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 and Aurora SIMIONESCU

Basic information

Original name

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

Authors

PLŠEK, Tomáš (203 Czech Republic, belonging to the institution), Norbert WERNER (703 Slovakia, belonging to the institution), Martin TOPINKA (203 Czech Republic, belonging to the institution) and Aurora SIMIONESCU

Edition

Monthly Notices of the Royal Astronomical Society, Oxford University Press, 2024, 0035-8711

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10308 Astronomy

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL arXiv

Impact factor

Impact factor: 4.800 in 2022

Organization unit

Faculty of Science

DOI

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

UT WoS

001112824700014

Keywords in English

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

Abstract

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.

Links

GX21-13491X, research and development project

Name: Zkoumání žhavého vesmíru a porozumění kosmické zpětné vazbě (Acronym: EHU)

Investor: Czech Science Foundation

Citovat

PLŠEK, Tomáš, Norbert WERNER, Martin TOPINKA and 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, vol. 527, No 2, p. 3315-3346. ISSN 0035-8711. Available from: 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 and 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, vol.~527, No~2, p.~3315-3346. ISSN~0035-8711. Available from: https://dx.doi.org/10.1093/mnras/stad3371.

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