Challenges and Pitfalls in the Classification of Variable Stars

Classifying stellar variability may seem straightforward, but it has become increasingly challenging with the advent of space missions and ultra-precise photometric data. A variety of physical phenomena, such as stellar activity, rotation, pulsations, and binarity, can produce brightness variations of similar amplitude and morphology. As a result, distinguishing between these effects is often difficult, and in some cases, multiple mechanisms may be present simultaneously. Beyond intrinsic variability, observational data can also be compromised by contamination or artifacts introduced during data processing, further complicating classification. In this talk, I will highlight the key issues that hinder accurate classification of variable stars and present examples demonstrating how misleading signals can lead to incorrect interpretations.