11:00 am–12:00 pm ERC 401
Dimitrios Tanoglidis "Shedding light on the Low-Surface-Brightness Universe with Galaxy Surveys and Machine Learning"
Low-surface-brightness galaxies (LSBGs), conventionally defined as galaxies with central surface brightness at least one magnitude less than that of the ambient dark sky, have remained largely elusive in past wide-field surveys. At the same time, observational and theoretical arguments point towards an LSBG-dominated universe. Current and upcoming deep and wide surveys are expected to illuminate the LSB regime. At the same time, the massive amount of data they are going to produce requires the development of novel, automated analysis techniques, with modern machine learning (ML) algorithms presenting a promising solution to this problem. In this work, I will first discuss the discovery and analysis of a large catalog of LSBGs from the Dark Energy Survey data. Afterward, I will describe the development of a number of ML-based tools that can automate the data analysis in future surveys, from using object detection methods in order to remove spurious light reflections in astronomical images, to automatically measuring galaxy profile parameters, with uncertainty quantification using Bayesian neural networks.
