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تسجيل مستخدم جديدSystematic search for tidal features around nearby galaxies: I. Enhanced SDSS imaging of the Local Volume
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Gustavo Morales
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In hierarchical models of galaxy formation, stellar tidal streams are expected around most, if not all, galaxies. Although these features may provide useful diagnostics of the $Lambda$CDM model, their observational properties remain poorly constrained because they are challenging to detect and interpret and have been studied in detail for only a sparse sampling of galaxy population. More quantitative, systematic approaches are required. We advocate statistical analysis of the counts and properties of such features in archival wide-field imaging surveys for a direct comparison against results from numerical simulations. Thus, in this paper we aim to study systematically the frequency of occurrence and other observational properties of tidal features around nearby galaxies. The sample we construct will act as a foundational dataset for statistical comparison with cosmological models of galaxy formation. Our approach is based on a visual classification of diffuse features around a volume-limited sample of nearby galaxies, using a post-processing of Sloan Digital Sky Survey imaging optimized for the detection of stellar structure with low surface brightness. At a limiting surface brightness of $28 mathrm{mag~arcsec^{-2}}$, 14% of the galaxies in our sample exhibit evidence of diffuse features likely to have arisen from minor merging events. Our technique recovers all previously known streams in our sample and yields a number of new candidates. Consistent with previous studies, coherent arc-like features and shells are the most common type of tidal structures found in this study. We conclude that although some detections are ambiguous and could be corroborated or refuted with deeper imaging, our technique provides a reliable foundation for the statistical analysis of diffuse circumgalactic features in wide-area imaging surveys, and for the identification of targets for follow-up studies.
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