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Simulating high-realistic galaxy scale strong lensing in galaxy clusters to train deep learning methods

Published online by Cambridge University Press:  04 March 2024

G. Angora Open the ORCID record for G. Angora [Opens in a new window] ,
P. Rosati ,
M. Meneghetti ,
M. Brescia ,
A. Mercurio ,
C. Grillo ,
P. Bergamini ,
A. Acebron ,
G. Caminha  and
L. Tortorelli
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G. Angora*
Affiliation:
Dipartimento di Fisica e Scienze della Terra, Università di Ferrara, Via Saragat 1, I-44122 Ferrara, Italy. INAF – Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, I-80131 Napoli, Italy
P. Rosati
Affiliation:
Dipartimento di Fisica e Scienze della Terra, Università di Ferrara, Via Saragat 1, I-44122 Ferrara, Italy. INAF – OAS, Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, via Gobetti 93/3, I-40129 Bologna, Italy INFN, Sezione di Ferrara, Via Saragat 1, I-44122 Ferrara, Italy
M. Meneghetti
Affiliation:
INAF – OAS, Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, via Gobetti 93/3, I-40129 Bologna, Italy
M. Brescia
Affiliation:
INAF – Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, I-80131 Napoli, Italy Dipartimento di Fisica “E. Pancini”, Università di Napoli “Federico II”, Via Cinthia 21, I-80126 Napoli, Italy INFN, Sezione di Napoli, Via Cinthia 21, I-80126 Napoli, Italy
A. Mercurio
Affiliation:
INAF – Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, I-80131 Napoli, Italy Dipartimento di Fisica, Università di Salerno, Via Giovanni Paolo II, 132, I-84084, Fisciano (SA), Italy
C. Grillo
Affiliation:
Dipartimento di Fisica, Università di Milano, via Celoria 16, I-20133 Milano, Italy INAF – IASF Milano, via A. Corti 12, I-20133 Milano, Italy
P. Bergamini
Affiliation:
INAF – OAS, Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, via Gobetti 93/3, I-40129 Bologna, Italy Dipartimento di Fisica, Università di Milano, via Celoria 16, I-20133 Milano, Italy
A. Acebron
Affiliation:
Dipartimento di Fisica, Università di Milano, via Celoria 16, I-20133 Milano, Italy INAF – IASF Milano, via A. Corti 12, I-20133 Milano, Italy
G. Caminha
Affiliation:
Technische Universität München, Physik-Department, James-Franck Str. 1, D-85741 Garching, Germany Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str. 1, D-85748 Garching, Germany
L. Tortorelli
Affiliation:
University Observatory, Faculty of Physics, Ludwig-Maximilians-Universität München, Scheinerstr. 1, D-81679 Munich, Germany
L. Bazzanini
Affiliation:
Dipartimento di Fisica e Scienze della Terra, Università di Ferrara, Via Saragat 1, I-44122 Ferrara, Italy. INAF – OAS, Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, via Gobetti 93/3, I-40129 Bologna, Italy
E. Vanzella
Affiliation:
INAF – OAS, Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, via Gobetti 93/3, I-40129 Bologna, Italy
*
email: [email protected]
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Abstract

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Galaxy-galaxy strong lensing in galaxy clusters is a unique tool for studying the subhalo mass distribution, as well as for testing predictions from cosmological simulations. We describe a novel method that simulates realistic lensed features embedded inside the complexity of observed data by exploiting high-precision cluster lens models. Such methodology is used to build a large dataset with which Convolutional Neural Networks have been trained to identify strong lensing events in galaxy clusters. In particular, we inject lensed sources around cluster members using the images acquired by the Hubble Space Telescope. The resulting simulated mock data preserve the complexity of observation by taking into account all the physical components that could affect the morphology and the luminosity of the lensing events. The trained networks achieve a purity-completeness level of ∼ 91% in detecting such events. The methodology presented can be extended to other data-intensive surveys carried out with the next-generation facilities.

Keywords

Gravitational lensinggalaxies: clustersimage processingdeep learning
Type
Contributed Paper
Information
Proceedings of the International Astronomical Union , Volume 18 , Symposium S381: Strong Gravitational Lensing in the Era of Big Data , December 2022 , pp. 85 - 93
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of International Astronomical Union

References

Acebron, A., Cibirka, N., Zitrin, A., et al. 2018, Apj, 858, 42.CrossRefGoogle Scholar
Akhazhanov, A., More, A., Amini, A., et al. 2022, MNRAS, 513, 2407.CrossRefGoogle Scholar
Angora, G., Rosati, P., Brescia, M., et al. 2020, A&A, 643, A177.Google Scholar
Angora, G., Rosati, P., Meneghetti, M., et al. 2023, A&A, 676, A40.Google Scholar
Auger, M.W., Treu, T., Gavazzi, R., et al. 2010, Apj, 721, L163.CrossRefGoogle Scholar
Bacon, R., Accardo, M., Adjali, L., et al. 2012, The Messenger, 147, 4.Google Scholar
Bacon, R., Vernet, J., Borisova, E., et al. 2014, The Messenger, 157, 13.Google Scholar
Bacon, R., Brinchmann, J., Richard, J., et al. 2015, A&A, 575, A75.Google Scholar
Bergamini, P., Rosati, P., Mercurio, A., et al. 2019, A&A, 631, A130.Google Scholar
Bergamini, P., Rosati, P., Vanzella, E., et al. 2021, A&A, 645, A140.Google Scholar
Bergamini, P., Agnello, A., and Caminha, G.B. 2021, A&A, 648, A123.Google Scholar
Bergamini, P., Acebron, A., Grillo, C., et al. 2023, Apj, 952, 84.CrossRefGoogle Scholar
Bonamigo, M., Grillo, C., Ettori, S., et al. 2017, Apj, 842, 132.CrossRefGoogle Scholar
Bonamigo, M., Grillo, C., Ettori, S., et al. 2018, Apj, 864, 98.CrossRefGoogle Scholar
Caminha, G.B., Grillo, C., Rosati, P., et al. 2016, A&A, 587, A80.Google Scholar
Caminha, G.B., Grillo, C., Rosati, P., et al. 2017, A&A, 607, A93.Google Scholar
Caminha, G.B., Rosati, P., Grillo, C., et al. 2019, A&A, 632, A36.Google Scholar
Caminha, G.B., Suyu, S.H., Grillo et al. 2022, A&A, 657, A83.Google Scholar
Cañameras, R., Schuldt, S., Shu, Y., et al. 2021, A&A, 653, L6.Google Scholar
Cao, S., Covone, G., Zhu, Z.H. 2012, ApJ, 755, 31.CrossRefGoogle Scholar
Collett, T.E. and Auger, M.W. 2014, MNRAS, 443, 969.CrossRefGoogle Scholar
Desprez, G., Richard, J., Jauzac, M., et al. 2018, MNRAS, 479, 2630.CrossRefGoogle Scholar
Diego, J.M., Broadhurst, T., Benitez, N., et al. 2015, MNRAS, 449, 588.CrossRefGoogle Scholar
Elasdóttir, Á., Limousin, M., Richard, J., et al. 2007, arXiv e-prints, arXiv:0710.5636.Google Scholar
Gentile, F., Tortora, C., Covone, G., et al. 2022, MNRAS, 510, 500.CrossRefGoogle Scholar
Grillo, C. 2010, Apj, 722, 779.CrossRefGoogle Scholar
Grillo, C., Rosati, P., Suyu, S.H., et al. 2018, ApJ, 860, 94.CrossRefGoogle Scholar
Ivezić, Ž., Kahn, S.M., Tyson, J.A., at al. 2019, Apj, 873, 111.CrossRefGoogle Scholar
Jackson, N. 2008, MNRAS, 389, 1311.CrossRefGoogle Scholar
Jullo, E., Natarajan, P., Kneib, J.-P., et al. 2010, Science, 329, 924.CrossRefGoogle Scholar
Keeton, C.R. 2001, arXiv e-prints, astro-ph/0102340.Google Scholar
Kinney, A.L., Calzetti, D., Bohlin, R.C. et al. 1996, Apj, 467, 38.CrossRefGoogle Scholar
Lagattuta, D.J., Richard, J., Bauer, F.E., et al. 2019, MNRAS, 485, 3738.Google Scholar
Lagattuta, D.J., Richard, J., Bauer, F.E., et al. 2022, MNRAS, 514, 497.CrossRefGoogle Scholar
Laigle, C., McCracken, H.J., Ilbert, O., et al. 2016, Apjs, 224, 24.CrossRefGoogle Scholar
Laureijs, R., Hoar, J., Buenadicha, G., et al. 2014, Astronomical Data Analysis Software and Systems XXIII, 485, 495.Google Scholar
LeCun, Y., Boser, B., Denker, J. S., et al. 1989, Neural Comput, 1 (4), 541–551.CrossRefGoogle Scholar
Lecun, Y., Bottou, L., Bengio, Y., et al. 1998, Proceedings of the IEEE 86, 11, 2278–2324.Google Scholar
Le Fèvre, O. and Hammer, F.: 1988, Apj, 333, L37.CrossRefGoogle Scholar
Leuzzi, L., Meneghetti, M., Angora, G., and Collaboration, Euclid 2023, arXiv e-prints, arXiv:2307.08736.Google Scholar
Li, R., Napolitano, N.R., Tortora, C., Spiniello, C., et al. 2020, Apj, 899, 30.CrossRefGoogle Scholar
Li, R., Napolitano, N.R., Spiniello, C., et al. 2021, Apj, 923, 16.CrossRefGoogle Scholar
Limousin, M., Kneib, J.-P., and Natarajan, P. 2005, MNRAS, 356, 309.CrossRefGoogle Scholar
Lombardi, M. and Bertin, G. 1999, A&A, 342, 337.Google Scholar
Lombardi, M., Rosati, P., Blakeslee, J.P., et al. 2005, Apj, 623, 42.CrossRefGoogle Scholar
Lotz, J.M., Koekemoer, A., Coe, D., et al. 2017, Apj, 837, 97.CrossRefGoogle Scholar
Meneghetti, M., Melchior, P., Grazian, A., et al. 2008, A&A, 482, 403.Google Scholar
Meneghetti, M., Rasia, E., Merten, J., et al. 2010, A&A, 514, A93.Google Scholar
Meneghetti, M., Davoli, G., Bergamini, P., et al. 2020, Science, 369, 1347.CrossRefGoogle Scholar
Meneghetti, M. 2022, Springer, ISBN: 3-030-73582-6.Google Scholar
Meneghetti, M., Ragagnin, A., Borgani, S., et al. 2022, A&A, 668, A188.Google Scholar
Metcalfe, N., Shanks, T., Campos, A., et al. 2001, MNRAS, 323, 795.CrossRefGoogle Scholar
Metcalf, R.B. and Petkova, M. 2014, MNRAS, 445, 1942.CrossRefGoogle Scholar
Metcalf, R.B., Meneghetti, M., Avestruz, C., et al. 2019, A&A, 625, A119.Google Scholar
Millon, M., Galan, A., Courbin, F., et al. 2020, A&A, 639, A101.Google Scholar
Moresco, M., Amati, L., Amendola, L., et al. 2022, Living Reviews in Relativity, 25, 6.CrossRefGoogle Scholar
Pawase, R.S., Courbin, F., Faure, C., et al. 2014, MNRAS, 439, 3392.CrossRefGoogle Scholar
Petkova, M., Metcalf, R.B., and Giocoli, C. 2014, MNRAS, 445, 1954.CrossRefGoogle Scholar
Petrillo, C.E., Tortora, C., Chatterjee, S., et al. 2017, MNRAS, 472, 1129.CrossRefGoogle Scholar
Petrillo, C.E., Tortora, C., Vernardos, G., et al. 2019, MNRAS, 484, 3879.CrossRefGoogle Scholar
Postman, M., Coe, D., Bentez, N., et al. 2012, Apjs, 199, 25.CrossRefGoogle Scholar
Richard, J., Kneib, J.-P., Ebeling, H., et al. 2011, MNRAS, 414, L31.CrossRefGoogle Scholar
Rosati, P., Balestra, I., Grillo, C., et al. 2014, The Messenger, 158, 48.Google Scholar
Sérsic, J.L. 1963, Boletin de la Asociacion Argentina de Astronomia La Plata Argentina, 6, 41.Google Scholar
Sérsic, J.L. 1968, Cordoba, Argentina: Observatorio Astronomico, 1968.Google Scholar
Simonyan, K. and Zisserman, A. 2014, arXiv e-prints, arXiv:1409.1556.Google Scholar
Smith, G.P., Kneib, J.-P., Smail, I., et al. 2005, MNRAS, 359, 417.CrossRefGoogle Scholar
Sonnenfeld, A., Treu, T., Gavazzi, R., et al. 2013, Apj, 777, 98.CrossRefGoogle Scholar
Sonnenfeld, A., Treu, T., Marshall, P.J., et al. 2015, Apj, 800, 94.CrossRefGoogle Scholar
Suyu, S.H., Bonvin, V., Courbin, F., et al. 2017, MNRAS, 468, 2590.CrossRefGoogle Scholar
Suyu, S.H., Huber, S., Cañameras, R., et al. 2020, A&A, 644, A162.Google Scholar
Swinbank, A.M., Webb, T.M., Richard, J., et al. 2009, MNRAS, 400, 1121.CrossRefGoogle Scholar
Sygnet, J.F., Tu, H., Fort, B., et al. 2010, A&A, 517, A25.Google Scholar
Tortora, C., Napolitano, N.R., Romanowsky, A.J., and Jetzer, P. 2010, Apj, 721, L1.CrossRefGoogle Scholar
Tortorelli, L. and Mercurio, A. 2023, Frontiers in Astronomy and Space Sciences, 10, 51.CrossRefGoogle Scholar
Treu, T. and Koopmans, L.V.E. 2002, Apj, 575, 87.CrossRefGoogle Scholar
Vanzella, E., Meneghetti, M., Caminha, G.B., et al. 2020, MNRAS, 494, L81.CrossRefGoogle Scholar
Vanzella, E., Caminha, G.B., Rosati, et al. 2021, A&A, 646, A57.Google Scholar
Williams, R.E., Blacker, B., Dickinson, M., et al. 1996, Apj, 112, 1335.CrossRefGoogle Scholar