor-15 (GDF-15), tumor necrosis factor-α (TNFα), endoglin, and 3 biomarkers of extracellular matrix turnover [tissue inhibitor of metalloproteinases (TIMP)-1, matrix metalloproteinase (MMP)-2, and MMP-9) (FDR < 0.05). The SVM model effectively predicted the 2-year risk of all-cause death in patients with acute HFpEF in training set (AUC 0.834, 95% CI: 0.771–0.895) and validation set (AUC 0.798, 95% CI: 0.719–0.877). The NRI and IDI indicated that the SVM model significantly improved patient classification compared to the reference model in both sets (p < 0.05).Conclusions: Multiple circulating biomarkers coupled with an appropriate machine-learning method could effectively predict the risk of long-term mortality in patients with acute HFpEF. It is a promising strategy for improving risk stratification in HFpEF.

scholarly journals 1608+656: A Quadruple Lens System Found In The CLASS Gravitational Lens Survey

1996 ◽  
Vol 173 ◽  
pp. 317-322
Author(s):  
Steven T. Myers
Keyword(s):  
Gravitational Lens ◽  
Second Phase ◽  
Lens Model ◽  
Lens System ◽  
Radio Image ◽  
Mass Model ◽  
Double Image ◽  
Very Large Array ◽  
Sky Survey ◽  
Simple Mass

The first phase of a large gravitational lens survey using the Very Large Array at a wavelength of 3.6 cm has been completed, yielding images for 3258 radio sources. The Cosmic Lens All-Sky Survey, or CLASS, is designed to locate gravitational lens systems consisting of multiply-imaged compact components with separations > 0.″2. From this first phase has come the discovery of 1608+656, a quadruply-imaged object with maximum separation of 2.″1. Images from the Palomar 5-m and Keck 10-m telescopes show the lensed images and the lensing galaxy. An optical spectrum obtained with the Palomar 5-m Telescope indicates a redshift of z = 0.63 for the lensing galaxy, and a newly-obtained Palomar spectrum indicates a redshift of z = 1.39 for the lensed source, which appears to be a galaxy. A simple single-galaxy lens model derived from the radio image reproduces the observed configuration and relative fluxes of the images, as well as the position, shape, and orientation of the lensing galaxy. Because a simple mass model is able to fit the observations, we argue that this lens system is promising for determining H0. CLASS has also yielded the new double image lens system 1600+434. The second phase of the survey is scheduled for August and September 1995 on the VLA, and should yield images for an additional 5000+ targets, bringing the CLASS total to over 8000.

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