Astroseminar - Patrick Janulewicz - IN PERSON

Patrick is a graduate student at McGill University whose work is primarily focused on brightest cluster galaxies, cosmological simulations, and data-driven approaches to astronomy. He works in Tracy Webb's galaxy evolution group at McGill and Laurence Perreault-Levasseur's group at the University of Montreal, which focuses on applying techniques from statistics and machine learning to solve problems in astrophysics. He is also a member of The Three Hundred collaboration, which studies properties of over 300 simulated galaxy clusters and their environment. His current work involves bridging the gap between simulations and observations, as well as automating the identification of brightest cluster galaxies for upcoming large surveys. 

Title: Accelerating the Identification of Brightest Cluster Galaxies with Neural Networks

Abstract: Brightest cluster galaxies (BCGs) lie deep within the largest gravitationally bound structures in existence. Though some cluster finding techniques identify the position of the BCG and use it as the cluster center, other techniques may not automatically include these coordinates. This can make identifying and studying BCGs in such surveys difficult, forcing researchers to either adopt oversimplified algorithms or to perform cumbersome visual identification. For large surveys, there is a need for a fast and reliable way of obtaining BCG coordinates. We propose machine learning to accomplish this task and train a neural network to identify positions of candidate BCGs given no more information than multiband photometric images. This network can be trained on either real observations or simulated ones. The latter can be generated from The Three Hundred project simulations with our recently released Python package, PyMGal. We find this technique to be a promising method to automate and accelerate the identification of BCGs in large datasets.