Alexander Magnus
Alexander Magnus

Wednesday 22nd June, 2022

Talks & Workshops

Investigating Periodic Variable Star Classification in Large Data Sets


Recent studies indicate that current algorithmic approaches to large-scale stellar classification are prone to widespread systematic error. We investigate techniques used by the WISE Catalog of Periodic Variable Stars to classify periodic variables. Using our own classifications of the 485 brightest stars categorized as “RR Lyrae” by the WISE Catalog, we compare the speed and accuracy of a variety of period searching algorithms to determine which are most applicable for handling large datasets. Further, to explore the physical characteristics of periodic variables, and potentially produce another classification method, we introduce a newly developed Python program capable of searching for long-period modulation such as the Blazhko effect in RR Lyraes. We find long-period modulation to be a very common feature of periodic variable stars, and conclude that current automated techniques are sufficient for identifying periodic variability, but are unreliable when classifying variable stars.

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