Understanding AGN physics through variability



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Universidad de Valparaíso





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Instituto de Fisica y Astronomia




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In recent years, the discovery of the so-called changing-look (CL) active galactic nu- clei (AGNs) has evidenced that several scenarios can lead to variations in the broad emission lines (BELs, with widths >1,000 km s−1) and so to the AGN optical classifications, which has challenged the classical view of AGNs defined by the simplest unified models (UM). This PhD thesis presents a comprehensive study of optical variability in AGNs with the aim of shedding light on the Type 1/Type 2 dichotomy and improving the search for the intriguing and rare CL AGNs. Our approach is based on the exploration of optical fluctuations of spectrally-classified Type 2 sources, that is, AGNs whose ac- cretion disc and broad line region (BLR) should be obscured, and so their BELs and optical continuum variability. For that purpose, we make use of the Zwicky Tran- sient Facility (ZTF), a state-of-the-art optical time-domain survey, and the Automatic Learning for the Rapid Classification of Events (ALeRCE), a Chilean-led broker using machine learning models, in preparation for the big data era with the arrival of the Legacy Survey of Space and Time (LSST). By analysing systematically the ZTF light curves of a large (>15,000) Type 2 sam- ple, we find that ∼ 11 per cent of sources show evidence for optical variations, which leads to the discovery of misclassified Type 1s with weak BELs and CL candidates. We then apply the same strategy (i.e., searching for optical variations in Type 2 sources) using the current light curve classifications given by ALeRCE, and find ∼ 60 new CL candidates. We took second epoch spectra of 36 candidates and confirmed 50 per cent of sources as turning-on CLs, resulting in one of the selection techniques with the highest success rate of CL confirmations up to date. Overall, this thesis strengthens the importance of variability studies in under- standing the physics of AGNs, and contributes to the use of machine learning algo- rithms together with all-sky variability surveys to search for intrinsically rare objects. The findings of this research will ultimately contribute to our understanding of the fundamental processes that drive the evolution of AGNs.


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