Other formats:
BibTeX
LaTeX
RIS
@article{1694737,
author = {Kosiba, Matej and Lieu, Maggie and Altieri, Bruno and Clerc, Nicolas and Faccioli, Lorenzo and Kendrew, Sarah and Valtchanov, Ivan and Sadibekova, Tatyana and Pierre, Marguerite and Hroch, Filip and Werner, Norbert and Burget, Lukas and Garrel, Christian and Koulouridis, Elias and Gaynullina, Evelina and Molham, Mona and RamosandCeja, Miriam E. and Khalikova, Alina},
article_location = {MALDEN},
article_number = {4},
doi = {http://dx.doi.org/10.1093/mnras/staa1723},
keywords = {galaxies: clusters: general; methods: data analysis; techniques: image processing},
language = {eng},
issn = {0035-8711},
journal = {Monthly Notices of the Royal Astronomical Society},
title = {Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks},
url = {https://doi.org/10.1093/mnras/staa1723},
volume = {496},
year = {2020}
}
TY - JOUR
ID - 1694737
AU - Kosiba, Matej - Lieu, Maggie - Altieri, Bruno - Clerc, Nicolas - Faccioli, Lorenzo - Kendrew, Sarah - Valtchanov, Ivan - Sadibekova, Tatyana - Pierre, Marguerite - Hroch, Filip - Werner, Norbert - Burget, Lukas - Garrel, Christian - Koulouridis, Elias - Gaynullina, Evelina - Molham, Mona - Ramos-Ceja, Miriam E. - Khalikova, Alina
PY - 2020
TI - Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks
JF - Monthly Notices of the Royal Astronomical Society
VL - 496
IS - 4
SP - 4141-4153
EP - 4141-4153
PB - WILEY
SN - 00358711
KW - galaxies: clusters: general
KW - methods: data analysis
KW - techniques: image processing
UR - https://doi.org/10.1093/mnras/staa1723
N2 - Galaxy clusters appear as extended sources in XMM-Newton images, but not all extended sources are clusters. So, their proper classification requires visual inspection with optical images, which is a slow process with biases that are almost impossible to model. We tackle this problem with a novel approach, using convolutional neural networks (CNNs), a state-of-the-art image classification tool, for automatic classification of galaxy cluster candidates. We train the networks on combined XMM-Newton X-ray observations with their optical counterparts from the all-sky Digitized Sky Survey. Our data set originates from the XMM CLuster Archive Super Survey (X-CLASS) survey sample of galaxy cluster candidates, selected by a specially developed pipeline, the XAmin, tailored for extended source detection and characterization. Our data set contains 1707 galaxy cluster candidates classified by experts. Additionally, we create an official Zooniverse citizen science project, The Hunt for Galaxy Clusters, to probe whether citizen volunteers could help in a challenging task of galaxy cluster visual confirmation. The project contained 1600 galaxy cluster candidates in total of which 404 overlap with the expert's sample. The networks were trained on expert and Zooniverse data separately. The CNN test sample contains 85 spectroscopically confirmed clusters and 85 non-clusters that appear in both data sets. Our custom network achieved the best performance in the binary classification of clusters and non-clusters, acquiring accuracy of 90 per cent, averaged after 10 runs. The results of using CNNs on combined X-ray and optical data for galaxy cluster candidate classification are encouraging, and there is a lot of potential for future usage and improvements.
ER -
KOSIBA, Matej, Maggie LIEU, Bruno ALTIERI, Nicolas CLERC, Lorenzo FACCIOLI, Sarah KENDREW, Ivan VALTCHANOV, Tatyana SADIBEKOVA, Marguerite PIERRE, Filip HROCH, Norbert WERNER, Lukas BURGET, Christian GARREL, Elias KOULOURIDIS, Evelina GAYNULLINA, Mona MOLHAM, Miriam E. RAMOS-CEJA and Alina KHALIKOVA. Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks. \textit{Monthly Notices of the Royal Astronomical Society}. MALDEN: WILEY, 2020, vol.~496, No~4, p.~4141-4153. ISSN~0035-8711. Available from: https://dx.doi.org/10.1093/mnras/staa1723.
|
