CAvity DEtection Tool (CADET): pipeline for detection of X-ray
cavities in hot galactic and cluster atmospheres

J 2024

CAvity DEtection Tool (CADET): pipeline for detection of X-ray cavities in hot galactic and cluster atmospheres

PLŠEK, Tomáš, Norbert WERNER, Martin TOPINKA a Aurora SIMIONESCU

Základní údaje

Originální název

CAvity DEtection Tool (CADET): pipeline for detection of X-ray cavities in hot galactic and cluster atmospheres

Autoři

PLŠEK, Tomáš (203 Česká republika, domácí), Norbert WERNER (703 Slovensko, domácí), Martin TOPINKA (203 Česká republika, domácí) a Aurora SIMIONESCU

Vydání

Monthly Notices of the Royal Astronomical Society, Oxford University Press, 2024, 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 arXiv

Impakt faktor

Impact factor: 4.800 v roce 2022

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.1093/mnras/stad3371

UT WoS

001112824700014

Klíčová slova anglicky

methods: data analysis; techniques: image processing; galaxies: active; galaxies: haloes; X-rays: galaxies

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 5. 4. 2024 10:37, Mgr. Marie Šípková, DiS.

Anotace

V originále

The study of jet-inflated X-ray cavities provides a powerful insight into the energetics of hot galactic atmospheres and radio-mechanical AGN feedback. By estimating the volumes of X-ray cavities, the total energy and thus also the corresponding mechanical jet power required for their inflation can be derived. Properly estimating their total extent is, however, non-trivial, prone to biases, nearly impossible for poor-quality data, and so far has been done manually by scientists. We present a novel machine-learning pipeline called Cavity Detection Tool (CADET), developed as an assistive tool that detects and estimates the sizes of X-ray cavities from raw Chandra images. The pipeline consists of a convolutional neural network trained for producing pixel-wise cavity predictions and a DBSCAN clustering algorithm, which decomposes the predictions into individual cavities. The convolutional network was trained using mock observations of early-type galaxies simulated to resemble real noisy Chandra-like images. The network's performance has been tested on simulated data obtaining an average cavity volume error of 14percent at an 89percent true-positive rate. For simulated images without any X-ray cavities inserted, we obtain a 5percent false-positive rate. When applied to real Chandra images, the pipeline recovered 93 out of 97 previously known X-ray cavities in nearby early-type galaxies and all 14 cavities in chosen galaxy clusters. Besides that, the CADET pipeline discovered seven new cavity pairs in atmospheres of early-type galaxies (IC4765, NGC533, NGC2300, NGC3091, NGC4073, NGC4125, and NGC5129) and a number of potential cavity candidates.

Návaznosti

GX21-13491X, projekt VaV

Název: Zkoumání žhavého vesmíru a porozumění kosmické zpětné vazbě (Akronym: EHU)

Investor: Grantová agentura ČR, Exploring the Hot Universe and Understanding Comic Feedback

Citovat

PLŠEK, Tomáš, Norbert WERNER, Martin TOPINKA a Aurora SIMIONESCU. CAvity DEtection Tool (CADET): pipeline for detection of X-ray cavities in hot galactic and cluster atmospheres. Monthly Notices of the Royal Astronomical Society. Oxford University Press, 2024, roč. 527, č. 2, s. 3315-3346. ISSN 0035-8711. Dostupné z: https://dx.doi.org/10.1093/mnras/stad3371.

@article{2373458,
   author = {Plšek, Tomáš and Werner, Norbert and Topinka, Martin and Simionescu, Aurora},
   article_number = {2},
   doi = {http://dx.doi.org/10.1093/mnras/stad3371},
   keywords = {methods: data analysis; techniques: image processing; galaxies: active; galaxies: haloes; X-rays: galaxies},
   language = {eng},
   issn = {0035-8711},
   journal = {Monthly Notices of the Royal Astronomical Society},
   title = {CAvity DEtection Tool (CADET): pipeline for detection of X-ray cavities in hot galactic and cluster atmospheres},
   url = {https://academic.oup.com/mnras/article/527/2/3315/7339785},
   volume = {527},
   year = {2024}
}

TY  - JOUR
ID  - 2373458
AU  - Plšek, Tomáš - Werner, Norbert - Topinka, Martin - Simionescu, Aurora
PY  - 2024
TI  - CAvity DEtection Tool (CADET): pipeline for detection of X-ray cavities in hot galactic and cluster atmospheres
JF  - Monthly Notices of the Royal Astronomical Society
VL  - 527
IS  - 2
SP  - 3315-3346
EP  - 3315-3346
PB  - Oxford University Press
SN  - 00358711
KW  - methods: data analysis
KW  - techniques: image processing
KW  - galaxies: active
KW  - galaxies: haloes
KW  - X-rays: galaxies
UR  - https://academic.oup.com/mnras/article/527/2/3315/7339785
N2  - The study of jet-inflated X-ray cavities provides a powerful insight into the energetics of hot galactic atmospheres and radio-mechanical AGN feedback. By estimating the volumes of X-ray cavities, the total energy and thus also the corresponding mechanical jet power required for their inflation can be derived. Properly estimating their total extent is, however, non-trivial, prone to biases, nearly impossible for poor-quality data, and so far has been done manually by scientists. We present a novel machine-learning pipeline called Cavity Detection Tool (CADET), developed as an assistive tool that detects and estimates the sizes of X-ray cavities from raw Chandra images. The pipeline consists of a convolutional neural network trained for producing pixel-wise cavity predictions and a DBSCAN clustering algorithm, which decomposes the predictions into individual cavities. The convolutional network was trained using mock observations of early-type galaxies simulated to resemble real noisy Chandra-like images. The network's performance has been tested on simulated data obtaining an average cavity volume error of 14percent at an 89percent true-positive rate. For simulated images without any X-ray cavities inserted, we obtain a 5percent false-positive rate. When applied to real Chandra images, the pipeline recovered 93 out of 97 previously known X-ray cavities in nearby early-type galaxies and all 14 cavities in chosen galaxy clusters. Besides that, the CADET pipeline discovered seven new cavity pairs in atmospheres of early-type galaxies (IC4765, NGC533, NGC2300, NGC3091, NGC4073, NGC4125, and NGC5129) and a number of potential cavity candidates.
ER  -

PLŠEK, Tomáš, Norbert WERNER, Martin TOPINKA a Aurora SIMIONESCU. CAvity DEtection Tool (CADET): pipeline for detection of X-ray cavities in hot galactic and cluster atmospheres. \textit{Monthly Notices of the Royal Astronomical Society}. Oxford University Press, 2024, roč.~527, č.~2, s.~3315-3346. ISSN~0035-8711. Dostupné z: https://dx.doi.org/10.1093/mnras/stad3371.

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