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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 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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