Description:

The era of data-driven discovery is producing a wave of new science in high energy astrophysics, and the Chandra Source Catalog (CSC) is an effective tool that enables it. The treasure trove found in CSC datasets has propelled population studies, the search for high energy transients, and the characterization of accretion in luminous X-ray systems. But the CSC has also become a valuable tool in machine learning studies, as it provides an exquisite training set that relates the basic units of X-ray data -single photon detections from a source- to astrophysically relevant measurables such as the spectral parameters of acreeting binaries or the timescales of exotic cosmic explosions. In this talk, I will present an overview of how the X-ray community is using machine learning in combination with the CSC to produce new representations of X-ray datasets that improve of fundamental tasks of X-ray astronomy, such as the classification of sources, the inference of physical parameters, and the discovery of anomalies of astrophysical relevance. I will also provide a perspective of how the CSC -and Chandra data in general- represent a legacy dataset as we enter the era of foundation AI.

Rafael Martinez Galarza, Staff Astrophysicist, Harvard-Smithsonian Center for Astrophysics

Host: Tim Kovachy