Skip navigation
  • INAF logo
  • Home
  • Communities
    & Collections
  • Research outputs
  • Researchers
  • Organization units
  • Projects
  • Explore by
    • Research outputs
    • Researchers
    • Organization units
    • Projects
  • Login:
    • My DSpace
    • Receive email
      updates
    • Edit Account details
  • Italian
  • English

  1. OA@INAF
  2. PRODOTTI RICERCA INAF
  3. 1 CONTRIBUTI IN RIVISTE (Journal articles)
  4. 1.01 Articoli in rivista
Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12386/29538
Title: LinKS: discovering galaxy-scale strong lenses in the Kilo-Degree Survey using convolutional neural networks
Authors: Petrillo, C. E.
TORTORA, CRESCENZO 
Vernardos, G.
Koopmans, L. V. E.
Verdoes Kleijn, G.
Bilicki, M.
NAPOLITANO, NICOLA ROSARIO 
Chatterjee, S.
Covone, G.
Dvornik, A.
Erben, T.
GETMAN, FEDOR 
Giblin, B.
Heymans, C.
de Jong, J. T. A.
Kuijken, K.
Schneider, P.
Shan, H.
SPINIELLO, CHIARA 
Wright, A. H.
Issue Date: 2019
Journal: MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 
Number: 484
Issue: 3
First Page: 3879
Abstract: We present a new sample of galaxy-scale strong gravitational lens candidates, selected from 904 deg<SUP>2</SUP> of Data Release 4 of the Kilo-Degree Survey, i.e. the `Lenses in the Kilo-Degree Survey' (LinKS) sample. We apply two convolutional neural networks (ConvNets) to {∼ }88 000 colour-magnitude-selected luminous red galaxies yielding a list of 3500 strong lens candidates. This list is further downselected via human inspection. The resulting LinKS sample is composed of 1983 rank-ordered targets classified as `potential lens candidates' by at least one inspector. Of these, a high-grade subsample of 89 targets is identified with potential strong lenses by all inspectors. Additionally, we present a collection of another 200 strong lens candidates discovered serendipitously from various previous ConvNet runs. A straightforward application of our procedure to future Euclid or Large Synoptic Survey Telescope data can select a sample of ∼3000 lens candidates with less than 10 per cent expected false positives and requiring minimal human intervention.
URI: http://hdl.handle.net/20.500.12386/29538
URL: https://academic.oup.com/mnras/article/484/3/3879/5290335
ISSN: 0035-8711
DOI: 10.1093/mnras/stz189
Bibcode ADS: 2019MNRAS.484.3879P
Fulltext: open
Appears in Collections:1.01 Articoli in rivista

Files in This Item:
File Description SizeFormat 
Petrillo+19_MNRAS_484_3879_LinKS.pdfPDF editoriale21.81 MBAdobe PDFView/Open
Show full item record

Page view(s)

4
checked on Jan 16, 2021

Download(s)

1
checked on Jan 16, 2021

Google ScholarTM

Check

Altmetric


Items in DSpace are published in Open Access, unless otherwise indicated.


Explore by
  • Communities
    & Collections
  • Research outputs
  • Researchers
  • Organization units
  • Projects

Informazioni e guide per autori

https://openaccess-info.inaf.it: tutte le informazioni sull'accesso aperto in INAF

Come si inserisce un prodotto: le guide a OA@INAF

La Policy INAF sull'accesso aperto

Documenti e modelli scaricabili

Feedback
Built with DSpace-CRIS - Extension maintained and optimized by Logo 4SCIENCE