Multiwavelength classification of X-ray selected galaxy cluster
candidates using convolutional neural networks

J 2020

Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks

KOSIBA, Matej; Maggie LIEU; Bruno ALTIERI; Nicolas CLERC; Lorenzo FACCIOLI et. al.

Základní údaje

Originální název

Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks

Autoři

Vydání

Monthly Notices of the Royal Astronomical Society, MALDEN, WILEY, 2020, 0035-8711

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10308 Astronomy

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL, URL

Impakt faktor

Impact factor: 5.287

Kód RIV

RIV/00216224:14310/20:00116962

Organizační jednotka

Přírodovědecká fakulta

DOI

https://doi.org/10.1093/mnras/staa1723

UT WoS

000574923200007

EID Scopus

2-s2.0-85095411533

Klíčová slova anglicky

galaxies: clusters: general; methods: data analysis; techniques: image processing

Štítky

14312040, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 10. 11. 2022 11:48, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

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.

Návaznosti

MUNI/I/0003/2020, interní kód MU

Název: MUNI Award in Science and Humanities 3 (Akronym: Space-Based High-Energy Astrophysics)

Investor: Masarykova univerzita, MUNI Award in Science and Humanities 3, MASH - MUNI Award in Science and Humanities

Citovat

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 a Alina KHALIKOVA. Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks. Monthly Notices of the Royal Astronomical Society. MALDEN: WILEY, 2020, roč. 496, č. 4, s. 4141-4153. ISSN 0035-8711. Dostupné z: https://doi.org/10.1093/mnras/staa1723.

@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 = {https://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 a 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, roč.~496, č.~4, s.~4141-4153. ISSN~0035-8711. Dostupné z: https://doi.org/10.1093/mnras/staa1723.

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