Speaker
Description
Active Galactic Nuclei (AGN) play a key role in galaxy evolution, but identifying a complete, unbiased sample is complicated because it requires extensive, multi-wavelength detections. To address this, we apply a machine learning framework to the Dark Energy Spectroscopic Instrument (DESI) survey, utilizing the available rest-frame UV and optical range (3600–9800 Å) to leverage established, precise AGN classification methods.
Our methodology employs SPENDER, an unsupervised machine learning framework, to compress galaxy spectra into a multi-dimensional latent space. Using a classifier algorithm on a curated, balanced and diverse sample of galaxy spectra—spanning $0 < z < 3.5$, our framework achieves an AGN detection rate exceeding 75%. By analyzing the topology of the latent space, we isolate the specific spectral features driving classification and examine AGN co-evolutionary tracks that are typically obscured in standard projections. Furthermore, we perform traversals of the latent space, stacking spectra within local neighborhoods to study the transition of galaxy properties across different types of galaxies in our dataset. This process helps us understand the regions of confusion/misclassification with the classification algorithms, as the latent representation captures complex, non-linear behaviour of galaxy spectra.
Finally, we employ a Bayesian inference pipeline that maps the purely mathematical latent space to fundamental physical spectral properties, such as stellar mass, metallicity, age, and other parameters, derived from DESI survey. This approach serves as a high-speed, scalable infrastructure alternative to the traditional Spectral Energy Distribution (SED) fitting. Such an efficient methodology makes it suitable for the massive datasets anticipated in future DESI, and other wide-area spectroscopic surveys.