Drifting Features: Detection and evaluation in the context of automatic RRLs identification in VVV

TL;DR

This paper introduces a novel method to detect Drifting Features in astronomical data, specifically applied to identifying RR Lyrae stars in VVV, revealing that these features are mostly related to color indices but have limited impact on classification accuracy.

Contribution

The paper presents an innovative ML-based approach to identify Drifting Features in astronomical datasets, enhancing understanding of feature stability over time.

Findings

Drifting Features are mainly related to color indices.

Removing Drifting Features does not significantly improve RR Lyrae classification.

The method effectively identifies features linked to the tile of origin.

Abstract

As most of the modern astronomical sky surveys produce data faster than humans can analyze it, Machine Learning (ML) has become a central tool in Astronomy. Modern ML methods can be characterized as highly resistant to some experimental errors. However, small changes on the data over long distances or long periods of time, which cannot be easily detected by statistical methods, can be harmful to these methods. We develop a new strategy to cope with this problem, also using ML methods in an innovative way, to identify these potentially harmful features. We introduce and discuss the notion of Drifting Features, related with small changes in the properties as measured in the data features. We use the identification of RRLs in VVV based on an earlier work and introduce a method for detecting Drifting Features. Our method forces a classifier to learn the tile of origin of diverse sources (mostly stellar 'point sources'), and select the features more relevant to the task of finding candidates to Drifting Features. We show that this method can efficiently identify a reduced set of features that contains useful information about the tile of origin of the sources. For our particular example of detecting RRLs in VVV, we find that Drifting Features are mostly related to color indices. On the other hand, we show that, even if we have a clear set of Drifting Features in our problem, they are mostly insensitive to the identification of RRLs. Drifting Features can be efficiently identified using ML methods. However, in our example, removing Drifting Features does not improve the identification of RRLs.