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The SRG/eROSITA all-sky survey: The morphologies of clusters of galaxies I: A catalogue of morphological parameters

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arxiv 2502.02239 v1 pith:3EVGGUFT submitted 2025-02-04 astro-ph.CO

classification astro-ph.CO
keywords clustersconcentrationparameterserositaobjectssurveyall-skyaround
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The first SRG/eROSITA all-sky X-ray survey, eRASS1, resulted in a catalogue of over twelve thousand optically-confirmed galaxy groups and clusters in the western Galactic hemisphere. Using the eROSITA images of these objects, we measure and study their morphological properties, including their concentration, central density and slope, ellipticity, power ratios, photon asymmetry, centroid shift and Gini coefficient. We also introduce new forward-modelled parameters which take account of the instrument point spread function (PSF), which are slosh, which measures how asymmetric the surface brightness distribution is, and multipole magnitudes, which are analogues to power ratios. Using simulations, we find some non forward-modelled parameters are strongly biased due to PSF and data quality. For the same clusters, we find similar values of concentration and central density compared to results by ourselves using Chandra and previous results from XMM-Newton. The population as a whole has log concentrations which are typically around 0.3 dex larger than South Pole Telescope or Planck-selected samples and the deeper eFEDS sample. The exposure time, detection likelihood threshold, extension likelihood threshold and number of counts affect the concentration distribution, but generally not enough to reduce the concentration to match the other samples. The concentration of clusters in the survey strongly affects whether they are detected as a function of redshift and luminosity. We introduce a combined disturbance score based on a Gaussian mixture model fit to several of the parameters. For brighter clusters, around 1/4 of objects are classified as disturbed using this score, which may be due to our sensitivity to concentrated objects.

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  1. Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising

    astro-ph.IM 2025-06 reject novelty 5.0 of 10

    Applying a U-Net VAE denoising step to galaxy images before classification is reported to improve accuracy, reaching 97.45% with a GCNN on Galaxy10 DECaLS, although no direct noisy baseline is presented.

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