Outliers within an astronomical catalog hold the potential to offer new insights into astrophysical phenomena.  Machine learning is used to identify outliers in the Chandra Source Catalog v2.0. 

Samantha Watkins used a Van Allen Summer Research Grant to show how machine learning can be to identify astronmical outliers in the Chandra Source Catalog v2.0. She developed a script in Python to query the SIMBAD astronomical database for objects near our outlier coordinates. She also considered the implications of missing data in the dataset. A follow-up study of the bias introduced by means of handling missing data has implications for future astronomical machine learning surveys.