Details & Abstract
The speaker discussed the machine learning approaches to identifying Active Galactic Nuclei (AGN) in DESI survey galaxies using multi-wavelength data. The session explored how autoencoders and spectral reconstruction techniques can improve AGN detection and support future astrophysical and cosmological studies.
What Happened
In this talk, the speaker described a machine learning approach to multi-wavelength Active galactic nuclei (AGN) identification for host galaxies within the DESI survey. AGNs emit light in all wavelengths in the electromagnetic spectrum, it is difficult to create an AGN selection that is fully complete. The identification of AGNs is key to understanding not only their astrophysics, being an important driver of galaxy evolution, and affecting the galaxy-halo connection, but also the biases and systematic uncertainties for further cosmological analysis. With the abundance of large multi-wavelength surveys, the application of machine learning techniques (reconstruction of galaxy spectra with unsupervised learning, specifically autoencoders) could provide the solution to a more complete AGN identification technique. This provides a new method to produce accurate and more complete AGN selections in wide-field surveys.
Afterwards there was a short informal discussion on applications, techniques and related research directions.
Speaker / Author
Dhavala Sai Srinivasa is a PhD student at the Institute of Cosmology and Gravitation. He is actively working on the analysis of galaxy spectra and other morphological properties using Machine Learning algorithms. He is interested in simulations, multi-wavelength observations, and coding algorithms.