Classifying Quasar Types Without a Spectrum
Distinguishing between Type 1 and Type 2 quasars is important because it helps us understand accretion regimes, black hole mass scaling, disk instabilities and feedback processes in active galaxies. Although spectroscopy provides robust classification, it does not scale well with the millions of quasars observed in modern surveys, as it requires substantial time and resources to acquire a good spectrum. On the photometry side, quasar light curves are always irregularly sampled and affected by the specifics of photometric surveys, making them difficult to analyze. In this work, we show that we can use irregularly sampled light curves from the Zwicky Transient Facility to classify quasar types without a spectrum, using Slepian Wavelet Variance. This technique allows us to decompose the variance of light curves into multiple timescales. We use agglomerative hierarchical clustering to classify 516 Type 1 and 238 Type 2 quasars from the MILLIQUAS catalogue, solely based on their wavelet variance curves. We obtain a recovery rate of 99% for Type 1 and 87% for Type 2 quasars, and the few misclassified quasars show the opposite variability behaviour to their spectral type. In contrast to structure functions and the Damped Random Walk model, Slepian Wavelet Variance offers a complementary, model-independent view of variability across short and long timescales.