000201349 001__ 201349
000201349 005__ 20181203023605.0
000201349 0247_ $$2doi$$a10.1051/0004-6361/201423365
000201349 022__ $$a0004-6361
000201349 02470 $$2ISI$$a000338681500085
000201349 037__ $$aARTICLE
000201349 245__ $$aA PCA-based automated finder for galaxy-scale strong lenses
000201349 260__ $$bEDP Sciences$$c2014$$aLes Ulis Cedex A
000201349 269__ $$a2014
000201349 300__ $$a10
000201349 336__ $$aJournal Articles
000201349 520__ $$aWe present an algorithm using principal component analysis (PCA) to subtract galaxies from imaging data and also two algorithms to find strong, galaxy-scale gravitational lenses in the resulting residual image. The combined method is optimised to find full or partial Einstein rings. Starting from a pre-selection of potential massive galaxies, we first perform a PCA to build a set of basis vectors. The galaxy images are reconstructed using the PCA basis and subtracted from the data. We then filter the residual image with two different methods. The first uses a curvelet (curved wavelets) filter of the residual images to enhance any curved/ring feature. The resulting image is transformed in polar coordinates, centred on the lens galaxy. In these coordinates, a ring is turned into a line, allowing us to detect very faint rings by taking advantage of the integrated signal-to-noise in the ring (a line in polar coordinates). The second way of analysing the PCA-subtracted images identifies structures in the residual images and assesses whether they are lensed images according to their orientation, multiplicity, and elongation. We applied the two methods to a sample of simulated Einstein rings as they would be observed with the ESA Euclid satellite in the VIS band. The polar coordinate transform allowed us to reach a completeness of 90% for a purity of 86%, as soon as the signal-to-noise integrated in the ring was higher than 30 and almost independent of the size of the Einstein ring. Finally, we show with real data that our PCA-based galaxy subtraction scheme performs better than traditional subtraction based on model fitting to the data. Our algorithm can be developed and improved further using machine learning and dictionary learning methods, which would extend the capabilities of the method to more complex and diverse galaxy shapes.
000201349 6531_ $$agravitational lensing: strong
000201349 6531_ $$atechniques: image processing
000201349 6531_ $$amethods: data analysis
000201349 6531_ $$adark matter
000201349 6531_ $$asurveys
000201349 6531_ $$acosmological parameters
000201349 700__ $$0248592$$g235282$$uEcole Polytech Fed Lausanne, Lab Astrophys, Observ Sauverny, CH-1290 Versoix, Switzerland$$aJoseph, R.
000201349 700__ $$0245072$$g166196$$aCourbin, F.
000201349 700__ $$uUniv Bologna, Dipartimento Fis & Astron, I-40127 Bologna, Italy$$aMetcalf, R. B.
000201349 700__ $$uUniv Bologna, Dipartimento Fis & Astron, I-40127 Bologna, Italy$$aGiocoli, C.
000201349 700__ $$uUniv Manchester, Jodrell Bank Ctr Astrophys, Sch Phys & Astron, Manchester M13 9PL, Lancs, England$$aHartley, P.
000201349 700__ $$uUniv Manchester, Jodrell Bank Ctr Astrophys, Sch Phys & Astron, Manchester M13 9PL, Lancs, England$$aJackson, N.
000201349 700__ $$uUniv Bologna, Dipartimento Fis & Astron, I-40127 Bologna, Italy$$aBellagamba, F.
000201349 700__ $$0246614$$g222189$$uEcole Polytech Fed Lausanne, Lab Astrophys, Observ Sauverny, CH-1290 Versoix, Switzerland$$aKneib, J.-P.
000201349 700__ $$uUniv Groningen, Kapteyn Astron Inst, NL-9700 AV Groningen, Netherlands$$aKoopmans, L.
000201349 700__ $$uUniv Munich, Dept Phys, D-81679 Munich, Germany$$aLemson, G.
000201349 700__ $$uINAF Osservatorio Astron Bologna, I-40127 Bologna, Italy$$aMeneghetti, M.
000201349 700__ $$g158695$$uEcole Polytech Fed Lausanne, Lab Astrophys, Observ Sauverny, CH-1290 Versoix, Switzerland$$aMeylan, G.$$0244706
000201349 700__ $$aPetkova, M.$$uUniv Bologna, Dipartimento Fis & Astron, I-40127 Bologna, Italy
000201349 700__ $$aPires, S.
000201349 773__ $$j566$$tAstronomy & Astrophysics
000201349 909C0 $$xU10933$$0252365$$pLASTRO
000201349 909CO $$pSB$$particle$$ooai:infoscience.tind.io:201349
000201349 917Z8 $$x103317
000201349 937__ $$aEPFL-ARTICLE-201349
000201349 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000201349 980__ $$aARTICLE