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@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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