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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

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