Seminar by Rafael Martinez-Galarza, Harvard
The era of data-driven discovery is producing a wave of new science in high energy astrophysics, and the Chandra Source Catalog (CSC) as well as other high energy datasets are effective tools that enable it. The treasure trove found in these datasets has propelled population studies, the search for high energy transients, and the characterization of accretion in luminous X-ray systems. But high energy catalogs have also become a valuable tool in machine learning studies, as they provide 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 X-ray datasets 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.
Host: Shifra Mandel