We would like to know whether you are planning to attend this session during the AAS HEAD 19 meeting. It will take place on Monday, March 14, at 6pm. Please let us know if you are potentially interested in attending this session, so that we can better plan the details. Expressing your interest does not bind you to attend. If you wish, you can leave us your email, so that we can provide updates and resources for the session.
A Python-friendly introduction to doing science with the Chandra Source Catalog
The Chandra Source Catalog (CSC) provides photometric, spectral, and variability properties for over 317,000 X-ray sources that have been processed uniformly, profiting from Chandra's superb sensitivity and resolution. Perhaps unknown to many CSC users, the catalog also provides millions of science-ready data products that can be accessed from a Jupyter notebook session (python) using CIAO and Virtual Observatory tools. These data products include spectra, light curves, spectral fits, sensitivity maps, among many others. The combination of tabulated properties and data products for hundreds of thousands of serendipitous X-ray sources makes the CSC an excellent tool for discoveries and population studies. In this workshop/tutorial, we will demonstrate how to perform several basic science tasks with the CSC in a python-friendly environment, including access to the property tables, downloads of the data products, and specific science workflows such as classifying X-ray sources (AGNs, stars, etcs) by cross-matching CSC with optical and infrared catalogs, determining cumulative coverage and sensitivity in a region of the sky, and using the CSC data as input for machine learning algorithms of classification and regression. We will also present a demonstration of CSC source classification using supervised machine learning.
Links:
https://cxc.cfa.harvard.edu/csc/threads/pyvoaccess/notebook.htmlhttps://cxc.cfa.harvard.edu/csc/threads/cumulative_sky_coverage/notebook.html