Publication

The strong gravitational lens finding challenge

Metcalf, R. B., Meneghetti, M., Avestruz, C., Bellagamba, F., Bom, C. R., Bertin, E., Cabanac, R., Courbin, F., Davies, A., Decencière, E., Flamary, R., Gavazzi, R., Geiger, M., Hartley, P., Huertas-Company, M., Jackson, N., Jacobs, C., Jullo, E., Kneib, J. -P., Koopmans, L. V. E., Lanusse, F., Li, C. -L., Ma, Q., Makler, M., Li, N., Lightman, M., Petrillo, C. E., Serjeant, S., Schäfer, C., Sonnenfeld, A., Tagore, A., Tortora, C., Tuccillo, D., Valentín, M. B., Velasco-Forero, S., Verdoes Kleijn, G. A. & Vernardos, G., 1-May-2019, In : Astronomy and astrophysics. 625, May 2019, 22 p., A119.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Metcalf, R. B., Meneghetti, M., Avestruz, C., Bellagamba, F., Bom, C. R., Bertin, E., Cabanac, R., Courbin, F., Davies, A., Decencière, E., Flamary, R., Gavazzi, R., Geiger, M., Hartley, P., Huertas-Company, M., Jackson, N., Jacobs, C., Jullo, E., Kneib, J. -P., ... Vernardos, G. (2019). The strong gravitational lens finding challenge. Astronomy and astrophysics, 625(May 2019), [A119]. https://doi.org/10.1051/0004-6361/201832797

Author

Metcalf, R. B. ; Meneghetti, M. ; Avestruz, C. ; Bellagamba, F. ; Bom, C. R. ; Bertin, E. ; Cabanac, R. ; Courbin, F. ; Davies, A. ; Decencière, E. ; Flamary, R. ; Gavazzi, R. ; Geiger, M. ; Hartley, P. ; Huertas-Company, M. ; Jackson, N. ; Jacobs, C. ; Jullo, E. ; Kneib, J. -P. ; Koopmans, L. V. E. ; Lanusse, F. ; Li, C. -L. ; Ma, Q. ; Makler, M. ; Li, N. ; Lightman, M. ; Petrillo, C. E. ; Serjeant, S. ; Schäfer, C. ; Sonnenfeld, A. ; Tagore, A. ; Tortora, C. ; Tuccillo, D. ; Valentín, M. B. ; Velasco-Forero, S. ; Verdoes Kleijn, G. A. ; Vernardos, G. / The strong gravitational lens finding challenge. In: Astronomy and astrophysics. 2019 ; Vol. 625, No. May 2019.

Harvard

Metcalf, RB, Meneghetti, M, Avestruz, C, Bellagamba, F, Bom, CR, Bertin, E, Cabanac, R, Courbin, F, Davies, A, Decencière, E, Flamary, R, Gavazzi, R, Geiger, M, Hartley, P, Huertas-Company, M, Jackson, N, Jacobs, C, Jullo, E, Kneib, J-P, Koopmans, LVE, Lanusse, F, Li, C-L, Ma, Q, Makler, M, Li, N, Lightman, M, Petrillo, CE, Serjeant, S, Schäfer, C, Sonnenfeld, A, Tagore, A, Tortora, C, Tuccillo, D, Valentín, MB, Velasco-Forero, S, Verdoes Kleijn, GA & Vernardos, G 2019, 'The strong gravitational lens finding challenge', Astronomy and astrophysics, vol. 625, no. May 2019, A119. https://doi.org/10.1051/0004-6361/201832797

Standard

The strong gravitational lens finding challenge. / Metcalf, R. B.; Meneghetti, M.; Avestruz, C.; Bellagamba, F.; Bom, C. R.; Bertin, E.; Cabanac, R.; Courbin, F.; Davies, A.; Decencière, E.; Flamary, R.; Gavazzi, R.; Geiger, M.; Hartley, P.; Huertas-Company, M.; Jackson, N.; Jacobs, C.; Jullo, E.; Kneib, J. -P.; Koopmans, L. V. E.; Lanusse, F.; Li, C. -L.; Ma, Q.; Makler, M.; Li, N.; Lightman, M.; Petrillo, C. E.; Serjeant, S.; Schäfer, C.; Sonnenfeld, A.; Tagore, A.; Tortora, C.; Tuccillo, D.; Valentín, M. B.; Velasco-Forero, S.; Verdoes Kleijn, G. A.; Vernardos, G.

In: Astronomy and astrophysics, Vol. 625, No. May 2019, A119, 01.05.2019.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Metcalf RB, Meneghetti M, Avestruz C, Bellagamba F, Bom CR, Bertin E et al. The strong gravitational lens finding challenge. Astronomy and astrophysics. 2019 May 1;625(May 2019). A119. https://doi.org/10.1051/0004-6361/201832797


BibTeX

@article{6afecfe2487b46ffa484ddb4a5e0f28b,
title = "The strong gravitational lens finding challenge",
abstract = "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 require picking them out of at least tens of millions of images, and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects, reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100 000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. Having multi-band, ground based data is found to be better for this purpose than single-band space based data with lower noise and higher resolution, suggesting that multi-colour data is crucial. Multi-band space based data will be superior to ground based data. The most difficult challenge for a lens finder is differentiating between rare, irregular and ring-like face-on galaxies and true gravitational lenses. The degree to which the efficiency and biases of lens finders can be quantified largely depends on the realism of the simulated data on which the finders are trained.",
keywords = "gravitational lensing: strong, methods: data analysis, Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics",
author = "Metcalf, {R. B.} and M. Meneghetti and C. Avestruz and F. Bellagamba and Bom, {C. R.} and E. Bertin and R. Cabanac and F. Courbin and A. Davies and E. Decenci{\`e}re and R. Flamary and R. Gavazzi and M. Geiger and P. Hartley and M. Huertas-Company and N. Jackson and C. Jacobs and E. Jullo and Kneib, {J. -P.} and Koopmans, {L. V. E.} and F. Lanusse and Li, {C. -L.} and Q. Ma and M. Makler and N. Li and M. Lightman and Petrillo, {C. E.} and S. Serjeant and C. Sch{\"a}fer and A. Sonnenfeld and A. Tagore and C. Tortora and D. Tuccillo and Valent{\'i}n, {M. B.} and S. Velasco-Forero and {Verdoes Kleijn}, {G. A.} and G. Vernardos",
year = "2019",
month = may,
day = "1",
doi = "10.1051/0004-6361/201832797",
language = "English",
volume = "625",
journal = "Astronomy & astrophysics",
issn = "0004-6361",
publisher = " EDP Sciences",
number = "May 2019",

}

RIS

TY - JOUR

T1 - The strong gravitational lens finding challenge

AU - Metcalf, R. B.

AU - Meneghetti, M.

AU - Avestruz, C.

AU - Bellagamba, F.

AU - Bom, C. R.

AU - Bertin, E.

AU - Cabanac, R.

AU - Courbin, F.

AU - Davies, A.

AU - Decencière, E.

AU - Flamary, R.

AU - Gavazzi, R.

AU - Geiger, M.

AU - Hartley, P.

AU - Huertas-Company, M.

AU - Jackson, N.

AU - Jacobs, C.

AU - Jullo, E.

AU - Kneib, J. -P.

AU - Koopmans, L. V. E.

AU - Lanusse, F.

AU - Li, C. -L.

AU - Ma, Q.

AU - Makler, M.

AU - Li, N.

AU - Lightman, M.

AU - Petrillo, C. E.

AU - Serjeant, S.

AU - Schäfer, C.

AU - Sonnenfeld, A.

AU - Tagore, A.

AU - Tortora, C.

AU - Tuccillo, D.

AU - Valentín, M. B.

AU - Velasco-Forero, S.

AU - Verdoes Kleijn, G. A.

AU - Vernardos, G.

PY - 2019/5/1

Y1 - 2019/5/1

N2 - 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 require picking them out of at least tens of millions of images, and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects, reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100 000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. Having multi-band, ground based data is found to be better for this purpose than single-band space based data with lower noise and higher resolution, suggesting that multi-colour data is crucial. Multi-band space based data will be superior to ground based data. The most difficult challenge for a lens finder is differentiating between rare, irregular and ring-like face-on galaxies and true gravitational lenses. The degree to which the efficiency and biases of lens finders can be quantified largely depends on the realism of the simulated data on which the finders are trained.

AB - 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 require picking them out of at least tens of millions of images, and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects, reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100 000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. Having multi-band, ground based data is found to be better for this purpose than single-band space based data with lower noise and higher resolution, suggesting that multi-colour data is crucial. Multi-band space based data will be superior to ground based data. The most difficult challenge for a lens finder is differentiating between rare, irregular and ring-like face-on galaxies and true gravitational lenses. The degree to which the efficiency and biases of lens finders can be quantified largely depends on the realism of the simulated data on which the finders are trained.

KW - gravitational lensing: strong

KW - methods: data analysis

KW - Astrophysics - Astrophysics of Galaxies

KW - Astrophysics - Cosmology and Nongalactic Astrophysics

KW - Astrophysics - Instrumentation and Methods for Astrophysics

U2 - 10.1051/0004-6361/201832797

DO - 10.1051/0004-6361/201832797

M3 - Article

VL - 625

JO - Astronomy & astrophysics

JF - Astronomy & astrophysics

SN - 0004-6361

IS - May 2019

M1 - A119

ER -

ID: 118172856