Active Galactic Nuclei (AGN) feedback is widely recognized as a key process shaping galaxy evolution, yet its observational signatures remain challenging to isolate across large and diverse samples. Seyfert galaxies occupy a unique regime in this context: they host active nuclei while preserving measurable star formation in their host galaxies, making them natural laboratories for studying AGN feedback in action. In this work, we investigate statistical and photometric signatures of AGN feedback in a large sample of Seyfert galaxies selected from the Sloan Digital Sky Survey (SDSS). Spectroscopic classifications based on standard emission line diagnostic diagrams are used to establish reliable Seyfert identifications, while the core analysis focuses on multi-wavelength photometric properties. By combining optical photometry with ultraviolet and mid-infrared data, we explore how star formation indicators, dust-obscured emission, and AGN related colors behave within the Seyfert population compared to star-forming and starburst galaxies. Our approach emphasizes a statistical perspective rather than individual case studies, allowing us to probe feedback-related trends across a broad parameter space. We examine deviations from canonical star-forming sequences, changes in color distributions, and correlations between photometric features and AGN classifications. Machine learning and multivariate statistical techniques are employed to identify the photometric dimensions most sensitive to AGN driven processes, providing an efficient framework for isolating feedback-related patterns without relying directly on spectroscopy. These results demonstrate that photometric and statistical analyses can capture meaningful imprints of AGN feedback in Seyfert galaxies, offering a scalable pathway for feedback studies in upcoming wide area surveys. This work highlights the potential of photometry driven approaches to extend AGN feedback investigations across all skies and cosmic times.
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Uzay Aydın (2026) studied this question.
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